Republic of Iraq Ministry of Higher Education and Scientific Research University of Technology Al-Rasheed College of Engineering and Science Electrical and Electronic Engineering Department

Design and Analysis of Virtual Human Arm

Driven by EMG Signal

A thesis submitted to the AL-Rasheed College of

Engineering and Science in partial fulfillment of the requirements for the Degree of Doctor of Philosophy in

Electrical and Electronic Engineering ( Control Engineering )

By Yousif Ismail Mohammed AL Mashhadany

(M.Sc. 1999)

Supervised by Asst. Prof. Dr. Mohammed Zeki AL Faiz

Ramadan 1430 September 2009

أعوذ باهلل السميع العليم من الشيطان الرجيم

بسم اهلل الرحمن الرحيم 1لقـاسم ربك الذي خاقرأ ب

2لقــن عـــان مــق الإ�ســــخل

3رمــــرأ وربك الأكــاق

4مــــم بالقلــــذي علــال

5مــــان ما لم يعلـــم الإ�ســـعل

           )سورة العلق ( صدق اهلل العظيم

To the memory of my

father.

To my mother with my

gratitude.

To my brothers with my

thanks.

To my wife and kids with

my love.

Acknowledgment

First of all, praise be to ALLAH for the support in all my life.

Special mention has to be made to my supervisor Dr. M.Z. AL-Faiz, I offer him my deepest appreciation for his continuous work the preparation of the thesis. He tried hard to ensure the high quality of the work.

Special thanks are due to Dr. Mohammed Abd AL-Waihed, Dr. Aimd AL-Salihy, Dr Firooq in the Medical City hospital and all who have helped me directly or indirectly in this work.

Special thanks are due to all staff of AL Rasheed College for helping me to complete my study.

Special thanks go to my mother and my brothers for their endless support and encouragement.

Last, but not least, special thanks go to my wife for supporting me all the time.

Abstract

The real human arm is a complex kinematics system. It works

according to instructions calculated by a highly accurate method, and has a

moving system working by deity manner. The design and simulation

analysis of arm moving system model with 7-Degree of Freedom (DOF) are

presented. The electromyography (EMG) signal is the activation signal for

muscles in human arm and accordingly the arm will have a certain

movement.

The arm dynamic system in real environment simulation was

implemented using the inverse kinematics problem (IKP) analytical solution.

The computation time of this simulation is very fast and has the ability to

include any real constrain. The finite recurrent back propagation neural

network ( FRBP-NN ) is used with the simulated system for identification,

classification, and human arm movements recognition with respect to a

specific EMG signal. Virtual Reality Toolbox, which is interfaced with the

Simulink\ MATLAB, is used as the environment for system simulation.

This work consists of four main parts:

The first part presents a proposed algorithm for analytical solution of

IKP introduced with its simulation. The simulation results are then compared

with another simulation results for the latest algorithms in this field.

The second part describes the EMG signal, its biology generation,

EMG sources, specification and characteristics. Moreover, it presents the

EMG real measuring system. Analysis of the processed signal is done to

select suitable features (amplitude and period) that can be used in the neuro-

identifier. A proposed structure of neuro-classification and neuro-

recognition is used for human arm movements based on EMG signal. An

i

EMG simulator is modified to generate an EMG data related to a clinical

specification which is used to simulate an EMG signal by using EMGlab

software.

The third part deals with the Virtual Reality (VR) environment. The

virtual reality is a simulation software for three dimension (3D) objects. It is

used to present different models ( human body model and skeleton human

arm ) for virtual human arm. Mixed reality human (MRH) is a new type of

embodied for objects in VR, that is used to present the model of skeleton

human arm. VR is used with the simulink to generate the commands for the

above mentioned models, which are calculated either by IKP or by a

recognized EMG signal.

In the fourth part a design of a proposed package is presented, which

introduces and solves all the above three parts. This package includes all

simulation data transportation between different programs, and it contains

many helps, which explain the individual parts for the working system and

important notes joined by photos and movies.

Satisfactory results are obtained, which give the solution of the

forward and inverse kinematic human arm by the proposed method.

Moreover, it shows the usefulness of using FRBP-NN to recognize the

movements of human arm using EMG signal and the effective use of virtual

environment to implement all movements of virtual human arm that are

similar to natural human performance.

ii

List of Contents

Subject Page

Abstract i

List of Contents iii

List of Symbols vi

List of Abbreviations ix

Chapter One : General Introduction

1.1 Introduction 1

1.2 Fundamental Concepts 1

1.3 Literature Survey 3

1.4 Aim of the Work 8

1.5 Work Contributions 8

1.6 Outline of Thesis 8

Chapter Two : Modeling, Analysis of Human Arm

2.1 Introduction 10

2.2 Autonomy Human Arm 10

2.2.1 Skeletal Structure 10

2.2.2 Human Arm Muscles 12

2.3 Types of Human Arm Joints and Models 14

2.3.1 Joint Rotations 14

2.3.2 Joints Model 16

2.4 Human Arm Kinematic 19

2.4.1 Forward Kinematic 20

2.4.2 The Inverse Kinematic (IK) 23

iii

2.4.2.1 Analytical Solution of I.K Human Arm 24

2.4.2.2 Analytical Solution of I.K Manipulator 32

Chapter Three : EMG Signal and Virtual Environment

3.1 Introduction 35

3.2 Physiology of (EMG) Signal 35

3.3 EMG Signal Fundamentals 38

3.3.1 Electrical Activity Generation of EMG Signal 38

3.3.2 EMG Characteristics 40

3.4 The Virtual Reality Axiom 43

3.5 Virtual Reality Requirements 43

3.6 VRML Coordinate System 45

3.7 Virtual Human Body 46

Chapter Four : Proposed Algorithm and Structures for Virtual Human

Arm Movements

4.1 Introduction 49

4.2 Proposed Algorithm of Human Arm Inverse Kinematics 49

4.3 Human Arm EMG Processing 57

4.4 Simulation Generated EMG signal 60

4.5 Simulation of Generated EMG and Measured EMG Signals 63

4.6 EMG Recognition System 65

4.7 Object Design in Virtual Reality: 68

4.7.1 Object Structure Design in VRML. 68

4.7.2 Human Body Project in VRML 70

iv

4.7.2.1 Skeleton of Human Body 70

4.7.2.2 complete Human Body 72

Chapter Five : Simulation Results and Discussion

5.1 Introduction 73

5.2 Proposed Package of a Complete Virtual Human Arm 73

Driven by EMG Signal

5.3 Measurement of Real EMG Signal 75

5.4 Neural Network Recognition System 83

5.5 Kinematics of Human Arm 94

5.6 Human Arm Movements Implementation in VR 99

5.6.1Virtual Human Body Model Demo 99

5.6.2 Virtual Kinematics Human Arm 101

5.6.3 Virtual Human Arm driven by EMG Signal 103

Chapter Six : Conclusions and Suggestions for Future Work

6.1 Conclusions 107

6.2 Suggestions for Future Work 108

References

Appendix A

Appendix B

Appendix C

v

List of Symbols

Symbol Symbol Name AD-H D-H parameters for shoulder joint to elbow joint Ae Transformation matrix for elbow to wrist joint Ai Homogenous transformation for joint No. i As Transformation matrix for shoulder to elbow joint ai Constant displacement of frame in axis xi

a(x,y,z) Position vector for elbow at zeros swivel angle BBD-H D-H parameters for elbow to wrist joint

C Center of circle for elbow movement ci Cosine of θi

di Constant displacement of frame in axis zi

enew New position of elbow position (cm) eold Old position of elbow position (cm)

e(ɸ) Position of elbow according swivel angle (cm) G Homogenous matrix of End-Effector Gf Homogenous transformation matrix for n joint ∧

n Normal vector of the plane swivel

nx,ny,nz,ox,oy,oz mx,my,mz

Parameters of orientation matrix For end-effector

P Position in space with (x,y,z) coordinate (cm) Rn×n Rotation matrix with dimension n×n

Rs & Re Rotation matrix for As & Ae respectively r Radius of circle for elbow movement (cm) si Sine of θi T1 Homogenous matrix for shoulder joint T2 Homogenous matrix for wrist joint te Transition vector for transformation between elbow and

wrist joints tg Position vector for target in space (cm)

tn×1 Transition vector with dimension n×1 xgt Coordinate of target at x-axis (cm)

ygt Coordinate of target at y-axis (cm)

vi

Symbol Symbol Name zgt Coordinate of target at z-axis (cm)

ts Transition vector for transformation between shoulder and elbow joints.

Ty Homogenous matrix for elbow joint ∧

u span the plane is the default elbow position

wnew New position of wrist position wold Old position of wrist position

XYZ Eular coordinate axis ∧∧∧zyx ,, magnitude vector for old position of elbow

,goalx∧ ∧ ∧

goalz,goaly magnitude vector for new position of elbow

αi Rotation of frame by bond angle around axis xi (rad) θ1 Rotation angle a round x-axis in shoulder joint (rad) θ2 Rotation angle a round y-axis in shoulder joint (rad) θ3 Rotation angle a round z-axis in shoulder joint (rad) θ4 Rotation angle a round y-axis in elbow joint (rad) θ5 Rotation angle a round x-axis in wrist joint (rad) θ6 Rotation angle a round y-axis in wrist joint (rad) θ7 Rotation angle a round z-axis in wrist joint (rad) θdi Rotation of frame by dihedral angle around axis zi (rad)

θe ϕe ψe Eular angles (θe around z-axis , ϕe around y-axis and ψe around x-axis) (rad)

ϕ Swivel angle for elbow (rad) cθdi Cosine of θdi in transformation matrix sθdi Sine of θdi in transformation matrix

vii

List of Abbreviation

Abbreviation Details AAR Area to Amplitude Ratio AL Anthropomorphic Limb AR Augmented Reality AT Anaerobic Threshold

CNS Central Nervous System CT Contraction Time D-H Denavit – Hartenberg DOF Degree of Freedom

DQEMG Decomposition based Quantitative EMG E-C Excitation – Contraction EDL Extensor Digitorm Longus EDS Extensor Digitorum Superficialis EMD Electromechanical Delay EMG Electromygraphic

FR Firing Rate GL Glntens Maximums

GTO Golgi Tendon Organ GUI Graphic User Interface

iEMG integrated electromygraphic IK Inverse Kinematics

IKP Inverse Kinematics Problem LG Lateral Gastrocnemius

MAV Mean Absolute Value MPF Mean Power Frequency MR Magnetic Resonance

MRH Mixed Reality Human MU Motor Uint

MUAP Motor Uint Action Potential MUAPT Motor Uint Action Potential Train

MVC Maximal Voluntary Contractions NMJ Neuromuscular Junction OR Objective Reality

viii

Abbreviation Details PSD Power Spectral Density RE Real Environment

RMS Root Mean Square SNR Signal to Noise Ratio

sEMG surface electromygraphic TA Tibialis Anterior VE Virtual Environment VH Virtual Human VR Virtual Reality.

VRML Virtual Reality Modeling Language 2D Two Dimensions 3D Three Dimensions

ix

Chapter One

General

Introduction

Chapter One

General Introduction 1.1 Introduction

There are numerous ways to model the human arm, especially with

respect to the shoulder area. This model allows accurate modeling of the

interdependence between joint movements. The virtual arm (VA) is a

mathematical and three-dimensional (3D) graphical representation of a

human arm. It is displayed on a computer screen and it moves in response to

an electromyographic activity recorded from the arm muscles.

Electromyographics (EMGs) are used to recognize the movements

developed by individual muscles crossing the bones of the arm.

This chapter introduces the fundamental concepts of some main terms,

gives the literature survey of the related work, presents the contribution of

this work in points of research, aim of the work and finally gives the outline

of the thesis.

1.2 Fundamental Concepts

Kinematics is concerned with the motion of articulated structures. The

surrounding environment and other physical forces are not considered. An

articulated structure is composed of links, i.e. rigid segments, and joints to

connect them. If a structure has no branches, it is referred to as kinematic

chain. The first element of such a chain is denoted as root, the last one is the

end-effector [1].

Motion is a change in the position of an object with respect to a

reference, and Mechanics is the science that studies the motion of objects.

For practical purposes, its treatment is split into two fields:

1

Forward Kinematics, is concerned with the computation of the

orientation and the position of the end-effector in the world frame for a

given a kinematics chain and a set of joint angles for the joints of the chain.

This is done by concatenating the frame transitions of the chain in the

correct order.

Inverse Kinematics (IK), is a widely used technique for controlling

the arms of human-like characters in interactive applications, and is

employed in several domains: ergonomics, virtual reality, computer games,

animation, etc. For instance, nearly all professional animation packages have

built-in IK solvers for manipulating skeleton hierarchies. The term IKP

refers to the specific problem of finding analytic solutions for every joint

angle and the degree of redundancy of the kinematics chain [3].

The EMG is sometimes referred to as a myoelectric activity. Muscle

tissue conducts electrical potentials similar to the way nerves do and the

name given to these electrical signals is the muscle action potential. Surface

EMG (sEMG) is a method of recording the information presented in these

muscle action potentials. EMG Electrodes The EMG signal is measured

either non-invasively with surface electrodes, or invasively, with wire or

needle electrodes [4].

The word virtual has been overused to describe just about anything

that deals with a computer–virtual banking or virtual teleconference. The

term virtual reality (VR) is “the effect created by generating an environment

that does not exist in the real world. Usually, a stereoscopic display is a

computer-generated three- dimensional environment giving the immersion

effect. The environment is interactive, allowing the participant to look and

navigate about the environment, enhancing the immersion effect” [5].

2

Although this definition has elements that enhance our understanding

of VR, it also limits our understanding of what virtual reality is by

specifying hardware that is used to create the “virtual” effect. Other

attempts at defining VR is “the combination of real-time 3-D computer

graphics with shading and texture mapping, high resolution stereoscopic

large screen or head mounted displays, along with novel user interfaces” [5].

1.3 Literature Survey

The system design in this work requires research in three main

approaches, so the review is presented according to these approaches. This

review will focus on the researches in the last nine years, due to the

extensive research activity in this period.

In EMG signal field, Gribble et.al., 2003 [6] introduced a study to test

for a possible relationship between cocontraction and movement accuracy in

multi-joint limb movements. The EMG activity of seven single- and double-

joint shoulder and elbow muscles was recorded using surface electrodes

while subjects performed a pointing task in a horizontal plane to targets that

varied randomly in size.

Nan Bu et.al., 2003 [7] proposed a new EMG discrimination method

based on a recurrent log-linearized Gaussian mixture network (R-LLGMN)

for prostheticc control. Because of the recurrent connections between the

third and the fourth layers in the R-LLGMN, the temporal information in the

EMG signal can be used for the pattern discrimination.

Bida, 2005 [8]  focused on demonstrating that advances in EMG

amplitude processors result in EMG-torque model performance

improvements. Advances in EMG amplitude estimation were applied to the

EMG-torque problem for constant-posture, non-fatiguing, and force-varying

3

contractions about the elbow. The dynamic relationship between EMG

amplitude and joint torque was formulated as a standard linear least squares

problem.

Hamilton and Stashuk, 2005 [9] developed a muscle model and EMG

simulation algorithms which were to produce EMG signals consistent with

those acquired from real muscle. The data from this simulation tool can be

used to develop and verify EMG signal analysis tools. Specifically, the

simulated signals can be used within EMG signal decomposition and

quantitative analysis algorithms to help develop and evaluate signal statistics

which correlate with specific aspects of the structure and activation of a

muscle. The most efficient EMG statistics can then be used for muscle

characterization and clinical interpretation.

Reaz et.al., 2006 [10] gave a brief information about EMG and

revealed the various methodologies to analyze the signal. Techniques for

EMG signal detection, decomposition, process and classification were

discussed along with their advantages and disadvantages. Discovery of a

problem or disadvantage in one method leads to other improved methods.

This study clearly points up the various types of EMG signal analysis

techniques so that right methods can be applied during any clinical

diagnosis, biomedical research, hardware implementations and end user

applications.

Hou et.al., 2007 [11] developed a spinal force prediction model using

an RFNN. The EMG feedback represents the muscular activation dynamics

better. At the same time, it utilizes the advantages of recurrent properties.

The model predicts forces directly from kinematics data, avoiding EMG

measurements and the use of biomechanics model. It can help us understand

4

the relationships between kinematic variables and EMG signals and spinal

forces. An adaptive learning algorithm is derived for the RFNN.

Cipriani et.al., 2008 [12] found a trade off between good grasping

capabilities of the device (strongly related to the complexity of the control

interface) and low subject effort into completing grasping tasks, without

addressing advanced algorithms for EMG signal processing. It determines

whether a vibrotactile feedback system is subjectively or objectively useful,

and how this system changes user performance.

Shrirao et.al., 2009 [13] presented that Root Mean Square (RMS) of

the surface EMG signals obtained from Dxtensor Digitorum Superficialis

(EDS) muscle during flexion-extension rotation of the index finger at

different speeds showed hysteresis. Six different neural network committees

were developed to predict the joint angle from the RMS o f the SEMG signal

. During testing, the neural network committees were able to predict the joint

In the human arm field, Baerlocher, 2001 [14] determined a posture

satisfying a set of prescribed tasks, where each task concern the interactive

manipulation of complex articulated figures by means of geometric

constraints usually expressed in the Cartesian space.

Wu et.al., 2004 [15] proposed a new analytic IK solver suitable for

multiple constrained 12-DOF human limbs. By decomposing human

skeleton into five parts one head chain , two arm chains and two leg chains,

a multi-constrained human skeleton can be solved analytically.

Mihelj, 2006 [16] The computation of the inverse kinematic model of

the human arm for robot based rehabilitation that uses measurements of the

hand position and orientation and radial acceleration of the upper arm,

analytical analysis and empirical validation of the method are presented. The

5

algorithm enables estimation of human arm angles, which can be used in

trajectory planning for rehabilitation robots.

Kallmann, 2007 [17] shows a new customizable whole-body IK

system which is fast, robust, and simple to implement. A new analytical IK

formulation based on the swing and-twist parameterization handles collision

avoidance and joint coupling in a unified way (source code provided). The

approach organizes key body postures as a function of the goal direction to

reach, simplifying interpolation and allowing for an intuitive way of

designing body behaviors.

Drzevitzky, 2008 [3] introduced IK problems for anthropomorphic

limbs and showed how to solve those analytically in order to obtain

symbolic solutions. The symbolic solutions can be modified and re-

computed to match ,for example, other input values that serve as constraints

when solving the according Inverse Kinematics problem.

Finally in VR field, Powell, 2007 [18] showed that the evolutionary

pathway of virtual reality technology development will not be able to

overcome all of the barriers and limitations inherent in the current generation

of interfaces. He used a reverse tree methodology to explore alternate

pathways to achieve strong VR.

Antonio .et.al., 2008 [2] presented an initial developed on virtual

agents. It describes how the agent was built searching an humanoid

representation through a geometric model. The building of a solid platform

gives total mobility and realism to both, the agent and the virtual

environment where our humanoid works. This work allow us to keep on

working on virtual agents area, to analyze and propose techniques to give the

agent total autonomy to its motion.

6

Tsepkovskiy et.al., 2008 [19] introduced the study for a design of the

3D and VRML virtual hand models for different types of mechanical

gripper. A 3D animation using Matlab/simulink is made.

Whitworth, 2009 [20] presented the idea that the universe is a virtual

reality created by information processing, and relates this strange idea to the

findings of modern physics about the physical world. The virtual reality

concept is familiar to us from online worlds, but our world as a virtual

reality is usually a subject for science fiction rather than science. Yet the

world could be an information simulation running on a multi-dimensional

space-time screen.

Kotranza .et.al., 2009 [21] presented mixed reality humans (MRHs), a

new type of embodied agent enabling a touch-driven communication.

Affording touch between human and agent allows MRHs to simulate

interpersonal scenarios in which touch plays a crucial role. Two studies

provide an initial evaluation of user behavior with an MRH patient and the

usability and acceptability of an MRH patient for practice and evaluation of

medical students’ clinical skills.

From the above survey it can be seen that there is a need to direct

research to further our knowledge in the modeling of the human body. The

design of virtual human arm driven by EMG signal is a necessity. This

requires analytical solution for the IKP of the human arm model and the

implementation of human arm in VR environment. Also the identification,

classification of EMG signal and the human arm movement recognition. The

ultimate grouping of these subjects should lead to the real life simulation of

the human arm.

7

1.4 Aim of the Work

The main objectives of this work can be summarized as follows:

1. Analytical solution of IKP for human arm to implement such a

solution with VR.

2. Design and analysis of EMG NN identification and classification

system that can be used to recognize the human arm movements based

EMG signal.

3. Designing different VR model for human arm to implement the

recognition of its movement by virtual environments.

1.5 Work Contribution

The contribution of this work falls in four points:

The first, an algorithm for analytical solution for IKP of human arm

with all real constrains in human arm is proposed.

The second, a structure for neuro-classification and human arm

movement recognition system based EMG signal is proposed.

The third, applying MVR concept for human arm to present the

skeleton human arm , human body and movement implementation.

The fourth, is building a suitable package to simulate all the above

systems with movies helps.

1.6 Outline of Thesis

The thesis is organized as follows:

Chapter One: Presents an introduction to the design of virtual human arm

based on EMG and reviews the main research activities in the three specific

fields. The aim of the current research is also set and summery for

contribution of work.

8

Chapter two: presents the modeling of human arm, forward kinematics and

analytical solution of IK for human arm manipulator and the analysis of the

results.

Chapter three: presents the background of EMG signal, biologist of signal,

fundamental of signal, characteristic of signal, its analysis, the measurement

system of EMG signal. This chapter presents VR axiom, its requirements

and VRML builder for VR and explain virtually of human body .

Chapter four: presents the details of proposed algorithm for IKP by

analytical method and presents the human arm EMG signal: processing of

this signal, generation by EMG simulator and its simulation, explain the

design of classification EMG signal and recognition of movements of human

arm by using NN. It presents the details of virtual design for skeleton

human arm, virtual skeleton human body and human body form.

Chapter five: presents simulation results for all parts of system design and

its discussion. The GUI designed for demonstration and simulation purposes

is also explained.

Chapter six: presents the conclusion of this work and suggestions for future

work.

References: presents the references with my two papers published.

Appendix A: presents the analytical solution for swivel angle and elbow

joint angle by classical approach.

Appendix B: presents program details of the simulation of the virtual

human body and its results.

Appendix C: presents some program in MATLAB which are used in this

work.

9

Chapter Two

Modeling, Analysis of

Human Arm

Chapter Two

Modeling, Analysis of Human Arm 2.1 Introduction

The human arm can be modeled as a 7-DOF mechanism, consisting

of three joints with two links and the hand. The IKP of the human arm can

be stated as follows: given the position and the orientation of the hand, find

the seven joint angles.

This chapter presents the autonomy human arm which include the

muscles and the skeleton structure of the arm. Joints arm types, their model

and joint limitation with the prescribed D-H parameters. The forward and

inverse kinematics are presented together with the analytical solution of IKP

for a real human arm. Two case studies are considered with full analytical

solution, one for real human arm and the other for manipulator as a human

arm with 5-DOF.

2.2 Autonomy Human Arm

In order to develop a consistent topological data structure, a

preliminary modeling of the musculoskeletal structure is necessary. Since

biomechanical modeling is intended, it is natural to begin with the

observation of the system components and their motion before modeling. In

the following section, basic anatomical descriptions of the human arm are

presented to outline various structures involved in its mechanics [22].

2.2.1 Skeletal Structure

The human arm is composed of three chained mechanisms, the

shoulder girdle, the elbow and the wrist, whose association allows a wide

range of combined motion, and confers to the human arm the highest

10

mobility in the human body. Due to the complexity of the hand mechanics, it

was taken as another rigid segment in the extension of the forearm.

Considering bones in pairs, seven joints may be distinguished: The

sterno-clavicular joint, which articulates the clavicle by its proximal end

onto the sternum. The acromio-clavicular joint, which articulates the scapula

by its acromion onto the distal end of the clavicle. The scapulothoracic joint,

which allows the scapula to glide on the thorax. The gleno-humeral joint,

which allows the humeral head to rotate in the glenoid fossa of the scapula.

The ulno-humeral and the humero-radial joints, which articulate both ulna

and radius on the distal end of the humerus, and finally the ulno-radial joint

where both distal ends of ulna and radius join together with the hand [23].

Assuming translations negligible compared to rotations, all, except the

scapulothoracic joint, are usually assumed as ball and socket joint, having

more or less 3-DOF. The scapulothoracic joint is a special case since it does

not properly involve articular structures between scapula and thorax.

However, due to its surrounding muscles, the scapula is usually considered

as constrained to glide on the thorax. This reduces the number of DOF of the

scapulothoracic joint to four. Considering all joints independently, the

number of DOF of the upper limb would amount to (22). However, as they

are organized in closed chains, the number of DOF of the upper arm reduces

to 7-DOF[24].

Fig.2.1 illustrates the bones of human arm starting with the clavicle or

collar bone, which is classified as along bone that makes up part of the

shoulder girdle, it receives its name from the Latin claviculea (little key)

because the bone rotates along its axis like a key when the shoulder is

abducted. The second bone is the scapula or shoulder blade, which connects

the humerus with clavicle, while the third bone is the humerus a longer bone

11

in arm runs from shoulder to elbow, in skeleton it fits between the scapula

and the ulna. The forth part is the radius and ulna bones which connects the

elbow to wrist by radius bone from the outside of limb to thumb side of the

wrist and the ulna from the inner side of arm to form hinge joint with the

trochlea of the humerus [25].

Clavicle

Scapula

Humerus

Radius

Ulna

Hand

Fig. 2.1. Skeleton of Human Arm [25]. .

2.2.2 Human Arm Muscles

The human arm has 22 main muscles, it is divided to three groups ,

the muscle of shoulder girdle, the muscle of upper arm and the muscle of

forearm. These muscles are shown in Fig’s (2.2 and 2.3), [26,27].

12

Biceps brachii Long head

latissimus

Deltoid Pectoralis major

Coracobrachialis

Biceps brachii short head

brachialis

Trapezius Deltoid

Teres major

Triceps brachii Long head

Triceps brachii

Fig.2.2 Muscles of Shoulder and Upper Arm [27]

.

Fig.2.3 Muscles of Forearm [27]

Lateral head

Latissimus

Triceps brachii Medial head

dorsi

Triceps brachii

brachioradialis Extensor carpi

Anconeus Extensor digitorum

Extensor crapi Flexor crapi Extensor carpi

ulnarisExtensor minimi

Biceps brachii brachii

brachioradialis

Pronator teres

Extensor carpi Radialis longus

Palmaris longus

Extensor carpi Radialis brevis

Flexor crapi ulnaris

Flexor digitorum superficialis

Flexor crapi ulnaris

13

2.3 Types of Human Arm Joints and Models

The state vector of generalized coordinates θ=( θ1, ...., θn )T,

expresses the configuration (or posture) of the articulated structure, which is

the configuration of all its joints. Hence the set of all possible configurations

are termed the joint space. The total number of degrees of freedom is n,

while the number of joints is k, with k ≤ n. For multiple-DOF joint models,

there is no simple one-to-one correspondence between a degree of freedom

and a joint: hence the generalized coordinates of the jth joint are noted θj,

whose number of degrees of freedom is noted nj. Hence:

∑=

=k

jj nn

1 (2.1)

As example, for a typical human arm, the number of degrees of

freedom (without the fingers) is n≈50, while the number of joints is k≈20.

Now the problem is reduced to find a parameterization for each joint, and to

impose limits on its motion. For this purpose, a minimal set of joint models

must be defined[14 ].

2.3.1 Joint Rotations

To model a joint, it is first necessary to define the respective joint

frame of each bone in a pair to enable the parametric description of its state.

Such description is then fully achieved with a transfer matrix relating its

relative frame to its reference frame. Various compositions exist to describe

the three-dimensional orientation of a frame with respect to another. The

model chooses successive local rotations with respect to the reference frame

of the joint: the rotation matrix changes the global frame (X0,Y0,Z0) into

the local frame (X3,Y3,Z3) of the joint and it is given by[28]

14

)()()(),,( ezeyexeee MMMM θψφθψφ = (2.2)

The coordinates in the global frame of any vector V and its image vector V'

by the above rotation may then be obtained by the relationship:

VMV eee ⋅= ),,(' θψφ (2.3)

Due to the planar symmetry between the left and right sides, it has

been necessary to define a different rotation order for each one, in order to

respect the convention of "direct coordinate system" and the anatomical

rotation order usually considered by practitioners. The intermediate

successive rotations around the local axis are shown in Fig. 2.4. for the left

arm and in Fig. 2.5. for the right arm[29].

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡−=

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

−=

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡ −=

ee

eeex

ee

ee

ey

ee

ee

ez

M

M

M

ψψψψψ

φφ

φφφ

θθθθ

θ

cossin0sincos0

001)(

cos0sin010

sin0cos)(

1000cossin0sincos

)(

Zo

Yo Xo X1

X3

X2=X1

Y3=Y2

Y1

Y2

Z1=Zo

Z2 Z3

ψe

ψe

ϕe

ϕe

θe

θe

Fig. 2.4 Rotations for the Left Arm [29]

15

⎥⎢ −=exM φφφ cos)(

Fig. 2.5 Rotations for the Right Arm [29]

2.3.2 Joints Model

The first purpose of a joint model is to compute a local transformation

matrix, noted , as a function of the set of generalized joint coordinates θj

These parameters represent either translational or rotational degrees of

freedom. However, translations are not considered here, since they do not

appear in the human body (except to a very limited extent).

The second purpose of a joint model is to define limits on the joint

coordinates θj, in order to avoid self-collisions between adjacent segments

and also the specification of unfeasible postures. The main joint models are

explained next [29].

A. The revolute joint model (one DOF): The revolute joint is the simplest

joint model that allows rotational motion: rotation occurs about a single,

fixed axis. Arbitrarily choose for the axis of rotation to be the z axis of the

local joint frame. Its natural parameterization is the angle of rotation θ, with

respect to a reference configuration: hence, the local transformation matrix

of the joint is simply Rz(θ).

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

−=

⎥⎥⎦

⎤

⎢⎢⎣

⎡

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡ −=

ee

ee

ey

ee

ee

ee

ee

ez

M

M

ψψ

ψψψ

φφ

θθθθ

θ

cos0sin010

sin0cos)(

cossin0sin0001

1000cossin0sincos

)(

Zo

Yo Xo X1

X2

X3=X2

Y2=Y1 Y1

3

Z1=Zo

Z2 Z3

ψe

ψe

ϕe

ϕe

θe

θe

Y

16

A revolute joint is typically used as a hinge joint (for flexion

purposes). If the axis of rotation is aligned with the distal (moving) segment,

a rotation results in a twist of the distal segment about itself. Hence the

revolute joint model may be used for two conceptually different motions:

flexion and twist [30].

θ

child segment

parent segment

Fig.2.6 A revolute joint connecting two segments, performing flexion

motion [14]

Because of its simplicity, and due to mechanical design

considerations, the revolute joint is by far the most used joint in robotic

manipulators. In human modeling, it is a convenient model for the flexion of

the interphalangeal joints of the hands, for example. It is tempting to

combine two or three revolute joints at the same point with different axes of

rotation to model more complex joints such as the shoulder [17].

B. The elbow joint model (2-DOF): A situation, where two revolute joints

can be easily combined to model a more complex joint is for joints with a

flexion/extension motion, combined with a twist of the outgoing segment.

Typical examples in the human body are the elbow and knee joint. The two

axes or rotation are independent, and joint limits can also be specified

independently one each degree of freedom [31].

C. The ball-and-socket joint model (three DOF):

A ball-and-socket joint possesses three rotational degrees of freedom.

Hence, it is the most mobile of the purely rotational joints. It allows an axial

17

motion (or twist) of the segment (1-DOF), as well as a spherical motion (or

swing) that determines its direction (2-DOFs). A mechanical illustration of

this joint is given in Fig.2.7. By convention, in the following discussion the

moving segment is aligned with the z axis of the local joint frame. Ball-and-

socket joints are used to model articulations such as the human shoulder and

the wrist [14].

Fig 2.7 A Ball-and-Socket Joint. The Outgoing Segment is Aligned with the

z-axis [14]

The accurate kinematic modeling of such articulations is a difficult

task. First, a clear mathematical description of the allowed relative motion

must be given by a proper parameterization: because of the complex non-

Euclidean nature of rotations, this must be done carefully, or one may incur

in the problem of singularities. Second, the range of motion should be

constrained by some joint limits, to restrict the parameter space to some

more realistic subset. The situation is complex, because the boundaries on

the three independent parameters are generally coupled. These two aspects

are discussed in more detail in [14] .

18

2.4 Human Arm Kinematic

The development of a high-DOF, kinematic human arm model can be

used to predict realistic human arm postures. A model was built upon the 6-

DOF spine and 5-DOF shoulder [32]. This model falls short of realistic arm

postures, however, as a result of spine rigidity and unrealistic skin

deformations with shoulder movements. The goal is to develop an improved

model that leads to more natural movement and allows for more realistic

skin deformations in the shoulder.

The model of human arm with full real movements depends on the

selection of origin point in human body to built the set of kinematic

matrices. If one consider the spine include with human arm, the model will

appear with 15-DOF or 21-DOF (see Fig 2.8 [33]), but when he select the

origin at the end of configuration of arm ( the shoulder joint) the form will

change as shown in Fig 2.9.

zo

z2 z1

z3

z4

z5

L1

L2

L3

L5

L4 L6

L7 L8

L9

z6 z7 z9

z8

z10

z11

z12

z13

z14 z15

Figure 2.8 A 15-DOF, Kinematics Human Model [33]

19

In this work, the anthropomorphic arm will be dealt with a 7-DOF

assuming the origin is at the shoulder joint. The kinematic chain to be

introduced resembles a human arm. Since most anthropomorphic limbs have

the same setup, the chain is a representation for all human limbs. The

distinction between limbs can be defined through joint limits. Usually one or

more joint limits are shifted when considering two identical but mirror-

inverted limbs, e.g. a left and a right arm.

x1

x3

y1

y2

y3

z1

z3

θ1

θ2

θ3

θ4

θ5

θ6

θ7

e

w

s

La=a3

Lb=a4

Fig 2.9 Kinematic Chain with Three Joints

2.4.1 Forward Kinematic

The homogeneous transformation matrices for the frame transitions

are set up with D-H parameters. The description of the rotation and the

translation that occur in those frame transitions in between the frames of the

joints. The 3 × 3 rotation matrices (embedded in homogeneous

20

transformation matrices) are denoted by Rs,Re and the translation vectors of

length 3 are denoted by ( ta,tb ), respectively. The transformation matrix from

the shoulder joint frame to the elbow joint frame is denoted by As, the

transformation matrix from the elbow joint frame to the wrist joint frame by

Ae, [22].

⎪⎪⎭

⎪⎪⎬

⎫

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡

=

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡

=

1000

,

1000

eee

sss

tRA

tRA (2.4)

As mentioned before, the shoulder joint and the wrist joint are both

assigned three DOFs, while the elbow joint only one DOF. The angles

(θ1,…,θ3) specify the rotation around the x-axis, y-axis, and z-axis of the

shoulder, respectively. Without loss of generality it will be assumed that X-

Y-Z Euler angles are being used, i.e. the rotation of angle θ1 around the x-

axis is the first one, followed by the rotation of angle θ2 around the y-axis,

concatenated with the rotation of angle θ3 around the z-axis therefore; the

homogeneous matrix for shoulder joint can be written as [3]:

⎪⎪⎪⎪⎪

⎭

⎪⎪⎪⎪⎪

⎬

⎫

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡⋅⋅

=

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡

=

100000)()()(0

100000),,(0

123

32111

θθθ

θθθ

xyz RotRotRot

RT

(2.5)

21

Analogously, the rotations of the wrist are specified by the angles θ5,…,θ7.

The 3 × 3 rotation matrix R2 of the wrist joint is a function of θ5 to θ7 and the

matrix T2 is the equivalent to the matrix T1, i.e.,

⎪⎪⎪⎪⎪

⎭

⎪⎪⎪⎪⎪

⎬

⎫

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡⋅⋅

=

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡

=

100000)()()(0

100000),,(0

567

76522

θθθ

θθθ

xyz RotRotRot

RT

(2.6)

The rotation of θ4 around the y-axis of the elbow is realized with the rotation

matrix Ry. The according homogeneous transformation matrix is Ty. We can

directly express this matrix in the following way [3]:

⎪⎪⎪⎪⎪

⎭

⎪⎪⎪⎪⎪

⎬

⎫

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡

−=

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡

=

100000001000

1000

0)(

44

44

4

θθ

θθ

θ

cs

sc

RT y

y

(2.7)

Given that the first joint of our kinematic chain is located at the origin

of the world frame, the position and orientation of the end-effector is given

by the concatenation of all the homogeneous transformation matrices. Those

are the matrices representing the joint rotations and those representing the

frame transition in between the joint frames. Since all rotations and

22

translations are presented in homogenous coordinates, the matrices can

simply be multiplied [3].

21 TATATG eysf = . (2.8)

The orientation and position of the end-effector in the world frame

can be represented with the rotational part Rg and the translational part tg, the

matrix G takes the form:

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡

=

1000

gg tRG (2.9)

2.4.2 The Inverse Kinematics

The inverse kinematics technique is useful both for the manipulation

and animation of articulated figures. The focusing on the inverse kinematic

problem, and on the issues raised by its solution. Basically, the problem is to

determine a joints configuration for which a desired task, usually expressed

in Cartesian space, is achieved. For example, the shoulder, elbow and wrist

configurations must be determined in order that the hand precisely reaches a

position in space. The equations that arise from this problem are generally

non-linear, and are thus difficult to solve in general [3].

There is a large number of methods to solve the inverse kinematics

problem. Each has its own advantages and drawbacks. They can be

compared on the following important criteria: efficiency (speed), robustness,

generality, naturalness of the result (except for robot manipulators), and

complexity of the method. The choice of a solution method clearly depends

on the intended application. For real-time applications, analytic methods are

23

always preferable, therefore; the analytical solution will be implemented in

this work in two forms as given below [31].

2.4.2.1 Analytical Solution of Inverse Kinematic Human Arm

The solution for the seven joint angles of the kinematic chain

introduced and the chain has one degree of redundancy. This means that

even if all seven joint angle values and of course the end-effector position

and orientation are known, it is still not possible to describe the position of

every joint of the chain. Now will determinate how to analytically describe

all DOFs and the degree of redundancy with the help of additional

constraints for all human arm joints [3].

Solving for Elbow Joint θ4:

The elbow joint θ4 is the only of the seven joints angle that regulates

the distance between the shoulder frame and the wrist frame. It does not

depend on the other joints and can be computed using the end-effector

position tg, given the matrices As and Ae, i.e. the D-H parameters describing

the arm. The translation of the end-effector in the world frame is contained

in matrix G. For the sake of readability, Ri(θi,θi+1,θi+2) is referred to as Rj.

Following equation (2.8), to get[3]:

21

111121

21

21

..

10001000

1000000

10001000000

10001000000

RRRRRR

tRtRRRtei

tRtRRRRRRRRtR

RtRRtRR

TATATG

byag

seysgseysbyagg

eeyss

eys

=

+=

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡+

=

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡

=

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡

⋅

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡

⋅

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡

⋅

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡

⋅

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡

=

=

(2.10)

24

As before, it can be assumed the shoulder to be positioned at the

origin of the global coordinate system. The distance between shoulder and

wrist is therefore the length of the translation vector between world frame

and end-effector frame, which is the same as the position of the wrist in the

world frame tg. As can be seen in equation(2.11), T1 does not contribute to

the distance. This is to be expected, since it contains merely the matrix R1,

which is the rotation of the wrist. The right hand side as well as the left hand

side of equation(2.11) represents the translation vector, which means both

sides must have the same length. Taking the dot product of the left hand side

and the right hand side of the equation and the definition of D-H parameters

the matrix As & Ae [3]:

⎪⎪⎪⎪⎪

⎭

⎪⎪⎪⎪⎪

⎬

⎫

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡−−

−

=

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡−−

−

=

1000

0

1000

0

3222323

3222323

233

2111212

2111212

122

dccscssdsscccs

asc

A

dccscssdsscccs

asc

A

e

s

αααθαθαααθαθ

θθ

αααθαθαααθαθ

θθ

(2.11)

From all above equations the following formula can be determined:

).2()()()(

)22()22(

211122

22

21

21

22211211142112114

dsasdadatgtgtg

addccasdcdacac

zyx αθ

αθθαθθ

−+−+−++=

+⋅++⋅ (2.12)

From the full solution of the above equations, the final form of θ4 can be

found (see Appendix A):

25

Solving for Swivel Angle (φ ) Elbow Position e(φ )

As explained at the beginning of this section, the matrix G consists of

a rotation matrix Rg and a vector tg. This vector is made up of three

coordinates in the world frame, the rotation matrix is the concatenation of

three rotations. The three coordinates of vector tg together with the three

angles of rotation implicitly given by Rg are six constraints. These six

constraints are imposed on the end-effector. This means that equation(2.8)

has to be solved according to these constraints. These six constraints do not

allow to determine the entire kinematic chain unambiguously, the chain has

a degree of redundancy. For a fixed end-effector, the elbow can swivel about

an angle φ from position (e) to position e(φ ) on a circle around the axis that

connects shoulder and wrist, see Fig 2.10[3]

w

e e(ϕ) s

y

x

z

ϕ

Fig 2.10 Elbow arc with center C and radius R. Elbow position e is rotated

about the normal of the plane to new elbow position (e(ϕ) ) [17]

The elbow position can be expressed as a function of the swivel angle

φ , for which the center C, the radius r and the plane of the circle, i.e. the two

26

vectors defining the plane, are used. The normal vector of the plane is the

vector from shoulder to wrist, which is the translation of the end-effector in

the world frame t

∧

n

g after being normalized in the Euclidean norm. The first

vector to span the plane is the default elbow position (the vector from s

to an arbitrary elbow position e) projected onto the plane. For this position,

the swivel angle is set to (0). The second vector of the plane is

perpendicular to the first one and the normal of the plane. It can be obtained

by using the cross-product of the other two vectors. The elbow position e(

∧

u∧

a

∧

v

φ )

can be calculated as the sum of the center C and a linear combination of

and [3].

∧

u∧

v

))sin()(cos()(

).(

).(

∧∧

∧∧∧∧

∧∧∧∧

∧∧∧∧∧

++=

×==

−

−=

vurCe

unvswswn

nnaa

nnaau

φφφ

(2.13)

The standard elbow position which equals φ = 0, e(ϕ)=e, is known,

then for any given swivel angle φ the current elbow position can be

computed using equation(2.13). The detail solution of the swivel angle φ

and the elbow position e(φ ) and the angles of joints (θ1 --- θ7) can be seen in

Appendix A.

Case study one:

The above algorithm was simulated by Drzevitzky [3] using maple

software, which is resimulated in this work by using Matlab software

Ver.2008a with the following numerical example. From the summary of

algorithms it can be seen that the key for analytical solution is the

27

calculation of (θ4), (note: literature survey reveals that this reference is the

first full real solution of inverse kinematic), but from the simulated results in

[3], (see Fig.2.11) and the simulated results by using Matlab Fig(2.12 A), the

same results are obtained for (θ4) is unacceptable as was deduced in

reference[3]. Therefore, S. Drzevitzky suggested a trial and error procedure

to get the correct value. Fig(2.12 B) shows the results of the simulation for

(θ4). The human limb D-H parameters which was used in the simulation is

shown in table(2.1).

θ 4 (r

ad)

Position of enf-effector in x-axis

Fig.(2.11) The result of [3] for suggested formula.

(rad) (rad)

Fig. 2.12. Simulation of θ4 . a: solution of equation(2.12). b: solution of

suggested formula of ref [3].

28

Table(2.1). D-H parameters for human limb [3].

D-H parameter θd1 d1 α1 a1 θd2 d2 α2 a2

value 0 0 0 1 0 0 0 1

The simulation of all steps in algorithm to explain the effect of value

of θ4 on the final result of solution of inverse kinematic. The simulation has

three steps: the first simulate with the first value of θ4 in equation(2.12). The

second step using the second value of θ4 in equation(2.12). The last step,

using trial and error for many value θ4 and get the good result with the value

(θ4 =1.571). The decision of good result achieved by checking the values of

(R1 , R2 , G , the value of joints angle θ1 … θ7) matrix and checking the

accuracy of algorithm by calculating the error between the target and

measuring of the position and orientation of end-effector. The D-H

parameters and all initial values for simulation are shown in table (2.2).

Table(2.2). D-H parameters and all initial values[3]

parame

ter

value

parame

ter

value

param

eter

value

parame

ter

value

θ1 π/4 ax 0.5 tgzo 4 θtgy π/2

d1 2 ay 6.5 exo 3 θtgz π/4

α1 π/4 az 6.5 eyo 3

a1 2 ex 2 ezo 3

θ2 π/4 ey 0.45 tgx 2.971

d2 2 ez 2 tgy 1.354

α2 π/4 tgxo 4 tgx 2.04

a2 2 tgyo 4 θtgx π

29

Table.(2.3).Analytical results for full simulation of algorithm.

Results\

No. of

step

One: with value of

θ4=-4.1574. rad

Two: with value of

θ4=3.8075. rad

R1⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡−−−

=5774.05774.05774.08122.04788.03334.00839.06614.07453.0

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡−−−

=5774.05774.05774.08122.04788.03334.00839.06614.07453.0

R2⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

−−−−

−−−=

7610.02906.05800.05904.00601.08049.02687.09550.01258.0

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

−−

−=

7610.02906.05800.05409.07778.03201.03580.05574.07491.0

Gf

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡

−−−−−−−−

=

10000614.17610.00449.09920.06525.19342.03439.00952.06041.23369.09550.00824.0

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡

−−−−

−

=

10004956.10338.00129.09993.08712.09978.00570.00330.0

2524.30565.09983.00184.0

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡−−−

=

10003090.20000.00000.00000.13540.17071.07071.00000.09710.27071.07071.00000.0

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡−−−

=

10003090.20000.00000.00000.13540.17071.07071.00000.09710.27071.07071.00000.0

G

Angle

of

Joints

(rad)

,7259.1,6187.0,7768.2,4206.0

,6155.0,7854.0

76

53

21

−====

−==

θθθθθθ

,4039.0,6187.0,3648.0,4206.0,6155.0,7854.0

76

53

21

−=−=−==−==

θθθθθθ

Error

in

position

of

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡=

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

2476.12985.03669.0

z

y

x

eee

end-

effector

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡=

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

8046.32252.22814.0

z

y

x

eee

30

table 2.3 (Continued) Results\No. of step three: with trail value of

θ4=1.571. rad

R1⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡−−−

=5774.05774.05774.08097.04961.03136.01054.06485.07539.0

R2⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

−−−

−−=

7426.03063.05956.06590.04929.05681.01195.08144.05679.0

Gf

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡

−−−−−−−

=

1000309.20208.00086.09997.0

2545.17071.07070.00087.00154.37068.07071.00208.0

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡−−−

=

10003090.20000.00000.00000.13540.17071.07071.00000.09710.27071.07071.00000.0

G

Angle

,3559.2,6380.0,7503.2,3942.0

,6155.0,7854.0

76

53

21

−====

−==

θθθθθθ

of

Joints(rad)

Error in

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡=

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

0090.00455.01154.0

z

y

x

eee

position of

end-effector

The results are shown in Figs(2.11 & 2.12) and table(2.3), are the

results obtained by the simulation in ref[3]. From the simulation results it

can be seen that the algorithm is complex due to the complexity of the

equations. These problems will be considered in chapter four where a new

algorithm for IKP is proposed.

31

2.4.2.2. Analytical Solution of Inverse Kinematic Manipulator:

Describe the real human arm as a manipulator by imaging the form of

arm in space and then writing the D-H parameters tables for each joint of

arm. Human arm has three main joints ( shoulder joint with 3-DOFs , elbow

joint with one DOFs and wrist joint with 3-DOFs). Fig(2.13) shows some

manipulators that represent the human arm which are called robot arm.

Every manipulator has fixed initial D-H parameters that describes its posture

in spaces[34]

Fig 2.13 Some types of manipulator of a human arm [34]

Solving of IK for human arm as manipulator by analytical solution

(see, Fig.2.13.A,B,C), can be done by calculating D-H parameters for every

DOF in each joint and then using the direct solution [16].

-A- -B- -D- -C-

Case study two:

Now, solving the numerical example for above algorithm of

manipulator with 5-DOFs , it represents the Lynx 6 robot arm[34]. The D-H

32

parameters for this manipulator are shown in table(2.7) and the initial

position and orientation are shown below[34]:

Table(2.4). D-H parameters of robot arm.

joint θrad dcm acm αrad

1 π/4 0 0 π/2

2 π/3 0 12.065 0

3 π/3 0 12.065 0

4 -π/4 0 14.249 - π/2

5 π/3 0 0 0

6 0 0 0 0

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡

=

10009640.1900000103140.26001

positioninitial

The transformation matrix for the given values in table(2.7), can be

calculated in general form:

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡−−

−−−

=

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡−

=

1000011004.1005.086.003.6086.05.0

;

1000899.701007071.007071.007071.007071.0

21 AA

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡

=

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡−

=

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡

−−−

=

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡−−

=

1000010000100001

;

1000899.701007071.005.007071.0086.0

10000010

07..1070.0070.007.1070.0070.0

;

10000110

04.1005.086.003.6086.05.0

65

43

AA

AA

33

The total transformation matrix, between base of manipulator and the

end-effector is:

61T

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡

−−−−

=

10004841.329659.01294.02241.0

7322.91830.02709.09451.07322.91830.09539.02380.0

61T

When the algorithm of analytical solution is applied to calculate the angle of

joints, the following results are obtained:

rad

rad

rad

rad

sc

rad

5234.086.049.0tan

7884.01735.451735.303015

5236.04.993714.99371tan

5267.086.05.0tan

5.03;86.03

7854.073.973.9tan

15

4

12

13

11

=⎟⎠⎞

⎜⎝⎛=

−=−=−−=

=⎟⎠⎞

⎜⎝⎛

+−

=

=⎟⎠⎞

⎜⎝⎛=

=⋅=

=⎟⎠⎞

⎜⎝⎛=

−

−

−

−

θ

θ

θ

θ

θ

The accuracy of this algorithm is calculated by comparing the target

value of end-effector and the calculated value[34]

Table (2.5). Error value for end-effector position.

Position

value

Calculated

value(cm)

Target

value(cm)

Error

value

px 9.73 9.3 0.43

p 9.73 9.27 0.46 y

p 32.48 31.85 0.63 z

34

Chapter Three

EMG Signal and Virtual

Environment

Chapter Three

EMG Signal And Virtual Environment

3.1 Introduction

The EMG signal which is generated by the muscles activity is located

inside the body and diffuses to the skin surface of human body. The EMG

signal must be processed and filtered to be a useful signal for recording,

monitoring and controlling. All organisms and instruments measuring of the

human called "Man Instrument system".

Interpersonal simulation allows users to practice their interpersonal

skills by communicating with a virtual human (VH). In current interpersonal

simulators, the communication between a user and the VH takes the form of

a conversation consisting of bidirectional speech and simple gestures. Use of

VH’s and interpersonal simulation is rapidly expanding to educate users in

medical, physical examinations , education and many application.

This chapter has two main parts, the first part presents the anatomy of

EMG signal, measurement, analysis, and its processing. The second part

presents the basic of VR, and the using of mixed virtual reality(MVR) in

human application.

3.2 Physiology of (EMG) Signal

Human body is a typical complex system, therefore, the biological

signals such as skin surface EMG signal contains a lot of control commands.

It is very difficult to obtain the same EMG signals for the same motion even

with the same person. Furthermore, each muscle activity for a certain motion

is highly nonlinear, because the responsibility of each muscle for the motion

varies in accordance with joint angles [35].

35

One muscle is not only concerned with one motion but also with other

kinds of motion. Moreover, activity level of each muscle and the way of

using each muscle for a certain motion is different from person to another.

Physiological condition of the user also affects the activity level of muscles.

In addition to these problems, the activity level of some muscles such as

biarticular muscle is affected by the motion of the other joint [36].

The relation between the load acting on the other joint and the change

in biarticular muscle activity level is different from person to another.

Furthermore, the activity level of muscles is affected by the external load

acting on the arm, therefore flexible and adaptive nonlinear control must be

applied to control the robot with the skin surface EMG signals. Moreover,

real-time control ability is required by the controller for a power assist robot

since motion delay gives a lot of stress to the user [23].

Understanding EMG signals implies the understanding and the way

they generate bioelectrical signals. It also implies the understanding of the

"forward problem", that is, how specific mechanisms and phenomena

influence the signals, as well as the more difficult problem is "Inverse

problem", that is, how the signals reflect certain mechanisms and

phenomena and allow their identification and description. The concept of

forward and inverse problem is familiar to physiologists and engineers and is

strictly associated to the concept of a system as a set of inputs, transfer

function and outputs, and of a model, as a set of descriptions and relations

associating, under certain conditions and assumptions, the inputs to the

outputs [37].

Motor unit (MU)

The central motor system and the concept of the MU are presented in

Fig.3.1. The central nervous system is organized in a hierarchical fashion.

36

Motor programming takes place in the premotor cortex, the supplementary

motor area, and other associated areas of the cortex. Inputs from these areas,

from the cerebellum and, to some extent, from the basal ganglia converge to

the primary motor cortex and excite or inhibit the various neurons of the

primary motor cortex. The outputs from the primary motor cortex have a

powerful influence on interneurons and motoneurons of the brain stem and

of the spinal cord [35].

Force=motoneuron + muscle fibers

Motor unit

Fig 3.1 A schematic representation of basic motor control mechanisms and of the motor unit and its components [35]

In voluntary contractions, force is modulated by a combination of MU

recruitment and changes in MU activation frequency. The greater the

37

number of MUs recruited and their discharge frequency, the greater the force

will be. During full MU recruitment the muscle force, when activated at any

constant discharge frequency, is approximately (2 - 5) kg/cm2, and in

general, this is relatively independent of species, gender, age, and training

status [38].

3.3 EMG Signal Fundamentals

3.3.1 Electrical Activity Generation of EMG Signal

Electrical activity in the muscles arises from the contraction of the

muscle fibers, the structure of which is shown in Fig.3.2. Each muscle fiber

contains a bunch of myofibrils (long chains of contractile units). The

myofibrils contain long chains of contractile units called sarcomeres, which

contribute to the force exerted within the muscles [8].

Each of the myofibrils is chemically activated by local neurons,

generating an electrical charge that moves up and down the myofibril,

activating the chains of sarcomeres (see Fig.3.3). The charge motion

generates an electromagnetic field that induces volume conduction, which

enables recording of an electrical signal both internally at the muscle and

externally at the surface over it [8].

38

Fig 3.2 Muscle Fibers Composition [8]

muscle fiber myofibril

sarcomere

Fig 3.3 Generation of Electric Field in Muscle Fibers [8]

A schematic representation of the EMG generation is shown in

Fig.3.4. The symbol mp(t, F), myoelectric signal as a function of time (t) and

the number of firings (F), represents the physiological EMG and it is not

recordable or measured. The detected EMG signal that is utilized in the

research is the observed signal m(t, F) that is contaminated with electronic

noise (almost white) and has lost some of the high frequency components

due to the filtering effects at the electrodes [8].

To conclude, considering the EMG signal as a time varying stochastic

process gives the possibility to model it as a zero-mean Gaussian

distribution, because EMG is the sum of a large number of MUAPs. This

random character of the EMG signal enables the later described

39

approximation of EMG amplitude as the square root of the detected signal’s

variance. In addition, the recorded EMG signal is dependent on the type,

geometry, and position of the recording electrodes. The depolarization wave

also causes chemical changes that result in a mechanical twitch, which is

slower than the electrical response, and delayed by (50-100) msec [39].

Dirac Delta

Impulse Trains Motoneuron (firing)

Motor Unit Action Potentials Trains

Physiological EMG Signal

System Noise Electrode and

Recording Equipment Observed EMG

Signal

∑

h1(t)

h2(t)

hi(t)

hp(t)

mp (t,F)

n ( t )

r (t)

m (t,F)

SP INAL

CORD

Detection Site

Fig 3.4. EMG Signal Origin Block Diagram [8]

3.3.2 EMG Characteristics

A motor unit action potential, or MUAPs, is a summated action

potential as detected from all the muscle fibers in the same motor unit. It is

40

the summation of all the MFAPs produced by fibers of the MU. The shape

and characteristics of a MUAP are shown in Fig.3.5. It will cover the

following characteristics: Duration / Amplitude / Area / Area-Amplitude, the

Area to Amplitude Ratio (AAR) / Size Index Firing Rate / Firing Rate per

Motor Unit (FR/MU) / # of Phases / % Polyphasic MUAPs / # of Turns [4].

Duration and amplitude characteristics are considered in this work because

of their importance in EMG signal identification and classification.

Turns

Baseline crossing

Satellite

Phase

Amplitude

Rise time

Duration

Fig 3.5 Characteristics of a EMG signal [4]

Amplitude:

Amplitude is a more variable characteristic than duration, since it

depends strongly on the position of the needle electrode. DQEMG reports

the peak to peak voltage, which is a standard measure, though some other

labs use the voltage difference between the largest negative peak and the

baseline for EMG studies. In clinical conditions physicians currently assess

41

amplitude qualitatively by viewing it on an oscilloscope-like display and

comparing the height of the signal with other signals in their memory. They

can do this partly because they usually maintain the same visual sensitivity,

or vertical scale, on their monitor [40].

Amplitude has a log normal distribution within an individual, the

range of which varies with disease classification, the amplitude is

consistently reduced or normal (5 to 850 mV) in myopathic cases, but can

vary between reduced and very high for neuropathic cases.

Polymyositis Normal

Num

bero

fMU

APS

Time (msec)

0

10

20

26

28 0 28 1414

Fig 3.6 Histograms of MUAP duration in normal and myopathic patients [4]

Amplitude also has correlations with other characteristics that

may make it valuable to graph it on a two dimensional scatter plot with

another characteristic. Amplitude and Duration are positively correlated.

Amplitude and AAR are negatively correlated which leads to more separable

data distributions when the two are plotted together [41].

42

3.4 The Virtual Reality Axiom

A virtual reality is here considered to be a reality created by

information processing. By definition it does not exist independently in and

of itself, as it depends upon processing to exist. If the processing stops then

the virtual reality must also cease to exist. In contrast an objective reality

simply is, and does not need anything else to sustain it. This suggests two

hypotheses about our reality:

1. The objective reality (OR) hypothesis: That our physical reality is an

objective reality that exists in and of itself, and being self-contained needs

nothing outside of itself to explain it [42].

2. The VR hypothesis: That our physical reality is a virtual reality that

depends upon information processing to exist, which processing must occur

outside of itself. Whatever one’s personal opinion, these views clearly

contradict. If a world is an objective reality it cannot be virtual, and if it is a

virtual reality it cannot be objective. These hypothesis are mutually

exclusive, and each has implications, e.g. objective reality suggests the

universe as a whole is permanent as it has nowhere to come from or go to.

For further details, the reader may refer to ref [43 & 44].

3.5 Virtual Reality Requirements

What would be necessary to create a VR that behaves like our world?

First one must assume information processing constancy, that the properties

of information processing are constant, e.g. information processing in our

world involves discrete data and calculable algorithms. It is reasonable to

assume that a virtual reality’s processing works the same way. The

requirements include [18]:

43

1. Finite processing allocations. That the processing that creates a VR that

behaves like our world allocates it’s processing in finite amounts. Apart

from the fact that we have no concept of what “infinite” processing means,

finite processing allocation suggests that every quanta of matter, time,

energy and space has a finite information capacity: “…recent observations

favor cosmological models in which there are fundamental upper bounds on

both the information content and information processing rate.”. While the

processing power needed to run a universe is enormous it is not

inconceivable[45].

2. Autonomy. Once started, a VR that behaves like our world must run itself

without further information input. Most human computer simulations require

regular data input to run. In a virtual world that behaves like ours, such

external data input would constitute a “miracle”, and in our world miracles

are at best rare. This VR simulation must run itself without miracles, i.e.

without ongoing data input.

3. Consistent self-registration. A VR that behaves like our world must

register itself consistently to internal “observers”. Most human computer

simulations output data to an outside viewer, but we see our world from

within. We register “reality” when light from the world interacts with our

eyes (also in the world). For a virtual reality to “register itself” internal

interactions must be consistent with respect to each local “observer”.

4. Calculability. A VR that behaves like our world must at all times be

calculable. If processing is allocated in finite amounts, local calculations

must not tend to infinity. While many mathematical calculations can do just

this, a calculable VR is guaranteed to avoid that, these requirements

constrain any VR model of our world [44].

44

3.6 VRML Coordinate System

The coordinate system used in VRML is the right-handed Cartesian

coordinate system. The VRML coordinate system is different from the

MATLAB, Aerospace Blockset, and SimMechanics coordinate systems.

VRML uses the world coordinate system in which the y-axis points upward

and the z-axis places objects nearer or farther from the front of the screen. It

is important to realize this fact in situations involving the interaction of these

different coordinate systems [46]. From Fig.3.7, it can be seen that the

coordinate in MATLAB ( Xm=Xv , Ym=-Zv , Zm=Yv ), see equation(3.1).

Fig.3.7: (a). MATLAB graphics coordinate system. (b) VRML coordinate

system.

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡−=

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡−=

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡−=

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

v

v

v

v

v

v

v

v

v

m

m

m

YZ

X

ZYX

ZYX

ZYX

010100

001

)2/cos()2/sin(0)2/sin()2/cos(0

001

ππππ (3.1)

Rotation angles in VRML are defined using the right-hand rule.

Imagine your right hand holding an axis while your thumb points in the

direction of the axis towards its positive end. Your four remaining fingers

45

point in a counter-clockwise direction. This counter-clockwise direction is

the positive rotation angle of an object moving around that axis , this is

shown in Fig 3.8.

In the hierarchical structure of a VRML file, the position and

orientation of child objects are specified relative to the parent object. The

parent object has its local coordinate space defined by its own position and

orientation. Moving the parent object also moves the child objects relative to

the parent object. The measurement units used in VRML in the lengths and

distances are measured in meters, and all angles are measured in radians

[46].

Fig 3.8 Direction of Rotation [47]

3.7 Virtual Human Body

The idea of a virtual atlas for the human body is not new and is possible

to find different projects based on several image or model databases. Many

of them provide an interactive way of visualization of the structures and

some are available over the Internet, some time present interactive models

but without detailed structures description, or 3D atlas which describe all

structures visualized. Other works intend to offer a detailed library of 2D

images and present explanations of specific body regions and traditional 2D

46

human that can offer video animations explaining the organs which explain

in two skeleton human shape and muscles human structure, see Fig3.9 [48].

Mixed reality human

Real environment (RE) and virtual environment (VE) are at two sides,

Mixed reality (MR) is in the middle, Augmented reality (AR) is near to the

real environment side. Data created by the computer can augment real

environment and enhance user’s comprehension about environment.

Augmented Virtuality (AV) is a term created by Milgram (see Fig.3.10). It

means add RE images to VE, such as add texture mapping video on virtual

objects. This term can increase virtual object’s reality degree, decrease

virtual object and real object’s differences. But VE is entirely virtual. In AR

real object, virtual object and user environment must be seamlessly

integrated together[49] .

Mixed Reality Humans ( MRHs ) are new type of embodied agent that

affords touch-driven communication. By affording touch-driven

communication between a human and an embodied agent, MRHs are able to

simulate interpersonal scenarios in which touch is a critical component of

effective communication

Interpersonal simulation allows users to practice their interpersonal

skills by communicating with a VH. In current interpersonal simulators, the

communication between a user and the VH takes the form of a conversation

consisting of bidirectional speech and simple gestures. Use of VHs and

interpersonal simulation is rapidly expanding to educate users in medical

domains.

In this work, MR is used to present different models for human arm,

with its principle explanation. Some of these models are presented as a

47

whole human body to apply the arm movements as a part from the body.

This is done by connection VR with Simulink\MATLAB.

Fig 3.9 Structure of human skeleton and muscles [ 25].

Real Virtual Environment

Fig 3.10 Milgram’s Reality-Virtuality Continuum [49]

Environment

Augmented Reality

(RE) (AR)

AugmentedVirtuality

Mixed reality

(VE)(AV)

48

Chapter Four

Proposed Algorithm

and Structures for

Virtual Human Arm

Movements

Chapter Four

Proposed Algorithm and Structures for Virtual

Human Arm Movements

4.1 Introduction

This work concerns with the implementation of the real human arm

movements by using the EMG signals and it presented with mixed virtual

reality environment. The proposed system combines various

methodologies utilized in previous studies and creates a method for

manipulating the human arm in a “real” manner through three joints.

Proposed algorithm and structure are build to simulate this system with

all real constrains.

This chapter consists of four main parts. The first part explains the

proposed algorithm for solving the IKP of human arm by analytical

solution. The second part presents the simulator of generating EMG

signal for human arm and the measurement of real EMG signal of human

arm and its simulation. The third part presents the proposed structures for

classification EMG signal and recognition of human arm movements by

using FRBP-NN, while the fourth part explains different designs of

human arm in virtual reality environment according to mixed reality fact.

4.2 Proposed Algorithm of Human Arm Inverse Kinematics

The anatomy of limbs and their joints is indeed very complex, (as

evidenced by the debate in the literature on the correct method for

modeling joint motion ). A combination of single degree- of-freedom

joints such a s a revolute joint can be employed (e.g., an elbow joint can

be represented as a revolute joint, while the wrist and shoulder joints can

be represented by three intersecting revolute joints). For example, if the

49

resultant motion is rotational, the joint will be modeled as a revolute joint.

The effect of a spherical joint is modeled as three revolute joints whose

axes intersect at the centre of the sphere. Indeed , all anatomical joints

can be modeled using basic kinematics pairs. In this work human arm

represented by three joints and two links with 7-DOF as shown in chapter

two( see Fig 2.9).

The proposed algorithm (see Fig.4.3) for solving IKP with analytical

solution depends on real human arm movements, real joints constrain and

the algebraic trigonometric relation to solve equation of human arm

model analytically. The algorithm consists of:

Step one: Calculate D-H parameters and all initialization

Consider the human arm shown in Fig 2.9 and modeled as a total of

7-DOF, where the wrist and shoulder joints are represented by three

intersecting revolute joints. From the D-H parameters one can get the

seven transformation matrices which transfer the movements from joint

to another and they are denoted by (Ai) where (i=1,…,7), the detail of

calculation of (Ai) is explain in chapter two. The real constrain of motion

for human arm joints are taken from ref[50] and by private

communication with the consultations in Baghdad Medical City, as

follows:

0

;99;33;65

12

;22;32

811;22

7

654

321

≤≤−

≤≤−≤≤−≤≤

≤≤−≤≤−≤≤−

θπ

πθππθππθπ

π π π π π πθ θ θ

The initialization of human arm model had been done, firstly by

setting the shoulder joint as origin point and then define the position and

orientation of target(end-effector), finally set initial value for all joints

angle and position and orientation of pointer (hand of arm).

50

Step two: determine the lengths ( Ltg ,La ,Lb )

The calculation of these lengths ( Ltg ,La ,Lb ) is very important,

where it represents the key for solving angle of elbow as will be

explained in the next step. The first length represents the distance from

shoulder ( origin point ) to wrist, it can be calculated by the norm of

vector tg:

)( gt tnormLg= (4.1)

The other two lengths are (La and Lb) have fixed values. It can be

taken from D-H parameters table where (La=a3 and Lb=a4) see Fig 4.1.

step three: calculate the angle of elbow (θ4)

Next, the angle of elbow joint (θ4) can be calculated by:

⎟⎟⎠

⎞⎜⎜⎝

⎛ −+= −

ba

gba

LLLtLL

2cos

2221

4 mπθ (4.2)

Only the negative sign in equation (4.2) will be considered due to the

actual constrain of the elbow angle, i.e.:

⎟⎟⎠

⎞⎜⎜⎝

⎛ −+−= −

ba

gba

LLLtLL

2cos

2221

4 πθ

51

La Lb

gtL

W

θ4

Envelop of elbow

π/12

5π/6

Fig 4.1 Form of Human Arm in Elbow Joint

Step four: determine the form of equation of shoulder joint

It is well known the position of wrist (which represent the position

of end-effector ) depends only upon the first four joints and this fact can

be written in the equation(4.3) :

gtAAAA =

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡

1000

4321 (4.3)

The first three matrices ( A1, A2 and A3) represents the

transformation of shoulder to elbow while A4 represents the

transformation from elbow to wrist. The general form of equation(4.3) is

shown in appendix A, and its analytical solution is complex and not

available in the literature. However, the work reported in this thesis

presents an analytical solution using an algorithm based on equation

(4.2), i.e. computing (θ4) first.

Step five: checking the humanely form of arm

In this step the human arm for a given D-H parameters based on 7-

DOF set of axes in space. This description of the arm is sufficient to

describe the jobs that the arm have to do. The validity of the solution of

52

equation (4.3) will be based on its unique solution as obtained by

comparing its two sides.

The next step is to determine the angles of the shoulder. If

equation(4.3) has no solution, this means it represents a manipulator with

7-DOF, and it can be solved by using the methods given in

references[14,15,16,17]. Usually numerical methods are used if and only

if all transformation matrices have no singularity.

Step six: find the shoulder joint angles

The shoulder joint is spherical joint and the solution of the rotation

angles analytically is very complex due to the cross-coupling between

two or three axes; therefore the analytical solution will depend upon two

sets of equations the first set are shown in equation(4.3) and the second

set are shown in equation(4.4).

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡

=

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡−=

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

11000

&; 321

21

2

21

z

y

x

a

a

a

z

y

x

eee

AAAccLsLcsL

eee

(4.4)

The position of elbow ( ex , ey , ez ) in equation(4.4) must be

determined in order to use them for solving the joint angles. From Fig4.2,

it can be seen that the position of elbow is represented by a circle with

center o and radius R.

s w

e

La Lb

RH

LH H

o R

tg d1

Fig 4.2 Elbow Position Control

53

start

Fig 4.3 The Proposed Algorithm for Inverse Kinematic Fig 4.3 The Proposed Algorithm for Inverse Kinematic

by Analytical Solution by Analytical Solution

Find the solution for θ1 θ2 θ3

Find the solution for θ1 θ2 θ3

Find the form In Eq.(4.7)

Find the solution for θ5 θ6 θ7

Calculate La, Lb & Ltg

Calculate θ4 Eq.(4.2)

Apply Eq.(4.3)

No

is there

analytical solution

for Eq.(4.3)

Yes

end

Find D-H Parameters and initialization

all variables

Find the solution as manipulator

Yes Is there

Singularity of matrices

A1---A4

No solution No

54

By comparing equations(4.3) & (4.4), and geometric relations in

Fig.4.3, the following equations are determined:

⎪⎪

⎭

⎪⎪

⎬

⎫

++=

−=

++=

−=

21211

22

2121134

2234

cstsstcte

stcte

cstsstctcsL

stctcsL

zxy

zx

zxy

zx

gggy

ggx

gggb

ggb

(4.5)

From the above equations the relation of shoulder joint angles can be obtained as:

⎪⎪⎪⎪⎪⎪⎪⎪

⎭

⎪⎪⎪⎪⎪⎪⎪⎪

⎬

⎫

⎟⎟

⎠

⎞

⎜⎜

⎝

⎛

+

++−=

⎟⎟⎟⎟

⎠

⎞

⎜⎜⎜⎜

⎝

⎛

=

⎪⎪⎩

⎪⎪⎨

⎧≠

−

=

⎟⎟⎠

⎞⎜⎜⎝

⎛

−=

−

−

−

yx

zzx

gg

ggg

a

z

a

x

a

y

y

x

tstctstcctcs

Le

s

otherwiseLs

e

cLce

s

ee

11

2212113

212

1

11

2

11

tan

tan

0

tan

θ

θ

θ

(4.6)

Step seven: solving for the wrist angles

Once the values of θ1, θ2, θ3 , and θ4 have been calculated the wrist

angles can be isolated by rearranging of the following equation:

( )

⎪⎪

⎭

⎪⎪

⎬

⎫

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡=

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

++−−

+−+=

===

−

−−

333231

232221

131211

657657576575

67676

657657576575

765

14321765

;efefefefefefefefef

Rcccscssssccssccsccscssscssscc

RRR

RRRRRRRRRR

effectorend

effectorendgw

(4.7)

55

There are two values for θ6 that can be found from equation(4.7) which

are:

⎪⎩

⎪⎨⎧

−+−

−=

−

−

))(sin(

)(sin

231

231

6

ef

ef

πθ (4.8)

According to the value of cosine of θ6 can be find the values of θ5 and θ6

are:

⎪⎪⎭

⎪⎪⎬

⎫

⎪⎪⎩

⎪⎪⎨

⎧

=

⎟⎟⎠

⎞⎜⎜⎝

⎛ −=

→=

⎪⎪⎪⎪

⎭

⎪⎪⎪⎪

⎬

⎫

⎪⎪⎪⎪

⎩

⎪⎪⎪⎪

⎨

⎧

⎟⎟⎟

⎠

⎞

⎜⎜⎜

⎝

⎛

=

⎟⎟⎟

⎠

⎞

⎜⎜⎜

⎝

⎛

=

→≠

−

−

−

0

tan0;

tan

tan

0

7

11

3115

6

6

22

6

211

7

6

33

6

131

5

6

θ

θ

θ

θ

efef

celse

cef

cef

cef

cef

cif (4.9)

Now all angles ( θ1,…, θ7 ) are calculated, therefore the solution can

be checked by calculated the forward kinematic and then calculate the

error between the results and the target values to determined the accuracy

of solution, this is done by equation(4.10).

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

−−−

=⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

=⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

=

−

−

−

−

−

−

forwardzz

forwardyy

forwardxx

forwardz

forwardy

forwardx

gg

gg

gg

z

y

x

forward

g

g

g

forward

tttttt

errerrerr

Gttt

AAAAAAAG

)3:1,4(

7654321

(4.10)

56

4.3 Human Arm EMG Processing

It is imperative that the raw EMG signal can be monitored in real-

time, as it is recorded and as the electrodes are placed on the subject.

Ideally, this monitoring must be performed as the EMG signal is recorded

as it is often difficult to differentiate between signal and noise if any

processing has been done to the EMG signal. One disadvantage of using

some computerized collection systems is that many do not provide the

ability to see a raw EMG signal in real time, Fig 4.4, shows an

unprocessed EMG signal[51].

Window of EMG signal for Biceps muscle

Time (sec) Am

plitu

de (m

V)

Fig 4.4 Real EMG Signal [simulated by EMGlab]

A novice electromyographer may have some trouble in

determining if any problems exist in the EMG signal. However, there are

several items that can be quickly spotted a wavering base line is a

common indication that low frequency movement artifacts are present.

Large, individual spikes can be also indicative of motion of the pickup

electrode on the skin surface. Other things to look for are common signals

across all channels (possibly a poor ground reference electrode) and/or an

underlying 50 or 60 Hz line power signal superimposed on the signal

[51].

Filtering the EMG signal

There are two basic filters that can be applied to EMG signals :

high-pass and low-pass. Other filters such as notch and band-pass filters

are just combinations of these two basic filters. Fig.4.5, shows zoom in of

window for EMG signal for Biceps EMG signal about time(0-0.45 msec).

57

Zoom in EMG signal for Biceps muscle

Time (sec)

Am

plitu

de (m

V)

Fig.4.5. EMG Signal Without Filtering.

Actual EMG data has frequency 10-15 Hz or higher, depending on

the activity (10 Hz for normal walking and 15 Hz for more rapid

movements). Clinical EMG data is usually low-pass filtered at 300-600

Hz for surface EMG, or 1,000 Hz or higher for fine wire EMG recordings

because of including the measuring environment. The choice of whether

to filter the data or not, and the filter points to use, depends partly on the

quality of the raw data and partly on the intended use of the processed

data [51].

EMG data for clinical motion analysis use tend to be more heavily

filtered partly to remove motion and other artifacts that may be

unavoidable in clinical subjects, and partly because the timing of muscle

activity is (in most cases) more significant than the fine details of the

content of the EMG activity. Fig.4.6, display the affect of low pass

(625,1250)Hz and high pass (2500Hz) filters on EMG signal.

Filtered with low pass freq.(1250Hz)

Filtered with low pass freq.(625Hz)

Filtered with high pass freq.(2500Hz) Am

plitu

de (m

V)

Time (sec) Fig 4.6 Filtered EMG Signal (for the signal shown in Fig.4.5)

58

Amplitude Normalization

The timing of muscle activity can be easily determined from the

raw EMG. The onset and cessation of EMG activity can then be

correlated to the stance and swing periods of gait. The difference in the

EMG amplitude of a single muscle represents varying levels of activity.

As more muscle strength is required, additional motor units are added,

thus visually the EMG signal has larger amplitude and becomes denser.

Normalization of the EMG signal amplitude is required when

attempting to compare the signals from different muscles. One simple

method of amplitude normalization is to normalize each of the displayed

EMG channels to the maximum of the signal throughout the current trial

within the channel. Thus, the muscle effort can then be displayed as a

simple percent of the maximum achieved by that muscle in the current

trial. This is a simple method that works very reliably for most common

EMG signals. This type of EMG signal normalization is called Maximum

Amplitude Normalization is considered in this work by equation(4.11).

( ) ( ) ;45.0;)(max*)(max

)()(:1:1

=+

===

nNknNk

n KksKks

ksks (4.11)

Where: : represents the normalized signal. : represents the gain for

tolerance of the signal normalization, obtained by trial and error.

ns nK

An alternative normalization method is to make an individual

recording from each muscle, while the subject is attempting to generate a

maximum contraction for the individual muscle (or muscle group). Each

EMG channel is then normalized to the maximum amplitude of the

recorded EMG signal during the test. This method is more complex and

time consuming than the simpler Amplitude Normalization method but is

considered to provide a more accurate comparison of the recorded EMG

59

signal. The type of EMG signal normalization is called Maximum

Voluntary Contraction Normalization – often abbreviated to MVC[8].

4.4 Simulation of Generated EMG Signal

The best modeling of clinical EMG signals was achieved in

algorithm by Hamilton at 2005[9] .This algorithm is simulated by using

Matlab software and using the GUI approach to get full mathematical

simulated model for generating real EMG signal of a specific human

muscle as shown in Fig4.7 [52]. This simulator has many options used

with rearrangement to generate EMG signal for human arm muscle. The

option of the simulator after rearrangement can be summarized as:

Muscle: This popup shows the muscle being simulated. One can select

from the list ( which adding for human arm muscles), of already defined

muscles or select "Custom." to define a new one. Clicking the edit button

allows one to modify the muscle parameters. Helping for specification of

human arm muscles is added to this window to help the user for

generation EMG data more nearest to clinical data, Fig 4.8 shows the

window for this option.

Fig 4.7 The EMG Signal Simulator

60

Fig 4.8 Window for Select or Design Human Arm Muscle

Electrode: This popup shows the electrode being simulated. The user can

select from the list of already defined electrodes or select "Custom..." to

define a new one. Clicking the edit button allows the user to modify the

electrode parameters. The "Electrode" panel allows the user to save new

or modified sets of electrode parameters. Helping for types of electrode,

advantages and disadvantages for each type is added to the window to

help the user for using the electrodes.

Selecting "Multiple Electrodes" allows the user to simulate simultaneous

recordings from more than one electrode. Select "Add" from the number

popup to add a new electrode. Each electrode can be of a different type. If

the user specify multiple electrodes, then the program creates separate

data files for each electrode. The signal from the first electrode is still

named filename.dat, and the signals from the other electrodes are named

filenameI.dat, where "I" is the electrode number. Fig 4.9 shows the

window for this option.

61

Fig 4.9 Window for Select the Type of Electrode

Advanced: This allows the user to specify some advanced simulation

preferences. Include all units: This causes the firing patterns of all the

active motor units to be included in the annotation file, not just the ones

closest to the electrode.

Output directory: Specifies the directory in which to write the data files.

The default directory is the data subdirectory in the simulator directory.

Output root filename: Specifies the root filename for the output files.

Signal duration: Specifies the length of each signal, in seconds.

Contraction: Selects a contraction. To add additional contractions select

"Add" from the popup.

%MVC: Specifies the contractile level for the selected contraction.

Position: Specifies the x, y, and z electrode locations (in mm) for the

selected contraction. The z coordinate is the distance from the muscle

endplate along the muscle axis. Use the "electrode" popup to specify

62

locations for multiple electrodes. Note that you can specify different

locations for each electrode in each contraction.

Delete: Deletes the current contraction.

Run Simulation. Runs the specified set of simulations (one simulation

per electrode per contraction). The simulations can take a fair amount of

time, depending on the specified signal duration. The trace statements

from the simulation routines are displayed in the command window.

Cancel. Quits the simulator and returns to Matlab.

After press the “ Run Simulation “ button , each simulation may take a

minute or more. The program creates the following files for each

contraction:

Filename.dat →→ EMG signal.

Filename.hea →→ header file ( allows signals to be read by another

software).

Filename.eaf. →→ annotation file

These file can be use to analysis the EMG signal.

4.5 Simulation of Generated EMG and Measured EMG Signals

The program, which explains the graphic of the files obtained from

the EMG simulator, is called EMGlab. This program is build in Matlab

software and the description of run EMGlab in Matlab command window

as follows [53].

The program runs in a single Matlab window, which is divided into

five panels: At the top is the signal panel, which displays a segment of the

EMG signal. Below that is the template panel, which displays the MUAP

templates. Below that on the left is the firing panel, which displays the

firing patterns of the identified MUs. To the right is the close-up panel,

which displays a section of the EMG signal at an expanded scale. At the

very bottom is the navigation panel which displays a thumbnail of the

63

EMG signal. The buttons on the edges of the panels are used to change

the display characteristics[54].

Fig.4.10 shows the output of EMGlab with data of EMG signal. It

receive the data that are generated by EMG simulator and the practical

measured data and explain the decomposition of the signal and register

the data of this signal. The inserted symbols in the window are:

+, - zoom in or out vertically

<, > scroll left or right, and ||, | | zoom in or out horizontally

Other buttons on the firing and close-up panels are described below.

At the top of the window are pull-down menus for selecting the channel

and high-pass filtering.

Decomposition for each peak

Signal and its decomposition

Zoom at specific place

Fig 4.10 The Output of EMGlab for EMG Signal

64

4.6 EMG Recognition System

The recognition system of EMG patterns consists of three stages.

The first, time series data for EMG is measured by electrodes in the input

part or generation by EMG simulator and then processing will be applied

to this signal such as filtering, amplification and amplitude normalization.

Fig4.11 summaries these processes.

Measured EMG

EMG Generator ( Simulator )OR

Filtering of EMG

Amplification of EMG

Amplitude normalization of

EMG

Collection data of EMG 1 I

1

Fig 4.11 Flowchart for Processing EMG Signal and Identification of NN

o

Target EMG

for learning

Learning algorithm

Signal processing part

Identification part

65

There are needle type electrodes and surface type electrodes to

measure EMG signal . The needle (insertion)-type electrode can measure

EMG from a specific muscle because of sticking muscle directly. But, it

is not preferable for the subject and in danger of sick contagion. As

compared with this, the surface-type electrode is preferable for the

subject and not in danger of sick contagion and pain. On the other hand, it

is difficult to use in practice.

The second stage is identification on FRBP NN (see Fig 4.12) by EMG

signal, it is done by parallel form of identify NN, where the data of EMG

arrangement by set with diminution (P×I). The length of I represents the

number inputs to NN, which denote number of muscles that share the

movement of a human arm. The numbers of muscles for all real

movements about 22 muscles. The length of P represents the number of

samples used in learning the NN [55].

n

m

I

Fig 4.12 FRBP Network with I inputs, one hidden layer of(m) unit and

(n) outputs [55]

The rule used for forward NN is delta rule and then backward by fast

momentum back-propagation algorithm, by using the error between the

output of NN and the data of raw EMG, this process was explained in

previous section and it is shown in Fig.4.11.

66

The Third stage is test recognition system, that is done by taking new raw

EMG signal and recollecting this data using the same procedure in signal

processing part as shown in Fig.4.11. After that, setting the NN by biases

and weight to identify NN in second stage above, enter the new data to

forward NN. The output of the recognition NN is compared with output

of the identify NN, the difference between them is entered to check for

amplitude in order to decide human arm muscle, where the identification

of NN is done by all muscles of human arm. This procedure is

summarized by Fig 4.13.

Reproc-essing

of EMG data

same as input part

n

m

I

Data Base of NN identifier

The decision of movement of HA according to action

of certain muscle

Forward NN ( without Learning )

EMG i/p Signal

Fig 4.13 Block Diagram of Neural Network for Recognition Movement

67

4.7 Object Design in Virtual Reality

The requirements for design in VRML are explained in chapter

three. In HVR there are many designs and every one is related to the

function of design. In this work we are interested in the design of an

object to implement real movements of human arm.

In the real human arm there are about 10 main movements and about

unknown coupling joints movements. The biology of these movement

and its kinematics was explained in chapter two. The real control and

drive for these movements were explained in chapter four. The

implementation of all this work can be done either as a practice object or

in VA approach.

This section presents the design of real human arm to connect with

another program to run it. Before starts, the explanation of procedure for

design in VRML will presents.

4.7.1 Object Structure Design in VRML

The design in VRML depends on the information of designer and his

imaging for object. There are two choices for design in VR, the first is the

standard configuration such as sphere, cone, cylinder,…etc and the

second choice is free design by select indexed face set button to get many

configuration with free rearrangement of points; therefore every real form

design in the second choice.

Fig.4.14, explain the design of six main parts of real skeleton human

arm. The procedure for design has two stages, it starts with building these

parts every one alone and check the shape similar to related real human

arm part. These bones of human arm cannot be simulated in VR when

the standard shape in VR library is used, where the bones are not uniform

shape; therefore the design was achieved by using indexed face set in VR.

68

The sequences of design for each part of human arm by using indexed

face set in VR is very difficult, where the using of this choice for design

must be controlled for all points in form. Fig.4.15 shows the design of

scapula and clavicle bones in same form, there are many points in this

form every point has proper position to get true final shape .

The second stage in the design is very important, where the

connection between all parts of design will be achieved to get the final

object after defining its origin point which represents the starting point.

The origin is set at the scapula and clavicle bones then the humerus bone

is connected to the bottom children the scapula and clavicle bones. The

same procedure was followed other parts.

a

humerus

clavicle shoulder joint

elbow joint

radius

ulna

hand wrist joint

scapula

Fig 4.14 Built Virtual Right Human Arm

The three spheres, that are used in Fig.4.15, denoted by

shoulder, elbow and wrist joints, used in design give the ability for object

to implements the real movements such as rotation around axes and twist

and so on.

69

points of design

Fig 4.15 Design Object in Indexed Face Set in VR

4.7.2 Human Body Project in VRML

There are many designs for human body, where every one is built

according to function for implementation of human body utility practice

project. Hence according to required movements of human body there

are two forms of human body design as follows:

4.7.2.1 Skeleton of Human Body

The skeleton human arm can be designed in VR as one unit, this

design is used as dummy skeleton. It can not do any function of human

skeleton such as arm, leg movement. This design is shown in Fig.4.18a,

this object used in VR of carton movie and same application.

The other design is to build the skeleton by collecting each bone

alone and then group them by selecting the origin of design and connect

the bones same as in a real skeleton human body. This design has wide

range of implementation in medical, education, and cinema applications.

The using of real skeleton in these application is very difficult and the

70

similarity of the design with real skeleton gives it high reliability in

practical implementation, (see Fig.4.16b.).

Fig 4.16a Design Skeleton Human Body as One Unit

Fig 4.16b Design Skeleton Human Body

71

4.7.2.2 Complete Human Body

The object built is by the same methods used in human skeleton as

one unit or same parts for real human body, as shown in Fig4.17. The

importance of this design is that it can recognize the sex of the human

from the design by adding the final form that classifies the human to

male, female, girl, boy , old man and old woman. Also, the human can be

classified according to his job by adding cloths proper to a job such as

teacher, solider,….etc.

Fig 4.17 Human Body for Girl and Boy [56]

72

Chapter Five

Simulation Results and Discussion

Chapter Five

Simulation Results and Discussion

5.1 Introduction

This chapter presents a proposed GUI package, which includes the

simulation of IKP, EMG simulation and its processing, and finally human

arm virtual model implementation. This package was built using

MATLAB Ver.R2009a package.

5.2 Proposed Package of a Complete Virtual Human Arm Driven by

EMG Signal

This package presents the overall system design simulation and it

contains many windows, each one has some related function. The

presentation of the results depends on the sequence of running the

windows and display them by tables, curves and movies.

The starting window of GUI is shown in Fig 5.1. It explains the

overall idea of the work. Button Next is used to transfer to the next

window shown in Fig 5.2.

Fig 5.1 Starting Window in GUI Simulation System

73

Fig 5.2 represents the system window with three main parts. The

help of each part can be executed by selecting its button. Fig 5.3(a,b,c)

presents the help windows of the specified subject. Sample of help frames

are given in appendix B.

Fig 5.2 The Structure of the Simulated System

-b-

-c-

-a-

Fig 5.3.a,b,c The Helping Windows in GUI System

Moreover, Fig 5.2 includes System Run button, where complete

simulation may be started.

74

5.3 Measurement of Real EMG Signal

Fig 5.4 represents the EMG signal window, where the EMG signal

has two main sources, either measurement of real EMG or generated by

using EMG simulator. Each one has advantages and disadvantages from

the point of view of accuracy and reality.

Fig 5.4 GUI of EMG signal in Recognition System

Fig 5.5 represents the real measurement system of EMG signal.

Locally (i.e. in Iraq) there are many problems associated with the

acquisition of real EMG measurement. The systems in the educational

hospitals in AL- Kadhimiyaa , AL Yarmook, Baghdad Medical City, and

some private Clinical centers do not justify this research requirements.

Fig 5.5 Real Measuring System

Processing and monitoring stage

sensors stage amplifier stage

75

Actually there are many sensor types (electrodes) of EMG signal,

but locally there is only a needle electrode type. The usual procedure for

using this type is by inserting the needle in the proper placement of

muscle to touch the fiber and sense the EMG signal. Fig 5.6 represents

sample photos for the real EMG signal measurement by needle electrode

at Baghdad Medical City hospital.

Fig 5.6 Real Measurement EMG signal by Using Needle Electrode

Measurement of real EMG signal by needle electrode has many

problems such as high pain to the patient and side effects on muscles.

Needle electrode is used only for up normal muscles.

Human arm movement recognition requires number of surface

channels equal to the number of recognized muscles. For example, four

channels must be available at the same time to recognize the wrist joint

movements.

When selecting the first button in Fig 5.4, real EMG data are

obtained, taking into consideration all the above mentioned technical

problems. The output on the screen of Micromid measuring system in

Baghdad Medical City hospital is shown in Fig 5.7.

76

Fig 5.7 Screen Graph of Real EMG Measurement System for Miled

Resistance on Deltiod Muscle of Right Human Arm

The measured EMG data in Baghdad Medical City hospital are

stored in a file format (filename.TRC). This data can be read and collected

using EMGlab program as explained in chapter four. These measurement

data are taken from every muscle for three cases: no resistance (no

activity of muscle), low resistance (Miled) and high resistance.

EMG simulator is used to overcome the problems of locally real

measuring system ( needle electrode and single channel ). The EMG data,

which is generated by the simulator, depends on three main parameters:

muscle specifications, electrode types and electrode position. In this work

only muscle specifications are taken into consideration, i.e. taking fixed

values for electrode type and position.

There are four muscle parameters which affect the EMG data. The

selected values of these parameters depend upon knowledge of the user

about the muscles. All these parameters represent biological specification

of muscle: number of MUs in muscle, muscle fiber density, muscle fiber

cross section area, and MU diameter ( minimum and maximum values).

77

The degree of reliability of generated EMG data depends on the

closeness of the value to those of the real biological muscle. Fig.5.8

shows the window settings of the specifications of biceps muscle of 20

years old human with a normal body.

The real value of the muscle parameters can be obtained by studying

the anatomy of muscles in detail. Depending on the practical data

obtained from medical table and consultation with the specialist [57], the

muscle parameters adopted are as shown in table 5.2.

Fig 5.8 Setting the Specification of Muscle

Table 5.2 Standard Parameters of Muscle Human Arm

Muscle name

No. of MU

Fiber Density ( per mm2 )

Fiber Area

( mm2 )

Range MU diameter ( mm )

biceps 400 15 0.0075 2 - 10 triceps 350 10 0.0055 2 – 9 deltoid 450 20 0.0085 3 - 11

flexor carpi radial

200 7 0.0030 1.5 - 8

extensor carpi radial

250 8 0.0035 1.5 – 8

flexor carpi ulna

100 5 0.0015 2 – 6

extensor carpi ulna

150 6 0.0025 2 – 6

78

Fig.5.9 represents the generation of EMG signal by EMG

simulator. There are two choices to get the EMG data. The first by EMG

simulator with specified values for the parameters of the muscle. The

results are data files with three types of format: filename.hea,

filename.eaf and filename.dat. The details of the EMG simulator was

explained in chapter four and the GUI window for running EMG

simulator is shown in Fig.5.10.

The second choice in Fig.5.9, is used to produce data of EMG signal

by selecting a specific muscle of the human arm. The data is calculated

by selecting the specification of normal human body as given in table.5.2.

Fig 5.9 Generation Window for EMG Signal

Fig 5.10 Running Window for EMG simulator with Deltiod Muscle

79

Finally the main difference between the two sources of EMG signals

is the reality of the signal where, the first source is the measurement of

real EMG signal direct from muscles considering all real constrains while

the second source obtained by EMG simulator with specific data of

parameters of muscles.

The processing of collected EMG data, was explained in chapter

four. Filtering, normalizing, and then collecting the data is done by the

GUI window shown in Fig 5.11.

Fig 5.11 Window for Processing on EMG Signal

As an example for using EMGlab real data measurements for deltoid

muscle will be used and its results will be compared with the results of

EMGlab by EMG data for deltoid muscle generated by EMG simulator.

Fig.5.12, presents real EMG deltoid signal and the steps of filtering by

low pass filter with cut-off frequency 625Hz, 1250Hz and by high pass

filter with cut-off frequency 2500Hz.

80

- a -

- b -

- c -

- d -

Fig 5.12 Real Deltiod EMG Signal with high resistance a. Unfiltered

signal b. Filtering signal with low pass filter (625Hz) c. Filtering signal

with low pass filter (1250Hz) d. Filtering signal with high pass filter

(2500Hz)

The same procedure is applied to the EMG data obtained by

simulation and the results are shown in Fig 5.13.

81

- a -

- b -

- c -

- d -

Fig 5.13 Deltiod EMG Signal Generated and Its Filtering a. Unfiltered

signal b. Filtering signal with low pass filter (625Hz) c. Filtering signal

with low pass filter (1250Hz) d. Filtering signal with high pass filter

(2500Hz)

By comparing Figs 5.12 & 5.13, it can be noticed that the EMG

simulator has effective ability to generate EMG signal for specific muscle

similar to real data of EMG signal and the ability of EMGlab to collect

82

the signal and filtering the signal by two type of filters. The effect of

using low pass filter and high pass filter is shown in above Fig’s. They

cancel the noise and smooth the signal. As it was mentioned earlier, there

are two parameters that influence the EMG signal estimation, these are

the amplitude and period of the signal.

The amplification of EMG signal ( first choice in Fig5.5 ) is used

with the real measuring system only. The amplitude of signal is

sometimes very low, where this phenomena occurs for many reasons,

some of them are clinical, and the rest is caused by the position of the

EMG sensor. Hence, after measuring this signal it will be amplified by

certain gain in the range of (10 - 10000).

The normalization of EMG signal is needed when it is used with NN

approach to recognize the movements of the human arm. Two method for

normalization were explained in chapter four. For NN approach, which is

used in human arm movement recognition, EMG signal normalization is

needed. Maximum amplitude normalization (equation(4.11)) method was

used in this work. .

5.4 Neural Network Recognition System

Fig 5.14 represents the GUI window for calling NN recognition

system. Clicking the identification button initiates the identification

process. In this design number of samples are chosen to be (1600

samples) and this represents the minimum value with acceptable error for

identification. The number of samples is adjusted by trial and error.

The simulation of the block diagram shown in Fig.4.11 is activated

by using the first button in Fig.5.14. The EMG signals are used with

identification process generated by EMG simulator for main seven

muscles of human arm (biceps, triceps, deltoid, flexor carpi radial,

extensor carpi radial, flexor carpi ulna and extensor carpi ulna ).

83

Mechanical activation of these muscles will cause the corresponding

movements of human arm ( supintion of elbow, flexion of forearm,

abduction of arm at shoulder, flexion of wrist, extension of wrist

,abduction of wrist and adduction of wrist ). Figs 5.15 and 5.16 present

the EMG identification data with the dimension (1600×7). These data are

used as an input to FRBP NN identifier.

Fig 5.14 Window of NN Recognition System

84

Fig 5.15 Selected Samples of Deltoid EMG Signal for Identification

Fig 5.16 Zoom in of 1600 samples from EMG Signal of (a). Triceps

muscle (b). Biceps muscle (c). Extensor Crapi Radail muscle (d).

Extensor Crapi Ulna muscle (e). Flexor Crapi Radail muscle (f). Flexor

Crapi Ulna muscle

0 200 400 600 800 1000 1200 1400 1600-1500-1000

-5000

50010001500200025003000

3500

No. of samples

Amplitude (µV)

- a - - b -

- c - - d -

- e - - f -

85

The recognition of movement under the real complex constrains is

very complex. The recognition design will be implemented with different

procedures depending on the two sources of EMG signal. When selecting

the measurement of real EMG button from Fig 5.4 the processed

window of Fig 5.17 is obtained, while selecting the button for generation

of EMG data by EMG simulator the processed window of Fig 5.9 is

obtained.

In this work the following points give the EMG sources:

a. measurement of real EMG signal from normal arm muscles.

b. measurement of up normal signal from ill arm muscles ( such as

neuropathic and myopathic ).

c. EMG signal generated by EMG simulator with standard

specification of arm muscles(by using biology tables)

d. EMG signal generated by EMG simulator by assuming random

parameters of simulator.

Fig 5.17 GUI of Real EMG Measurement

From the above description of information about EMG (

specifications and measuring methods ), the procedure to recognize

movement can be done by using the following steps:

86

Step one: The identification system has two procedures depending on the

reliability of the measured signal. If the signal measured by sources ( b &

d), it must be classified as EMG signal for human arm or unknown EMG

signal.

The structure used for NN identifier is FRBP-NN with dimensions

(7-30-7), values of learning rate (αr=0.01) and momentum term

(mu=0.015). This is shown in Fig.4.11. The NN identifier has seven

inputs with EMG signals, (shown in Figs 5.15&5.16 ), thirty neurons in

hidden layer and seven outputs. The training of NN was achieved by

(1600) samples with accepted error by mean square equal to (0.0005).

After the learning of network all parameters of NN are stored in order to

use them with NN classifier. The outputs of NN identifier are shown in

Figs.5.18&5.19.

0 200 400 600 800 1000 1200 1400 1600-3000

-2000

-1000

0

1000

2000

3000

No. of samples

Am

plitu

de

Identification of EMG signal for Bicepe Muscle

NN outputEMG Signal

μv

( μv

)

Fig 5.18 Identification process for NN

87

Fig 5.19 Identification of NN for six EMG human arm muscle signals

0 200 400 600 800 1000 1200 1400 1600-1500

-1000

-500

0

500

1000

1500

2000

2500

3000

3500

No. of samples

Am

plitu

de

Identification of EMG signal for Triceps Musclevμ

0 200 400 600 800 1000 1200 1400 1600-1000

-500

0

500

1000

1500

2000

2500

No. of samplrs

Am

plitu

de

Identification of EMG signal for FlexorCrapi Radail Muscle

vμ

3500EMG signalNN output

0 200 400 600 800 1000 1200 1400 1600-1000

-500

0

500

1000

1500

2000

2500

No. of samples

Am

plitu

de

Identification of EMG signal for Extensor Crapi Ulna Muscle

EMG signal.NN output

vμ

0 200 400 600 800 1000 1200 1400 1600-1500

-1000

-500

0

500

1000

1500

2000

2500

3000

3500

No. of samples

Am

plitu

de

Identification of EMG signal for Flexor Crapi Ulna Muscle

EMG signalNN output

vμ

0 200 400 600 800 1000 1200 1400 1600-1000

-500

0

500

1000

1500

2000

2500

3000

No. of samples

Am

plitu

de

Identification of EMG signal for Extensor Crapi Radail Muscle

NN outputEMG signal

μ v

0 200 400 600 800 1000 1200 1400 1600-1500

-1000

-500

0

500

1000

1500

2000

2500

3000

No. of samples

Am

plitu

de

Identification of EMG signal for Deltiodl Muscle

NN outputEMG signal

vμ

( μv

)

( μv

)

( μv

)

( μv

)

( μv

)

μv )

(

88

Step two : Fig 5.20 represents the GUI window of this step. Any tested

signal is applied in the forward direction of the NN in the proposed

structure and its output is compared with NN identifier data base to

classify the tested signal as human arm EMG signal or unknown EMG

signal. The comparison procedure is as follows:

For each sample the accepted amplitude difference between tested

signal and data base is less than ( 2.5% ). If the number of accepted

samples is greater than ( 90% ), the tested signal will be classified as

human arm muscle EMG signal and this result will appear in the

decision label in Fig.5.20.

Fig 5.20 Neural Network for classified EMG signal

Step three : This step will start the operation of NN to recognize the

movements of the human arm depending on the EMG signal. The signal

after steps (1)&(2) may be received from any of the four sources as

explained above. The signal from sources (a)&(c) will enter directly to

this step.

The recognition algorithm is built on two biological facts. The first

one is the amplitude of EMG signal depending on four factors for the

generation of EMG and these parameters differ for any given muscle. The

89

second fact is the activation time of muscles which can never occur

simultaneously. These facts can be seen in Fig 5.21, which shows EMG’s

for the seven muscles during the same period.

0 200 400 600 800 1000 1200 1400 1600-3000

-2000

-1000

0

1000

2000

3000

4000

No. of samples

Fig 5.21 EMG Signals for Seven Muscles

The procedure for recognition starts with the identification of NN

with maximum value of normalized EMG data for seven channels. These

channels are connected to the human arm at the same time. This

environment of measurement can not be found locally. The reasons for

that was explained in the previous section; therefore the EMG used was

obtained by using EMG simulator.

In this step, the NN identifier has the same structure of NN identifier

in step one with learning rate (αr=0.9), momentum term (mu=0.95) and

(500) iteration. Fig 5.22 represents the NN identification results.

Am

plit

max 2

max 1

max 3

max 4

max 5max 6

max 7

( μv

) ud

e

EMG1 EMG2 EMG3 EMG4 EMG5 EMG6 EMG7

90

Fig 5.22 Identification for Normalized Max. Value of Amplitude for

Seven EMG signals

Step four : The finial step of the design for movement recognition system

is achieved by recognizing the normalized maximum amplitude for test

0 50 100 150 200 250 300 350 400 450 5000

0.1

0.2

0.7

0.6

0.5

0.4

0.3

No.of iteration

itude

deltiod muscle

bicepe muscle

tricps muscle

extensor crapi radial muscle

flexor crapi radial muscle

flexor crapi ulna muscle

extensor crapi ulna muscle

Am

pl

0.7

0 5 10 15 20 25 30 35 40 45 500

0.1

0.2

0.3

0.4

0.5

0.6

No.of iteration

extensor crapi ulna muscle

extensor crapi radial muscle

tricps muscle

deltiod muscle

flexor crapi ulna muscle

flexor crapi radial muscle

bicepe muscle

Nor

mal

ized

N

orm

aliz

ed

mpl

itude

A

91

signal by using the same NN in forward direction only. After that the

output of net is compared to the output of NN identifier. The difference

must be less then (0.05%) for any one of the seven values, thus relating it

to human arm muscle. Depending on muscle recognition the human arm

movements may be recognized. Fig 5.23 represents the GUI window of

this step.

either - or

Fig 5.23 GUI for Simulation of Recognition System by EMG Signal

92

5.5 Kinematics of Human Arm

The case under consideration was shown in Fig.2.12, with the D-H

parameter values given in table 5.3:

Table.5.3. Numeric Value for D-H Parameters of Human Arm.

Frame (joint)

θi

(rad)

di

(cm)

aj

(cm)

αj

(rad)1 θ1 0 0 π/2 2 θ2 0 0 π /2 3 θ3 0 20 - π /2 4 θ4 0 25 π /2 5 θ5 0 0 - π/ 2 6 θ6 0 0 π /2 7 θ7 0 0 - π /2

The position of end-effector is assumed to be [29.71 , 13.45 , 23.09]

in cm, with the orientation as (π , π /2 , π /4) in x-axis, y-axis and z-axis

respectively. All initial joint angle values are assumed to be zero. As in

the proposed algorithm explained in section 4.2, and according to

table.5.3: La=20 cm and Lb=25 cm.

After setting of all initial condition in first step, equation(4.1) is

applied to calculate Ltg=39.95 cm. The rotation matrix and position of

end-effector can be calculated as:

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡

−−−−

=−

100009.2300145.137071.07071.0071.297071.07071.00

effectorendG (5.1)

Considering Fig.4.1, and equation(4.2), (θ4) can be calculated

as(θ4=1.5728 rad). Fig 5.24 gives the values of (θ4) with the position of

end-effector in x-axis (0:0.1:2) and zero in (y,z) axes.

93

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

0.5

1

1.5

2

2.5

3

3.5

the

valu

e of

thet

a4 (

rad

)

position of E.F. in x-axis

note: the value of E.F in y & z axes are set to zero

Fig 5.24 Form of (θ4), with 0== gzgy tt and 2:1.0:0=gxt

When applying equation(4.3), to the case under consideration the

following equations are obtained:

)cos(),sin(:

)(

)(

321431321

32432

321431321

iiii

gx

gy

gx

cswhere

tassasscss

tacacc

tascasccsc

θθ ==

=−+−

=−−

=−−−

(5.2)

It is quit clear that the above equations have unique analytical

solution where, the values of shoulder joint rotations ,, 21 θθ and 3θ can be

expressed as:

⎪⎪

⎭

⎪⎪

⎬

⎫

⎟⎟⎠

⎞⎜⎜⎝

⎛ −=

⎟⎟⎠

⎞⎜⎜⎝

⎛

−

−⋅=⎟

⎟⎠

⎞⎜⎜⎝

⎛=

−

−−

gy

gzgx

gxgz

gx

gz

taa

tatatata

tt

3

3412

44

331

13

11

)cos(tan

)tan(cos;tan

θθ

θθθ

(5.3)

94

Substituting the numerical values of the D-H parameters given in

table.5.3 into the set of equation(5.3), the following values for shoulder

joint are obtained:

θ1=0.7854 rad , θ2=-0.6155 rad , θ3=0.4243 rad

To find the value of the wrist joint angles , with Rw as in

equation(4.7) the following is obtained:

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

−−−−−

−−−=

657576575765

67676

657576575765

csccssssccssssccccccssscsscsc

Rw (5.4)

Applying the other form of equation(4.7), Rw may be calculated as:

( )⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

−−−=−

4152.06947.05873.04028.07192.05661.0

8157.00015.05785.01

4321 gRRRRR (5.5)

Consider equations(5.4) and (5.5) for computing matrix Rw , the

wrist joint angles are calculated by comparing both sides, that is:

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

−−−=

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

−−−−−

−−−=

4152.06947.05873.04028.07192.05661.0

8157.00015.05785.0

657576575765

67676

657576575765

csccssssccssssccccccssscsscsc

Rw

.4708.0;)3,1()3,3(

tan 51

5 radRR

w

w =⎟⎟⎠

⎞⎜⎜⎝

⎛ −= − θθ

.4146.0,)1,2(

)2,2(tan 7

17 rad

RR

w

w =⎟⎟⎠

⎞⎜⎜⎝

⎛−

= − θθ

.904.0,)sin(/)3,3(

)3,2(tan 6

5

16 rad

RR

w

w =⎟⎟⎠

⎞⎜⎜⎝

⎛−

= − θθ

θ

In applying equation(4.10) the forward kinematic may be calculated

using the above calculated angles ( θ1,…, θ7 ) as follows:

95

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡

−−−−

==

1000094.23001452.137071.07071.00712.297071.07071.00

7654321 AAAAAAAG forward (5.6)

The errors in position of end-effector are calculated by comparing

equations 5.1 and 5.6, while the orientation values are similar. These

errors are:

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

×

×

×

=

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡

−

−

−

4

4

3

104102

104.2

gz

gy

gx

t

t

t

e

e

e

(5.7)

From the values of error, it can be seen that the accuracy of calculation of

IK is acceptable as in equation(5.7).

Human learning sense test is considered through many repetition of

the above calculation method. When a human tries to touch a known

position and his neural system is familiar with this position; the same

results will be obtained with each trial. The result is shown in Fig 5.25.

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 520

30

40

posit

ion

(cm

)in

x-a

xis

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 50

20

40

posit

ion

(cm

)in

y-a

xis

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 520

30

40

No. of return run.

posit

ion

(cm

)in

z-a

xis

position of E.F o/p model initail position

Fig 5.25 Simulation for IKP by proposed algorithm

96

If the position of end-effector changes without changing the D-H

parameters and initial condition, the result is shown in Fig.5.26. From this

figure it can be seen that the position error will decrease with calculation

repetition similar to real human sense.

The envelope of movements in this model, related to human arm real

constrain is calculated with the following condition:

bagba LLLtLL 2222 ⟩−+ (5.8)

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 530

35

40

posit

ion

(cm

)in

x-a

xis

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 50

20

40

posit

ion

(cm

)in

y-a

xis

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 510

20

30

40

posit

ion

(cm

)in

z-a

xis

position of E.F o/p model initail position of model

No. of return run.

Fig 5.26 Simulation with different values of position of end-effector

The GUI that simulate the kinematics of this design is shown in Fig

5.27. The kinematic system design is shown with two forms. The forward

kinematics and the inverse kinematics are presented by simulink under

MATLAB. The use of simulink is to connect the simulation with VR

design. When the first button is selected, forward kinematics human

arm model will only run the forward part while when the second button is

97

selected forward kinematics human arm model will run all parts of the

model shown in Fig 5.28.

Fig 5.27 GUI system for kinematics and VR for Human Arm

Given end-effector position

Computing end-effector position

Fig 5.28 Simulink Flowchart of Forward and Inverse Kinematics

98

5.6 Human Arm Movements Implementation in VR

The VR technique in this work is used as an environment similar to

real or practical environment. In this design there are three choices to

implement the movements in the VR which are explained next.

5.6.1 Virtual Human Body Model Demo

The design of VR models for human body is explained in chapter

four. The simulation of human body is achieved by connecting the VR

with simulink/ MATLAB, where the commands for movement will be

calculated in simulink and then given to VR model .

The command is calculated by using the real constrain in human

arm joints, and every movement is checked separately. Then all these

data are collected by lookup table in MATLAB workspace. The

simulation of movement will take the data from lookup table by

sequences to implement all real movements for the two human arms

(right and left).

The movements implemented in this design represent the main

movements (14) in human arm such as abduction-adduction , flexion-

extension , and internal-external rotation of the upper arm. The flexion-

extension , and pronation - supination of the forearm, the ulna-radial

deviation ( abduction-adduction of wrist ) , and flexion-extension of the

hand. Fig.5.29, present the simulink design for the system. The output is

shown in Fig.5.30. The details of Fig.5.29 and the residue of the results

are shown in Appendix B.

99

Fig 5.29 Virtual Human Body Model driven by Simulink

0.1 sec 0.2 sec 0.3 sec 0.4 sec

0.5 sec 0.6 sec 0.7 sec 0.8 sec

0.9 sec 0.10 sec 0.11 sec 0.12 sec

Fig 5.30 Simulated Results for Human Body Model

100

5.6.2 Virtual Kinematic Human Arm

The skeleton of a human arm in VR was shown in details in section

(4.7.1). This design will now be simulated and the instruction of

movements achieved by applying a proposed algorithm will be as

explained previously, see Fig 5.31

Fig 5.31 Virtual Human Arm driven by a proposed kinematics algorithm

101

The example for simulation results of this model is shown in

Fig.5.32. From these results, it can be seen that the solution for IKP by

analytical solution has many advantages such as the ability to implement

the system in VR and then can be done in practice with artificial limps.

This movement is implemented according to all real constrains of real

movement of human arm ( the value of joints angle and the real envelop

of movements ).

0.1 sec 0.2 sec 0.3 sec 0.4 sec

0.5 sec 0.6 sec 0.7 sec 0.8 sec

0.9 sec 0.10 sec 0.11 sec 0.12 sec

0.13 sec 0.14 sec 0.15 sec 0.16 sec

0.17 sec 0.18 sec 0.19 sec 0.20 sec

Fig 5.32 Simulation Virtual Skeleton Human Arm

102

5.6.3 Virtual Human Arm driven by EMG Signal

The GUI for simulation of this design is shown in Fig 5.33. The

implementation of movement based EMG signal starts with the

preprocessing on the EMG signal and NN identification for seven EMG

signals of seven different muscles, then classifies the signal, and finally

recognizes the movements. The overall simulation was explained in this

chapter, and it can be summarized in the following steps:

Step one: receives the signal from any sources as shown in Fig 5.4.

Step two: applies the procedure shown in Figs(5.4→5.13) preprocessing

the data and discussing the other types of signal.

Step three: applies the procedure for identification of network and signal

classification, (see Figs(5.14→5.23)).

Fig 5.33 GUI for Simulation of Recognition of Movements of Human

Arm

Now, the final step is the recognition of the human arm movements

which is produced by contraction of the seven muscles. When applying

the first step in Fig.5.33, the recognized signal result is obtained

according to a certain movement and the data for movement or the EMG

103

signal for other muscle in human arm is prepared. The system will show

message the signal is not arm defined muscle. These two results are

explained in GUI shown in Fig.5.34(a&b).

- a - - b -

Fig 5.34 Simulation Results for step one of movements recognition

If the result of step one is the same as in the Fig.5.34b, it can not be

applied in the next step, and another signal must be tested.

The next step is to simulate the instruction to execute virtual human

arm by simulink program, and recall the data obtained in first step from

workspace and then enter to the system shown in Fig.5.29. The period for

running the system is two seconds to execute the order. To explain the

operation of the design the following case study will be considered.

Case Study:

The case under consideration is dealt with by using generated EMG

signal by the simulation of deltoid muscle, which controls the abduction

104

of human arm at the shoulder. After running the EMG simulator with

specification of deltoid muscle we will get the first signal shown in Fig

5.18. The data for this signal are stored with the name ( deltoid_test.mat )

in workspace. This signal will be used in the complete example. The steps

of simulation for this design are presented in details in Fig.5.35(a→h).

From the above figure, it can be seen that the use of NN recognition

based on normalized amplitude EMG signal after classifying the signal as

EMG signal by using FRBP-NN with samples of EMG about (1600)

samples captured from the monitoring of EMG signal is a very powerful

method for moving virtual human arm. From these results it can be said

that using analytical solution has a suitable accuracy in presenting the real

human arm movements.

click click

- a - - b -

105

Double click

click

click

- c - - d -

- e - - f -

Fig 5.37(Continued)

Fig 5.35(a→f) Procedure for running the simulation of Virtual Human

Arm Driven by EMG Signal

106

Chapter Six

Conclusions and

Suggestions for Future

Work

Chapter Six

Conclusions and Suggestions for Future work

6.1 Conclusions

From the obtained results it can be concluded that:

1. The analytical solution of IKP is an effective method to simulate human

arm and it has the ability for adding any real constrain to the model without

any complexity. The proposed algorithm has the ability to solve IKP for

anthropomorphic limbs by analytical solution with high accuracy when

compared with those available in the literature.

2. Using an EMG simulator overcomes the problems of hardware

unavailability in the real EMG measuring system, where the local real

measuring system has only a single channel with needle electrode. The use

of EMGlab gives the facility of obtaining an EMG signal from the given

data either from real measuring system or an EMG simulator.

3. High accuracy NN classifier is used to distinguish between the human arm

muscle EMG signals. Human arm movements recognition system based on

EMG signals using FRBP-NN is a good method from the point of view of its

simplicity and accuracy and it can be implemented in many medical

applications such as the use of artificial limbs and high accuracy robot

applications.

4. The use of mixed reality human ( MRH ) adds more realities to the VR

model to approximate the real sense of human arm movement similar to that

of natural human performance.

107

5. The connection between MATLAB / simulink and VR model gives the

design a facility to calculate the human arm movements by many methods

of simulation through providing a suitable command to VR model. The

relation between the MATLAB and VR offers a VR model to execute all

movements run directly from VR through a generation of virtual model

animation.

6. Design of the proposed package – based GUI technique gives the ability

to connect all three parts under consideration, simulate individual parts by

transportation between them, and finally presents the three parts

simultaneously as one animation system. Moreover, many helps are included

in this package with explanations by notes, graphs and movies enabling this

package to be used as an education tool to teach human arm kinematic, its

EMG signal and VR animation.

6.2 Suggestion For Future Work

1. Applying an optimal control approach in calculation of human arm

posture based EMG signal.

2. Using of augmented reality ( AR ) technique to have more reality for

virtual human arm model.

3. Implementation of the proposed package as assistance with

manufacturing of artificial limbs.

108

References

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[37] H. Reucher, G. Rau, J. Silny, “ Spatail Filtering of Noninvasive Multielectrode EMG:n Part I-Introduction to Measuring Technique and Applications ”, IEEE Transaction on Biomedical Engineering, Vol. 34, no. 2, PP 98-105, February 1987.

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[42] L. Mesin, D. Farina, “ Simulation of Surface EMG Signals Generated by Muscle Tissues with Inhomogeneity Due to Fiber Pinnation ”, IEEE Transaction on Biomedical Engineering Vol. 51, no. 9, PP 1521-1529, September 2004.

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113

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[50] K. Abdel-Malek, W. Yu, “Human Placement for Maximum Dexterity”, Department of Mechanical Engineering The University of Iowa City, last my visit to web in 15/8/2009.: http://www.engineering.uiowa.edu/~amalek/papers/HumandexteritySAE.pdf.

[51] Motion Lab Systems, Inc, “A software user guide for EMG Graphing and EMG Analysis EMG Analysis ”, Updated Thursday, February 26, 2009.

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[57] Private Consultation in Baghdad Medical City.

----------------------------------------------------------- Published papers: [*] M. Z. Al-Faiz, Y. I. Al-Mashhadany, “Analytical Solution for

Anthropomorphic Limbs Model (IK of Human Arm)”, IEEE Symposium on Industrial Electronics and Applications (ISIEA 2009), October 2009, Kuala Lumpur, Malaysia.

[**]M. Z. Al-Faiz, Y. I. Al-Mashhadany, “Human Arm Movements Recognition Based on EMG Signal ”MASAUM Journal Of Basic and Applied Sciences (MJBAS) Volume 1 Issue 2, PP 164-171, (September 2009)

114

Appendices

Appendix A: Details of Classical Analytical Solution[3]. The final form to get elbow joint angle (θ4) can be obtained from the solution of equation(2.12) shown below :

).2()()()(

)22()22(

211122

22

21

21

22211211142112114

dsasdadatgtgtg

addccasdcdacac

zyx αθ

αθθαθθ

−+−+−++=

+⋅++⋅ (A.1)

From the full solution of above equations we get the finial two forms of θ4 (see ref 3):The first form:

)

)cos(2)sin()sin(2

)cos(2

))sin()sin(4))(sin())(sin(4

)sin()sin(4)sin()sin(4))(cos())(cos(4

))(cos(4))(cos(422

2222222

22222222

22222)sin()sin(4

)sin()sin(4)sin()sin(4

)sin()sin(4(24)cos()cos(4

5.0arctan(2

1122211

12222

222

2221

21

21

221122

22

22

121

21132221

31

21

21

22

22

421

21

22

21

22

22

22

222

22

222

22

222

22222

222

222

2222

21

221

221

221

21

21

22

21

22

21

221

221

221

221

21

222211

22

22112

221122

42

442

4

41

412211

2121122

4

⎥⎥⎦

⎤

⎢⎢⎣

⎡

−

+−+−+−−+

⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢

⎣

⎡

+

−−−

+−+++

−+++−++

−−−+++−−

−+++++

−+−−−−

−−−++−

∗=

θαθ

α

αθαθ

αθαθθα

θα

αθ

αθαθ

αθθα

θ

aada

dddtgatgtgda

datgda

addaad

tgaadddtgda

tgaatgdtgtgtgatgdtgtgtg

tgtgddtgdtgdtgddaaada

tgatgatgatgadadad

datgdaadtgatg

dadatgdaad

zxy

y

yz

zxxzxyyzy

xyzyx

zxyx

zzx

x

(A.2)

Or the second form:

A-1

)

)cos(2)sin()sin(2

)cos(2

))sin()sin(4))(sin())(sin(4

)sin()sin(4)sin()sin(4))(cos())(cos(4

))(cos(4))(cos(422

2222222

22222222

22222)sin()sin(4

)sin()sin(4)sin()sin(4

)sin()sin(4(24)cos()cos(4

5.0arctan(2

1122211

12222

222

2221

21

21

221122

22

22

121

21132221

31

21

21

22

22

421

21

22

21

22

22

22

222

22

222

22

222

22222

222

222

2222

21

221

221

221

21

21

22

21

22

21

221

221

221

221

21

222211

21

22112

221122

42

442

4

41

412211

2121122

4

⎥⎥⎦

⎤

⎢⎢⎣

⎡

−

+−+−+−−+

⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢

⎣

⎡

+

−−−

+−+++

−+++−++

−−−+++−−

−+++++

−+−−−−

−−−−+−

∗=

θαθ

α

αθαθ

αθαθθα

θα

αθ

αθαθ

αθθα

θ

aada

dddtgatgtgda

datgda

addaad

tgaadddtgda

tgaatgdtgtgtgatgdtgtgtg

tgtgddtgdtgdtgddaaada

tgatgatgatgadadad

datgdaadtgatg

dadatgdaad

zxy

y

yz

zxxzxyyzy

xyzyx

zxyx

zzx

x

(A.3)

The final form of swivel angle φ and the elbow position e(φ ) as follow:

A-2

)

,

)

(

)

(

)

(

(2arctan

21

222

222

222

22

2

2

22

22

1

222

222

222

22

1

222

222

222

22

1

222

222

222

⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟

⎠

⎞

⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜

⎝

⎛

⎪⎪⎪⎪

⎭

⎪⎪⎪⎪

⎬

⎫

⎪⎪⎪⎪

⎩

⎪⎪⎪⎪

⎨

⎧

⎥⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢⎢

⎣

⎡

⎟⎟⎟⎟⎟

⎠

⎞

⎜⎜⎜⎜⎜

⎝

⎛

−−++

−−++

++−−

⎥⎥⎥⎥⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢⎢⎢⎢⎢

⎣

⎡

+

++

−+

−−

−−

−+

⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟

⎠

⎞

⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜

⎝

⎛

⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪

⎭

⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪

⎬

⎫

⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪

⎩

⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪

⎨

⎧

⎥⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢⎢

⎣

⎡

⎟⎟⎟⎟⎟

⎠

⎞

⎜⎜⎜⎜⎜

⎝

⎛

−−−−

+−−−−

+++−−

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

+

+−

−−

+

⎥⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢⎢

⎣

⎡

⎟⎟⎟⎟⎟

⎠

⎞

⎜⎜⎜⎜⎜

⎝

⎛

−−−−

+−−−−

+++−−

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

+

+−

−−

+

⎥⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢⎢

⎣

⎡

⎟⎟⎟⎟⎟

⎠

⎞

⎜⎜⎜⎜⎜

⎝

⎛

−−−−

+−−−−

+++−−

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

+

+−

−−

=

yyzxxzyzxz

zzyxxyzyxy

zzxyyxzxyx

xyxyxy

zzyyyxx

zzxxxyy

zyyzzxxz

xxzzyyzz

yyzxxz

zzyxxyyzxz

zzyxxyzyxy

zyxyyxzxyx

xyxzzxy

zxyxyz

yxzxyx

zzyxxyyzxz

zzyxxyzyxy

zyxyyxzxyx

xyxzzxy

zxyxyz

yxzxyx

zzyxxyyzxz

zzyxxyzyxy

zyxyyxzxyx

xyxzzxy

zxyxyz

yxzxyx

tgatgtgatgtgatga

tgatgtgatgtgatga

tgatgtgatgtgatga

atgeatge

aetgtgaetg

aetgtgaetg

tgaetgtgaetg

tgaetgtgaetg

aetgaetg

tgatgtgatgtgatga

tgatgtgatgtgatga

tgatgtgatgtgatga

tgatgeaetg

atgeatge

aetgaetg

tgatgtgatgtgatga

tgatgtgatgtgatga

tgatgtgatgtgatga

tgatgeaetg

atgeatge

aetgaetg

tgatgtgatgtgatga

tgatgtgatgtgatga

tgatgtgatgtgatga

tgatgeaetg

atgeatge

aetgaetg

φ

(

A.4 )

A-3

The form of elbow position can be get by the following equations:

( )

( )( )⎪⎪⎪⎪

⎭

⎪⎪⎪⎪

⎬

⎫

−−++−−++=

−−++=

++=⟩⟨

++=

τττσ

ττζ

τ

21

21

22

22

22

22

21

21

22

22

21

21

222

21

2

,

dadadada

dada

tgatgatgaatg

tgtgtg

zzyyxx

zyx

(A.5)

⎪⎪⎪⎪

⎭

⎪⎪⎪⎪

⎬

⎫

++

+++++=

+++−−−=

+++−=

+++−=

321

222222444

222223

222

221

)(2vvv

tgtgtgtgtgtgtgtgtgv

tgatgatgtgatgtgatgatgav

tgatgtgatgatgav

tgatgtgatgtgaav

zyzxyxzyx

zzyyzxxzzzyzxz

zzyyyxxyy

zzxyyxxxx

τ

τ

(A.6)

( )

( )

( )⎪⎪⎪⎪

⎭

⎪⎪⎪⎪

⎬

⎫

⎟⎟⎟⎟⎟⎟⎟⎟

⎠

⎞

⎜⎜⎜⎜⎜⎜⎜⎜

⎝

⎛

+⎟⎠⎞

⎜⎝⎛ ⟩⟨

−+

+⎟⎠⎞

⎜⎝⎛ ⟩⟨

−+

+⎟⎠⎞

⎜⎝⎛ ⟩⟨

−+

=

)sin()cos(,

)sin()cos(,

)sin()cos(,

)(

φφτ

σζ

φφτ

σζ

φφτ

σζ

φ

vtgatgatg

vtgatgatg

vtgatgatg

e

zzz

yyy

xxx

(A.7)

Solving for Shoulder Rotation Matrix R1:

Two coordinate systems are set up: one for the current arm position and one for

the desired arm position. The idea is to treat the shoulder rotation as the rotation from

the plane containing the current triangle oldold wes ⋅⋅Δ. to the plane containing the new

triangle . For our purposes it is easier to consider the frames defined by newnew wes ⋅⋅Δ.

A-4

these triangles and compute the rotation in between these two frames. This rotation is

then the rotation performed by R1 .

The rotation of the shoulder R1 is the change of bases. This change can be

computed by placing the vectors of the bases in homogeneous transformation

matrices at the position of the rotation matrices. The shoulder rotation is the rotation

from the first frame to the second frame. This is equivalent to the concatenation of the

rotation from the first frame to world frame and the rotation from the world frame to

the second frame.

⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢

⎣

⎡

⋅

⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢

⎣

⎡

=∧∧∧∧∧∧

100000

0

100000

0

1zyxzyxT goalgoalgoal (A.8)

We are able to extract the combined rotations of the shoulder in form of the 3

× 3 matrix R1(θ1,θ2,θ3). To further extract the values for the angle joints θ1,θ2, and θ3,

the combined rotations need to be decomposed, Rs can be defined as the

concatenation of three rotations in a specific order. Therefore, these matrices take the

following form:

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

−+−+++−

=

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡−⋅

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

−⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡ −=

+++

++++++++

++++++++

++

++

++

++

iiiii

iiiiiiiiiiii

iiiiiiiiiiii

ii

ii

ii

ii

ii

ii

rot

ccscscssscssscccs

cscssssccscc

cssc

cs

sccssc

R

111

12212212

12212212

11

11

22

22

00

001

0010

0.

10000

(A.9)

The final expression of analytical solution of shoulder joint angles are:

A-5

),,(2arctan)),sin(/,(2arctan

),,(2arctan

23233

11222

12121

cccsscs

ccsc

===

θθθ

θ (A.10)

Solving for Wrist Rotation Matrix R2:

Having set up analytical expressions for θ1,….,θ4 and φ , it is finally possible to

compute R2. Recall that the orientation and position, i.e. the constraints, of the end-

effector are given by G from equation (2.13). This equation can thus be solved for T2:

⎪⎪⎪

⎭

⎪⎪⎪

⎬

⎫

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡+

=

⋅=−

−

10001000

)(1

111

112

ggseyseys

eys

tRtRtRRRRRRR

GATATT

(A.11)

From the definitions of the respective matrices, the inverse of a homogeneous

transformation matrix can in general be expressed analytically as:

⎥⎦

⎤⎢⎣

⎡ −=⎥

⎦

⎤⎢⎣

⎡−

1010

1tRRtR TT

( A.12)

⎪⎪⎪⎪⎪

⎭

⎪⎪⎪⎪⎪

⎬

⎫

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡

≡

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡

=

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡−

=

100000),,(0

1000000

1000

76521

1112

θθθRRRRRR

tRRRRtRRRRRRRRRT

gTT

sTy

Te

eTe

Ty

Ts

Tg

TTs

Ty

Teg

TTs

Ty

Te

(A.13)

Because T2 only has a rotational part, we can reduce equation (2.24) and express R2

as:

A-6

gTT

sTy

Te

geys

RRRRR

RRRRRR

1

112 )(

=

⋅= −

(A.14)

Since the shoulder rotation matrix R2 is the same concatenation of rotation

matrices as R1, except of course that R2 is a function of θ5,θ6, and θ7, it is possible to

derive the values for these angles as done for the shoulder joints θ1,θ2, and θ3, to get:

),,(2arctan)),sin(/,(2arctan

),,(2arctan

67677

55666

56565

cccsscs

ccsc

===

θθθ

θ (A.15)

Last but not least, analytical representations for the joint angles θ5,θ6, and θ7.

All representations from this section can now be merged into a single solution for the

Inverse Kinematics problem of the given chain defined through end-effector position

tg and orientation Rg as well as the swivel angle φ .

A-7

Appendix B: Some Output of System Design: The output of atypical help of the developed package built to simulate

the design is shown in Fig.B.1.

Fig.B.1. Help Output for Muscles Demo.

B-1

Fig.B.2. Help Output for Skeleton Human Arm.

Fig.B.3. Help Output for Neuron Instruction.

B-2

Fig.B.4. Output of Virtual Human Arm Model

B-3

-a-

-b-

B-4

Variable

name

Data

x-axis in shoulder

( Xs)

x-axis in shoulder

( Xs)

x-axis in shoulder

( Xs)

Data

x-axis in

elbow ( Xs)

x-axis in

elbow ( Xs)

x-axis in

elbow ( Xs)

Data

x-axis in

wrist ( Xs)

x-axis in

wrist ( Xs)

x-axis in

wrist ( Xs)

Type of movement

y1 sine 0 0 +1 0 0 0 0 0 0 0 0 Abduction of arm at shoulder

y2 sine +1 0 0 0 0 0 0 0 0 0 0 Flexion of arm at shoulder

y3 sine -1 0 0 0 0 0 0 0 0 0 0 Extension of arm at shoulder

y4 0 0 0 0 sine +1 0 0 0 0 0 0 Flexion and Extension forearm at elbow

Y5 0 0 0 0 sine 0 1 0 0 0 0 0 Proration forearm at elbow

Y6 0 0 0 0 sine 0 -1 0 0 0 0 0 Supination forearm at elbow

- - -

-c-

Fig.B.5. Details of Simulink Human Body Demo Instruction. a. Shoulder joint Instruction. b. Wrist Joint Instruction. c. Elbow joint Instruction.

B-5

Fig.B.6. Output of Virtual Human Body Demo Model

B-6

Appendix C:

Sample Programs in Matlab Editor Ver.2009a: % %%%%%%%%%%%%%%%%%%% program No.one %%%%%%%%%%%%%%%%%%%%%%%% %% This program used to solve Two main kinematic taskes: %%%% %%%%% 1. slove inverse dynamic for real human arm and %%%%%% %%%%% the solution according to the classical algorithm %%%%% %%%% this algorithm define by Stephanie Drzevitzky 2008 %%%%% %%%% 2. solve the forward kinematic to check the values %%%%% %%%% of joint angles which are get by inverse kinematic %%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% clc clear %%%%%%%%%%%%%%%% INVERSE KINEMATIC ALGORITHM %%%%%%%%%%%%%%%%%%%% % Define Limb via matrix A & B with Denavit-Hartenberg parameters: % Define the End-Effector position with vector tg: %----------------------------------------------------------------- a1=20;%input('enter the parameter a1 D_H='); a2=20;%input('enter the parameter a2 D_H='); d1=20;%input('enter the parameter d1 D_H='); d2=20;%input('enter the parameter d2 D_H='); th1t=pi/4;%input('enter the parameter theta1 D_H='); th2t=pi/4;%input('enter the parameter theta2 D_H='); aL1=pi/4;%input('enter the parameter alpha1 D_H='); aL2=pi/4;%input('enter the parameter alpha2 D_H='); tgx=input('enter the end affecter in x axis tgx='); tgy=input('enter the end affecter in y axis tgy='); tgz=input('enter the end affecter in z axis tgz='); %------------------------------------------------------ % Define all initail values: ax=5;ay=65;az=65; a=[ax ay az];tho5=pi/6;tho6=pi/4;tho7=pi/6; c1t=cos(th1t);c2t=cos(th2t);caL1=cos(aL1);caL2=cos(aL2); s1t=sin(th1t);s2t=sin(th2t);saL1=sin(aL1);saL2=sin(aL2); ex=20;ey=-4.5;ez=20;tgxo=40;tgyo=40;tgzo=40;exo=30;eyo=30;ezo=30; tg=[tgx tgy tgz]; Ltg=norm(tg,2); eold=[exo eyo ezo]; %******************************************************* % Define the rotation of end-effector: thgx=pi;%input('enter the angle for rotate in of the end-affector x-axis ='); thgy=pi/2;%input('enter the angle for rotate in of the end-affector y-axis ='); thgz=pi/4;%input('enter the angle for rotate of the end-affector in z-axis =');

C-1

sx=sin(thgx);cx=cos(thgx); sy=sin(thgy);cy=cos(thgy);sz=sin(thgz);cz=cos(thgz); Rgch=[ cz*cy -sz*cx+cz*sy*sx sz*sx+cz*sy*cx sz*cy cz*cx+sz*sy*sx -cz*sx+sz*sy*cx -sy cy*sx cy*cx ]; tg=[tgx;tgy;tgz]; Gc=[Rgch,tg]; Gc(4,4)=1; Gch=Gc; %******************************************************* % calculated for matrix A : % A transformation from 0th to 1st frame : % rotation matrix around x-axis by angle alpha_o: Rotx_aL=[ 1 0 0 0 ; 0 caL1 -saL1 0 ; 0 saL1 caL1 0 ; 0 0 0 1 ]; % transation in x-axis by a1 Transx_a=[ 1 0 0 a1 ; 0 1 0 0 ; 0 0 1 0 ; 0 0 0 1 ]; % rotation matrix around z-axis by angle theta1: Rotz_th=[ c1t -s1t 0 0 ; s1t c1t 0 0; 0 0 1 0 ; 0 0 0 1 ]; % transation in z-axis by d1 Transz_d=[ 1 0 0 0 ; 0 1 0 0 ; 0 0 1 d1 ; 0 0 0 1 ]; % transformation matrix from 0th frame to 1st frame is ( A matrix ): A=Rotx_aL*Transx_a*Rotz_th*Transz_d Ra=A(1:3,1:3); ta=A(1:3,4); Lta=norm(ta,2); Lu=Lta; %pause; % calculated for matrix B: % A transformation from 1st to 2nd frame : % rotation matrix around x-axis by angle alpha: Rot1x_aL=[ 1 0 0 0 ; 0 caL2 -saL2 0 ; 0 saL2 caL2 0 ; 0 0 0 1 ]; % transation in x-axis by a2 Trans1x_a=[ 1 0 0 a2 ; 0 1 0 0 ; 0 0 1 0 ; 0 0 0 1 ]; % rotation matrix around z-axis by angle theta: Rot1z_th=[ c2t -s2t 0 0 ; s2t c2t 0 0; 0 0 1 0 ; 0 0 0 1 ]; % transation in z-axis by d1 Trans1z_d=[ 1 0 0 0 ; 0 1 0 0 ; 0 0 1 d2 ; 0 0 0 1 ]; % transformation matrix from ith frame to ith+1 frame is ( B matrix ): B=Rot1x_aL*Trans1x_a*Rot1z_th*Trans1z_d Rb=B(1:3,1:3); tb=B(1:3,4);

C-2

Ltb=norm(tb,2); Lf=Ltb; % define the buffers for storge: Fia1=[];Fi1=[];Fi2=[];the4=[];the1=[];the2=[];the3=[];T1F=[];TyFa=[] ;the4a=[];the1a=[];the2a=[];the3a=[];the5=[];the6=[];the7=[];TT1F=[]; FiF=[];Gfr=[];E2=[];TT1F=[];T2F=[];TyF=[];EFiF=[];Ecf=[];the4q=[]; tgxF=[];tgyF=[];tgzF=[];thez4=[];tgyF1=[];tgzF1=[];tgxF1=[];TGF=[]; T1fr=[];T2fr=[];Tyfr=[];tgyF2=[];tgzF2=[];tgxF2=[]; as=3; %******************************************************* % define the condition for envelope of moveing of arm: mm1=abs(Lta.^2+Ltb.^2-Ltg.^2); mm2=abs(2*Lta*Ltb); if mm1 > mm2 disp('this coordinate ot of range movement') stop end N=10; %******************************************************* % Solve elbow angle theta4 : Eqn 2.2.7: % Choice the value of elbow angle from two values: %------------------------------------------------------- for ii=1:N; tg=[tgx tgy tgz]; tgxF1=[tgxF1;tgx]; tgyF1=[tgyF1;tgy]; tgzF1=[tgzF1;tgz]; Ltg=norm(tg,2); th4n1=2*atan2(0.5*(-4*caL2*d2*a1*c1t+4*a2*d1-2*(4*tgx.^2*a1*s1t*saL2*d2 ... -a1.^4-d1.^4-tgx.^4-a2.^4-tgz.^4-d2.^4 ... -a2.^2*a1*s1t*saL2*d2+4*tgz.^2*a1*s1t*saL2*d2 ... +4*d1.^2*a1*s1t*saL2*d2+2*a2.^2*d1.^2+2*a1.^2*tgx.^2 ... +2*a1.^2*tgy.^2+2*a1.^2*tgz.^2-2*a1.^2*d1.^2-2*a1.^2*a2.^2 ... -2*a1.^2*d1.^2+2*d1.^2*tgx.^2+2*d1.^2*tgy.^2 ... +2*d1.^2*tgz.^2-2*d1.^2*d2.^2-2*tgy.^2*tgx.^2-2*tgy.^2*tgz.^2 ... +2*tgy.^2*d2.^2+2*tgy.^2*a2.^2-2*tgx.^2*tgz.^2+2*tgx.^2*d2.^2 ... +2*tgx.^2*d2.^2+2*tgx.^2*a2.^2+2*a2.^2*tgz.^2-2*a2.^2*d2.^2 ... +2*tgz.^2*d2.^2+4*caL2.^2*d2.^2*d1.^2+4*a2.^2*a1.^2*c1t.^2 ... +tgy.^4+4*caL2.^2*d2.^2*a1.^2*c1t.^2-4*a1.^3*s1t*saL2*d2 ... -4*d2.^3*a1*s1t*saL2-4*a1.^2*s1t.^2*saL2.^2*d2.^2 ... +4*tgy.^2*a1*s1t*saL2*d2).^0.5),(a1.^2+d1.^2-tgy.^2-tgx.^2 ... +a2.^2-tgz.^2+d2.^2-2*caL2*d2*d1+2*a1*s1t*saL2*d2-2*a2*a1*c1t)) % or

C-3

th4n2=2*atan2(0.5*(-4*caL2*d2*a1*c1t+4*a2*d1+2*(4*tgx.^2*a1*s1t*saL2*d2 ... -a1.^4-d1.^4-tgx.^4-a2.^4-tgz.^4-d2.^4 ... -a2.^2*a1*s1t*saL2*d2+4*tgz.^2*a1*s1t*saL2*d2 ... +4*d1.^2*a1*s1t*saL2*d2+2*a2.^2*d1.^2+2*a1.^2*tgx.^2 ... +2*a1.^2*tgy.^2+2*a1.^2*tgz.^2-2*a1.^2*d1.^2-2*a1.^2*a2.^2 ... -2*a1.^2*d1.^2+2*d1.^2*tgx.^2+2*d1.^2*tgy.^2 ... +2*d1.^2*tgz.^2-2*d1.^2*d2.^2-2*tgy.^2*tgx.^2-2*tgy.^2*tgz.^2 ... +2*tgy.^2*d2.^2+2*tgy.^2*a2.^2-2*tgx.^2*tgz.^2+2*tgx.^2*d2.^2 ... +2*tgx.^2*d2.^2+2*tgx.^2*a2.^2+2*a2.^2*tgz.^2-2*a2.^2*d2.^2 .. . +2*tgz.^2*d2.^2+4*caL2.^2*d2.^2*d1.^2+4*a2.^2*a1.^2*c1t.^2 ... +tgy.^4+4*caL2.^2*d2.^2*a1.^2*c1t.^2-4*a1.^3*s1t*saL2*d2 ... -4*d2.^3*a1*s1t*saL2-4*a1.^2*s1t.^2*saL2.^2*d2.^2 ... +4*tgy.^2*a1*s1t*saL2*d2).^0.5),(a1.^2+d1.^2-tgy.^2-tgx.^2 ... +a2.^2-tgz.^2+d2.^2-2*caL2*d2*d1+2*a1*s1t*saL2*d2-2*a2*a1*c1t)) % choice as value according to three choices in algorithm: % as=input('enter 1 or 2 for select the angle of rotate elbow='); if as==1 th4=th4n1; elseif as==2 th4=th4n2 else th4=1.571;%input('enter the correct value of theta4='); end the4=[the4;th4]; % if elbow position e is known but swivel angle unknown: % solve Eqn: 2.2.16: %-------------------------------------------------------------------- %define the elbow position: ex=6;ey=1;ez=6; % to calculate the swivel angle phi is : Fia= atan2((abs(((tgz*ey*ax-tgz*ex*ay-ez*tgy*ax-ey*tgx*az+tgy*ex*az+ez*tgx*ay)*tgx)./ ... ((abs(-ax*tgy.^2-ax*tgz.^2+tgx*ay*tgy+tgx*az*tgz).^2+abs(ay*tgx.^2+ ... ay*tgz.^2-tgy*ax*tgx-tgy*az*tgz).^2+abs(az*tgx.^2+az*tgy.^2-tgz*ax*tgx ... -tgz*ay*tgy).^2).^0.5)).^2 ... +abs(((tgz*ey*ax-tgz*ex*ay-ez*tgy*ax-ey*tgx*az+tgy*ex*az+ez*tgx*ay)*tgy)./ ... ((abs(-ax*tgy.^2-ax*tgz.^2+tgx*ay*tgy+tgx*az*tgz).^2+abs(ay*tgx.^2+ ... ay*tgz.^2-tgy*ax*tgx-tgy*az*tgz).^2+abs(az*tgx.^2+az*tgy.^2-tgz*ax*tgx ... -tgz*ay*tgy).^2).^0.5)).^2 ... +abs(((tgz*ey*ax-tgz*ex*ay-ez*tgy*ax-ey*tgx*az+tgy*ex*az+ez*tgx*ay)*tgz)./ ...

C-4

((abs(-ax*tgy.^2-ax*tgz.^2+tgx*ay*tgy+tgx*az*tgz).^2+abs(ay*tgx.^2+ ... ay*tgz.^2-tgy*ax*tgx-tgy*az*tgz).^2+abs(az*tgx.^2+az*tgy.^2-tgz*ax*tgx ... -tgz*ay*tgy).^2).^0.5)).^2).^0.5, ... (tgz.^2*ex*ax+tgz.^2*ey*ay-tgz*ez*ay*tgy-tgz*ez*ax*tgx-tgz*tgx*ex*az ... -tgz*tgy*ey*az-tgy*ey*ax*tgx+tgx.^2*ez*az-tgx*ex*ay*tgy+tgy.^2*ez*az ... +ey*tgx.^2*ay+ex*tgy.^2*ax)/ ... ((abs(-ax*tgy.^2-ax*tgz.^2+tgx*ay*tgy+tgx*az*tgz).^2+abs(ay*tgx.^2+ ... ay*tgz.^2-tgy*ax*tgx-tgy*az*tgz).^2+abs(az*tgx.^2+az*tgy.^2-tgz*ax*tgx ... -tgz*ay*tgy).^2).^0.5)) Fia1=[Fia1;Fia] ni=tg./Ltg; LLa=norm(a,2); ai=a./LLa; vb=ai-(dot(ai,ni)*ni); Lvb=norm(vb,2); ui=vb./Lvb; vi=cross(ni,ui); aL=acos((Lta.^2+Ltg.^2-Ltb.^2)/(2*Lta*Ltg)); CC=cos(aL)*Lta*ni; % Eqn 2.2.10 CC_a=((Lta.^2+Ltg.^2-Ltb.^2)/(2*Lta*Ltg))*Lta*ni RR=sin(aL)*Lta RR_a=(1-((Lta.^2+Ltg.^2-Ltb.^2)/(2*Lta*Ltg)).^2).^0.5*Lta ; % Eqn 2.2.11 eFi=CC+RR*((cos(Fia)*ui)+(sin(Fia)*vi)); EFiF=[EFiF;eFi]; %************************************************************************ % to calculate R1 ,R2 ,th1 --- th7 : % Define the previous elbow position eold % Define the previous end-effector position tgold %------------------------------------------------------------------------ % solve sholuder rotation matrix R1 Eqn 2.2.18 c4=cos(th4);s4=sin(th4); nor_eo=((abs(exo)).^2+(abs(eyo)).^2+(abs(ezo)).^2).^0.5; % eqn of normal vector nor_en=((abs(ex)).^2+(abs(ey)).^2+(abs(ez)).^2).^0.5; h1=norm(eold,2); %used norm matlab function to check the difference Xold=[a1 saL1*d1 caL1*d1]; h3=norm(Xold,2); wold=[tgxo tgyo tgzo] qq1=c1t*c4*a2+s1t*saL2*d2+c1t*s4*caL2*d2+a1 ; % used Eqn 2.2.20 qq2=((caL1*s1t*c4+saL1*s4)*a2)-caL1*c1t*saL2*d2+(caL1*s1t*s4-saL1*c4)*caL2*d2-saL1*d1; qq3=((caL1*s1t*c4-saL1*s4)*a2)-caL1*c1t*saL2*d2+(caL1*s1t*s4+saL1*c4)*caL2*d2+saL1*d1;

C-5

wwold=[qq1 qq2 qq3] % used Eqn 2.2.20 h4=norm(wwold,2); ex=eFi(1);ey=eFi(2);ez=eFi(3); enew=[ex ey ez] h2=norm(enew,2) ; wnew=[tgx tgy tgz]; X=eold./h1; xgoal=enew./nor_en; XX=Xold./h3; y1=wold-(dot(wold,X)*X); y1n=norm(y1,2); Y=y1./y1n; yy1=wwold-(dot(wwold,XX)*XX); yy1n=norm(yy1,2); YY=yy1./yy1n; y1g=wnew-(dot(wnew,xgoal)*xgoal); y1gn=norm(y1g,2); ygoal=y1g./y1gn; Z=cross(X,Y); ZZ=cross(XX,YY); zgoal=cross(xgoal,ygoal); rt1=[xgoal;ygoal;zgoal]'; rt1(4,4)=1; R1a=rt1; rt2=[X;Y;Z]';rt2(4,4)=1; rrt2=[XX;YY;ZZ]';rrt2(4,4)=1; R2a=rt2'; RR2a=rrt2'; disp('solve by used Eqn 2.2.18'); T1=R1a*R2a % solve by used Eqn 2.2.18 disp('solve by used Eqn 2.2.20'); TT1=R1a*RR2a; % solve by used Eqn 2.2.20 T1F=[T1F;T1]; TT1F=[TT1F;TT1]; %*************************************************************** % solve for T2 matrix Ry=[c4 0 s4;0 1 0;-s4 0 c4]; Ty=Ry;Ty(4,4)=1 TyF=[TyF;Ty]; R1=T1(1:3,1:3); th1=atan2(R1(3,2),R1(3,3)); s1=sin(th1); th2=atan2(-R1(3,1),R1(3,2)/s1); th3=atan2(R1(2,1),R1(1,1));

C-6

%************************************************************** R2=Rb'*Ry'*Ra'*R1'*Rgch; ra2=R2; ra2(4,4)=1; T2=ra2 T2F=[T2F;T2]; th5=atan2(R2(3,2),R2(3,3)); s5=sin(th5); th6=atan2(-R2(3,1),R2(3,2)/s5); th7=atan2(R2(2,1),R2(1,1)); %****************************************************************** % Inverse Kinematics: the1=[the1;th1] the2=[the2;th2] the3=[the3;th3] the5=[the5;th5] the6=[the6;th6] the7=[the7;th7] %*************************************************************** %%%%%%%%%%%%%%%% INVERSE KINEMATIC ALGORITHM %%%%%%%%%%%%%%%%%%% %*************************************************************** s1=sin(th1);c1=cos(th1);s2=sin(th2);c2=cos(th2); s3=sin(th3);c3=cos(th3);s4=sin(th4);c4=cos(th4); s5=sin(th5);c5=cos(th5);s6=sin(th6);c6=cos(th6); s7=sin(th7);c7=cos(th7); %************************************************************* %disp(' THE SHOLUDER MATRIX:'); %disp(' Rotate around X_axis by theta1:'); Rot_sx=[ 1 0 0 ; 0 c1 -s1 ; 0 s1 c1 ]; %disp(' Rotate around Y_axis by theta2:'); Rot_sy=[ c2 0 s2 ; 0 1 0 ; -s2 0 c2 ]; %disp(' Rotate around Z_axis by theta3:'); Rot_sz=[ c3 -s3 0 ; s3 c3 0 ; 0 0 1 ]; RSxyz=Rot_sz*Rot_sy*Rot_sx; R1f=RSxyz; T1f=RSxyz; T1f(4,4)=1 T1fr=[T1fr;T1f]; %disp(' THE ELBOW MATRIX :'); %disp(' Rotate around Y_axis by theta4:'); Rot_ey=[ c4 0 s4 ; 0 1 0 ; -s4 0 c4 ]; Ry=Rot_ey;

C-7

Tyf=Rot_ey; Tyf(4,4)=1 Tyfr=[Tyfr;Tyf]; %disp('THE WRIST MATRIX:'); %disp(' Rotate around X_axis by theta5:'); Rot_wx=[ 1 0 0 ; 0 c5 -s5 ; 0 s5 c5 ]; %disp(' Rotate around Y_axis by theta6:'); Rot_wy=[ c6 0 s6 ; 0 1 0 ; -s6 0 c6 ]; %disp(' Rotate around Z_axis by theta7:'); Rot_wz=[ c7 -s7 0 ; s7 c7 0 ; 0 0 1 ]; RWxyz=Rot_wz*Rot_wy*Rot_wx; R2f=RWxyz; T2f=RWxyz; T2f(4,4)=1 T2fr=[T2fr;T2f]; Gf=T1f*A*Tyf*B*T2f Gfr=[Gfr;Gf]; %pause; tgxa=Gf(1,4); tgya=Gf(2,4); tgza=Gf(3,4); ee1=tgx-tgxa ee2=tgy-tgya ee3=tgz-tgza %pause; tgx=tgxa tgy=tgya tgz=tgza tgxF=[tgxF;tgxa]; tgyF=[tgyF;tgya]; tgzF=[tgzF;tgza]; %**************************************************** end ii=1:N;tgxo(1:ii)=4;tgyo=tgxo;tgzo=tgxo; figure; plot(ii,tgxF1,'--.r',ii,tgxF,'-.og',ii,tgxo,'*');title('tgx'); figure; plot(ii,tgyF1,'*r',ii,tgyF,'-.og',ii,tgyo,'*');title('tgy'); figure; plot(ii,tgzF1,'.r',ii,tgzF,'-.og',ii,tgzo,'*');title('tgz');

C-8

% -------------------------------------------------------- % ---- program No.Two ---- % ---- identification of Neural Network by EMG signal ---- % ---- The signal get from seven muscles ---- % ---- FAST MOMENTUM BACKPROBAGATION ALGORITHM ---- % --------------------------------------------------------- clear clc tic %----------------------------------------------------- % LOAD DATA FOR emg SIGNALS: load yydat % REARRAGEMENTS OF DATA TO INPUT NN: ip=[ yo1 yo2 yo3 yo4 yo5 yo6 yo7 ]; op=ip; mm=max(max([ip])); mm1=mm+(0.45.*mm); ipn=ip./mm1; opn=op./mm1; % -------------------- n=7; % input neurons p=30; % hidden neurons m=7; % output neurons alpha=.01; % learning rate mu=.015; % momentum term %------------------------------ Y=[];ww3=[];bb3=[];ww4=[];bb4=[];opp=[]; N=max(size(ip)); % --------------------- % -- initialization --- % all initial cond. = 0 % --------------------- for i=1:n; for j=1:p; w3(i,j)=0.00001;%rand*1.65-1*.001; dw3(i,j)=0; end end for j=1:p; b3(j)=0.0;%rand*1.65-1*.001; db3(j)=0; end for i=1:m; for j=1:p; w4(i,j)=0.0;%rand*1.65-1*.001; dw4(i,j)=0; end end

C-9

for j=1:m; b4(j)=0.0;%rand*1.65-1*.001; db4(j)=0; end % -------------------- % -- input to sys. --- % -------------------- yd(1)=0; % initial condition iter=0;B=40; Y1=[ 0 0 0 0 0 0 0 ]; for kk=1:N-1; for i=1:n; x(kk,i)=ipn(kk,i); % 1st input end % -------------------- % forward propagation % -------------------- for j=1:p; % bipolar activation function [-1,1], bounded zin=0 ; % processing (n-p) for i=1:n; zin = zin + x(kk,i) * w3(i,j); end zin = zin + b3(j); z(j) = ( 1 - exp(-1 * zin) ) / ( 1 + exp(-1 * zin) ); end for i=1:m; % processing (p-m) yin(i)=0 ; for j=1:p; yin = yin + z(j) * w4(i,j); end yin(i) = yin(i) + b4(i); y(i) = ( 1 - exp(-1 * yin(i)) )*B / ( 1 + exp(-1 * yin(i)) );%yin(i);% end Y1=[Y1;y]; ww3=[ww3;w3]; ww4=[ww4;w4]; bb3=[bb3;b3]; bb4=[bb4;b4]; opp=[opp;opn]; % -------------------- % backward propagation % -------------------- for i=1:m; errin1(i) = ( opn(kk,i) - Y1(kk,i) ) * ( 1 + y(i) ) * ( 1 - y(i) ); end

C-10

% ---------- for j=1:p; sign=0; for i=1:m; sign = sign + errin1(i) * w4(i,j); end errin2(j) = sign * ( 1 + z(j) ) * ( 1 - z(j) ); end % ---------- for j=1:p; for i=1:m; dw4(i,j) = alpha * errin1(i) * z(j) + mu * dw4(i,j); end end % ---------- for i=1:n; for j=1:p; dw3(i,j) = alpha * errin2(j) * x(i) + mu * dw3(i,j); end end % ---------- for j=1:p; db3(j) = alpha * errin2(j); end % ---------- db4 = alpha * errin1; % ---------- for i=1:n; for j=1:p; w3(i,j) = w3(i,j) + dw3(i,j); end end % ---------- for i=1:m; for j=1:p; w4(i,j) = w4(i,j) + dw4(i,j); end end % ---------- for i=1:p; b3(i) = b3(i) + db3(i); end % ---------- b4 = b4 + db4; % -------------------- % - error processing - % -------------------- % iter=iter+1; for i=1:m; we(kk,i) = abs( opn(i) - Y1(kk,i) ); % mean square error error(kk,i) = ( .5 * we(kk,i) * we(kk,i) ); end %error

C-11

%pause; % -------------------- end % end of main for loop Y2=Y1.*mm1; figure subplot(411);plot(error(:,1)); subplot(412);plot(error(:,2)); subplot(413);plot(error(:,3)); subplot(414);plot(error(:,4)); figure subplot(311);plot(error(:,5)); subplot(312);plot(error(:,6)); subplot(313);plot(error(:,7)); figure hold on plot(Y2(:,1)); plot(op(:,1),'--r'); hold off figure hold on plot(op(:,2),'--k'); plot(Y2(:,2),'m'); hold off figure hold on plot(Y2(:,3)); plot(op(:,3),'--r'); hold off figure hold on plot(op(:,4),'--k'); plot(Y2(:,4),'m'); hold off figure hold on plot(Y2(:,5)); plot(op(:,5),'--r'); hold off figure hold on plot(op(:,6),'--k'); plot(Y2(:,6),'m'); hold off figure hold on plot(op(:,7),'--g'); plot(Y2(:,7)); hold off time_learning=toc w3f=ww3; b3f=bb3; w4f=ww4; b4f=bb4; Yn=Y2;

% SAVING NN DATA TO USED IN NN CLASSIFICATIO .....

save ydat_idenf w3f b3f w4f b4f Yn Y1 time_learning

C-12

بيئة الحقيقية االفتراضية وھي نوع من براميجات المحاكاة لألشياء التناول : الجزء الثالث

باستخدام التمثيل الثالثي لألبعاد، وقد تم استخدم لتقديم أنموذجات مختلفة للذراع البشري

). أنموذج ھيكل عظمي للذراع وأنموذج جسم بشري كامل ( االفتراضي

لتجستلطة ھي نوع جديد ان الحقيقة البشرية المخ األشياء في بيئة الحقيقية االفتراضية،مي

استخدم لتقديم أنموذج الھيكل العظمي للذراع البشري تم موائمة الحقيقة االفتراضية مع .

برامجيات الماتالب لتوليد األوامر إلى األنموذجات المشارة اليھا أعاله والتي بدورھا

التحليلي لمعضلة الحركة العكسية او من خالل تميز إشارة حسبت اما باأستخدام الحل

.التحفيز العضلي

يقدم تصميم حزمة برمجية لمحاكاة األجزاء الثالثة السالفة الذكر اعاله من : الجزء الرابع

خالل التنقل لبيانات المحاكاة ما بين مختلف البرامج كما انھا تحوي على عدة مساعدات

لمنظومة العاملة مع المالحظات العامة معززة بالصور تشرح االجزاء المختلفة ل

.واالفالم

تم الحصول على نتائج مرضية تبين حل حركة الذراع البشري بطريقة تحليلية

ذات التعليم –مقترحة، عالوة على ذلك تبين الفائدة من استخدام الشبكات العصبية

د على إشارة التحفيز العضلي العكسي المحدد في تمييز حركات الذراع البشري باالعتما

وفعالية البيئة االفتراضية في تطبيق كل حركات الذراع البشري االفتراضي المشابھة

. لألداء البشري الحقيقي

المستخلص

، يعمل بموجب أوامر محسوبة لبشري الحقيقي ھو نظام حركي معقدالذراع ا

تم تقديم تصميم وتحليل . انيةريقة ربعمل بطمنظومة تحريك تبطريقة دقيقة جدا ويمتلك

إن. وبسبعة درجات من حرية الحركة بطريقة المحاكاة,لألنموذج الرياضي للذراع

والتي بدورھا تمكن الذراع من أداء حركة ةتحفيز العضلھي أشارة ) EMG(أشارة

.معينة

يق ھذه م الحل التحليلي لمعضلة الحركة العكسية للمنظومة الديناميكية لتطبأستخد

على الحتواءالمنظومة في محاكاة البيئة الحقيقية ألنه سريع جدا في الحساب وله القابلية

ذات التعليم العكسي المحدد لمنظومة – الشبكات العصبية استخدمت. قيود حقيقية

التحفيز العضلي وتصنيفھا وتمييز حركة الذراع البشري تم اإلشاراتالمحاكاة لتعريف

لمحاكاة الستخدامھا الحقيقة االفتراضية المعشقة مع برنامج المات الب أدوات استخدام

.المنظومة

:ھذا العمل يتكون من أربعة أجزاء رئيسية وھي كاألتي

يقدم الحل التحليلي لمعضلة الحركة العكسية بطريقة مقترحة وتم مقارنة :الجزء األول

.نتائج ھذا المقترح مع أخر طرق الحل في ھذا المجال

يصف أشارة التحفيز العضلي وبيولوجية توليدھا ومصادر ھذه اإلشارة : الثانيلجزء ا

وخواصھا ومواصفاتھا عالوة على ذلك يقدم منظومة القياس الحقيقي لإلشارة وعمليات

التي تستخدم في عملية ) مستوى وفترة اإلشارة ( التحليل الستخالص السمات المناسبة

م تقديم ھيكل مقترح لعملية التصنيف تمييز حركة الذراع ت. التعريف للشبكات العصبية

مع تعديالت مناسبة ) EMG (أشارةتم تقديم محاكي . بناءا على إشارة التحفيز العضلي

بناءا على المواصفات السريرية للعضلة وبالتالي يمكن ) EMG(والذي يستخدم لتوليد الـ

). EMGlab( براميجات باستخدام) EMG( الـ أشارة تستخدم ھذه البيانات لمحاكاة أن

راقــــــــورية العـــــــــجمھ وزارة التعليم العالي والبحث العلمي

ةــــــــيـولوجــــــة التكنــــالجامع للھندسة والعلوم دـــــــــــــرشيـة الـــــــليــك

قسم الھندسة الكھربائية وااللكترونية

تصميم تحليل و ذر اع بشري افتراضي بوساطة أشارة التحفيز العضلي مقاد

كلية الرشيد للھندسة والعلوم قسم الھندسة الكھربائية وااللكترونية أطروحة مقدمة إلى دكتوراه فلسفة وجية وھي جزء من متطلبات نيل درجة شھادة لفي الجـــامعــة التـكنو

قسم الھندسة الكھربائية وااللكترونيةفي )ھنــــدسة سيطــــرة (

يوسف إمساعيــــل حممــد املشهــــدانـي لــن قبـم ) 1999ماجستري (

رافـــــبأش محمــد زكـــــي الفــائـــز.د.م.أ

م 2009أيلــــول ھـ 1430رمضــان