Ngo Duy Kien _Thesis Version 10_27

7/31/2019 Ngo Duy Kien _Thesis Version 10_27

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Error! Reference source not found.VIETNAM NATIONAL UNIVERSITY, HA NOI

UNIVERSITY OF ENGINEERING AND TECHNOLOGY

--------

Ngo Duy Kien

FALL DETECTION BASED ON

ACCELEROMETER SENSOR

Major: Computer Science

Ha Noi 2012


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VIETNAM NATIONAL UNIVERSITY, HA NOI

UNIVERSITY OF ENGINEERING AND TECHNOLOGY

--------

Ngo Duy Kien

FALL DETECTION BASED ON

ACCELEROMETER SENSOR

Major: Computer Science

Supervisor:Assoc. Prof. Bui The Duy

Co-Supervisor:Dr. Vu Thi Hong Nhan

Ha Noi 2012


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AUTHORSHIP

I hereby declare that the work contained in this thesis is of my own and has not been

previously submitted for a degree or diploma at this or any other higher education institution.

To the best of my knowledge and belief, the thesis contains no materials previously published

or written by another person except where due reference or acknowledgement is made.

Signature:


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SUPERVISORS APPROVAL

I hereby approve that the thesis in its current form is ready for committee examination as a

requirement for the Bachelor of Computer Science degree at the University of Engineering

and Technology.

Signature:


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ACKNOWLEDGEMENT

First of all, I wish to express my respect and my deepest thanks to my advisers

Assoc.Prof. Bui The Duy and Dr. Vu Thi Hong Nhan, University of Engineering and

Technology, Viet Nam National University, Ha Noi for their enthusiastic guidance, warm

encouragement and useful research experiences.

I would like to gratefully thank all the teachers of University of Engineering and

Technology, VNU for their invaluable knowledge which they gave to me during four

academic years.

I would also like to say my special thanks to my friends in K53CA class, University

of Engineering and Technology, VNU, especially Tran Nguyen Le in the Behavior

Recognition group for their helpful discussions.

Last, but not least, my family is really the biggest motivation behind me. My parents

and my brother always encourage me when I have stress and difficulty. I would like to

send them my gratefulness and love.

Ha Noi, May 24th

, 2012Ngo Duy Kien


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FALL DETECTION BASED ON ACCELEROMETER SENSOR

Ngo Duy Kien

Course: QH-2008-I/CQ, Computer Science

Abstract:

The percentage of elderly population is rapidly growing in recent years. Fall-relatedinjuries are a main issue for this population. Falls account for approximately half of all

admissions- related injuries. Falls cause not only broken bones and other health injuries, but also

cause psychological trauma which can reduce the independence and confidence in

communication. Therefore, techniques for detecting and tracking down the movement are

instrumental in dealing with this problem.

Elderly people often want to live at home, therefore, new technologies need to be able to

support automatic fall detection, which guarantee their living independence and security. Thisthesis meets the challenge of different types of motions as part of a system designed to fulfill the

demand for wearable device to collect data for fall and non-fall analysis. This study focuses on

building a standard database for fall events and evaluating different low-complexity the typical

attributes for fall detection by using tri-axial accelerometer sensor. Towards this task, scenarios in

which both fall and non-fall events in different situations are set up. They include: forward falls,

backward falls and lateral falls right and lateral falls left, performed with legs straight and flexed

in fire middle aged subjects. Data from non-fall (activity of daily living) were used as the

reference. Some approaches with increasing detection complexity are investigated and employed

for the real time classification in the characteristics of a fall event: beginning of the fall, falling

velocity, variance of the falls, the accelerometer inclination angles, the acceleration vector

changes and fall impact after the fall.

The experimental results indicate that with machine learning algorithm, fall detection

using a tri-axial accelerometer sensor is efficient, and can reach a sensitivity of 94%.

Keyword: fall detection, accelerometer, activity of daily living


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Table of Contents

Table of Contents ...................................................................................................... viList of Figures ......................................................................................................... viiiList of Table .............................................................................................................. ixChapter 1Introduction ............................................................................................ 1Chapter 2Related Work .......................................................................................... 3

2.1. Fall Detection using 3D Head Trajectory Extracted from a Signal CameraVideo Sequence ...................................................................................................... 42.2. Fall Detection using accelerometer sensor ........................................................ 6

2.2.1 Analytical and Threshold Method ............................................................. 72.2.1.1. Human fall detection algorithm using bi-axial gyroscope sensor ......... 72.2.1.2. Human fall-detection algorithm using tri-axial accelerometer sensor 10

2.2.1 Learning Methods for fall detection......................................................... 13Chapter 3Fall Detection Process ........................................................................... 15

3.1. Accelerometer Data collection ....................................................................... 163.2. Algorithm for Fall Detection .......................................................................... 18

3.2.1. Pre-noise processing data ........................................................................ 193.2.2. Action Segmentation ............................................................................... 203.2.3. Feature Extraction ................................................................................... 20

3.2.3.1. Length of the acceleration vector ...................................................... 213.2.3.2. The variance of the length of the acceleration vector ........................ 21 3.2.3.3. The speed of change in acceleration along the z-axis ........................ 223.2.3.4. The acceleration vector changes (AVC) ............................................ 223.2.3.5. The accelerometer inclination angles ................................................ 23

3.2.4. Forming feature vector ............................................................................ 233.3. Action learning .............................................................................................. 24

3.3.1. Support Vector Machine (SVM) .............................................................. 243.3.2. Learning .................................................................................................. 27


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Chapter 4Experimental Setup and evaluation ..................................................... 284.1. Experiment Setup........................................................................................... 28

4.1.1. Background of fall and non-fall action .................................................... 284.1.1.1. Study of fall...................................................................................... 294.1.1.2. The simulated-fall study ................................................................... 314.1.1.3. The ADL study (Activity of Daily Living) ........................................ 31

4.1.2. Data Collection ....................................................................................... 334.2. Performance measures ................................................................................... 364.3. Experimental results of the fall detection system ............................................ 36

Chapter 5 ................................................................................................................ 38Conclusion ............................................................................................................... 38Bibliography ............................................................................................................ 40Appendix A .............................................................................................................. 43


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List of Figures

Figure 2-1: Reading trunk pitches rolocity V v and horizontal velocity Vh. These velocities

were obtained without markers from the 3D trajectory of a person who brutally sits down andfalls. The different action are: the person(a) stands up,(b)sits down, (c) is seated, (d)stands upagain, (e)remains stand up and (f) falls ................................................................................... 5Figure 2-2: Acceleromter Sensor ............................................................................................ 6Figure 2-3: Reading trunk pitches and roll gyroscope ............................................................. 8 Figure 2-4: Overlapping and non-overlapping sampe Fall and ADL data................................ 8Figure 2-5: Bi-axial gyroscope fall-detection algorithm flow chart ......................................... 9Figure 2-6: Fall detection algorithm operation example for upper and lower fall threshold,using an artificial example signal ......................................................................................... 10Figure 3-1: The general model cycle .................................................................................... 16Figure 3-2: Fall annotation for a single fall ........................................................................... 17Figure 3-3: Fall Detection Flow Chart .................................................................................. 18Figure 3-4: Low-pas and high-pass filter algorithm ............................................................. 19Figure 3-5: RSS plot of a forward fall .................................................................................. 21Figure 3-6: Accelerometer inclination angle ......................................................................... 23Figure 3-7: Separating hyper plane ....................................................................................... 25Figure 4-1: The stage of fall ................................................................................................. 29

Figure 4-2: The four phases of a fall event ........................................................................... 30 Figure 4-3: Two types of non-fall(ADL) .............................................................................. 32


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List of Table

Table 3.1: Sample data acquired from acceleromter ............................................................. 17 Table 4.1: Events Sequence in the test Scenario ................................................................... 34Table 4.2: The detail of ten young volunteers ....................................................................... 35 Table 4.3: The distribution of the samples ............................................................................ 37Table 4.4: The test result ...................................................................................................... 37


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Chapter 1

Introduction

In the modern society, the percentage of elderly people is increasing rapidly.

The proportion of the population aged more than 65 years old in the developed

countries is projected to increase from 7.5 % in 2009 to 19.6% in 2030 (United

Nations 2009).The ratio of elderly people living alone in the house is quite high. Fall-related injury is a main issue of elderly people, both home environment and hospital

which affects the overall quality of life either at home or hospital. Most of falls occur

when walking, standing or sitting down, or trying to find something.Peopleusuallythink that falling recognition is the most important in our lives. Fall detection has

become a major health concern recently. Falls account for approximately half of all

admissions which are related to injury. Falls cause not only disabling fractures and

other health injuries but also traumas which can reduce the independence and

confidence when communicating. Moreover, the elder people always want to live in a

place which ensures the independence and safety in life. Because of this, new

technologies to guarantee and assist their lives are absolutely necessary. This is a

factor to promote the development of technology.

However, there are many approaches that can be used to construct a fall

detection systems. The first one is based on image and video system and the second

one is based on accelerometer sensor. The later is our concern in this study because

with the rapid advances in wireless network and tiny sensors they can be embedded


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easily in the mobile devices which can be carried anywhere. Moreover, recently,

smart phone applications are being developed faster and faster. Therefore, with a

phone equipped sensors, researchers have developed applications for fall detection

based on accelerometer sensor. These applications are mainly developed based on the

threshold algorithms, some works involve the use of machine learning techniques for

the detection of fall and movement classification. This enables the elders status to be

monitored anytime and anywhere and aided when necessary.

In this thesis, we study the overview about fall and non-fall from our test

scenarios to build a standard database for fall events which the previous researches did

not mention. Two types of experiments were investigated. Type A with ten young

people performed both falls and non-fall .Type B with ten people including the elder

people and children performed non-fall. They include: forward falls, backward fallsand lateral falls right and lateral falls left, performed with legs straight and flexed in

fire middle aged subjects, etc... Based on the standard databases, we conducted to

compute the typical attributes of accelerometer for classification, namely length of the

acceleration vector, the variance of the length, speed of change in acceleration along

the z-axis, the acceleration vector changes, and the accelerometer inclination angles.

Thanks to this, we evaluate the performance of classification methods for the

application of fall detection.

The remaining of the thesis is organized as follows: Chapter 2 presents a

detailed overview about of related work on recognition activities and fall detection. In

addition, Chapter 3 describes the design of a simulation system for fall detection in

detail. Firstly, in order to get data to train the classifier, we design a number of dataset

collection forms to extract features and assign their labels automatically. Chapter 4 we

present an experiment to emphasize the importance of feature extraction in fall

detection. Besides, we described the process of data collection and the construction ofaccelerometer sensor system and evaluate its performance using machine learning

algorithms.


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Chapter 2

Related Work

Accelerometer and gyroscopes are the two most popular methods in most of

the research on fall. Threshold for acceleration, the change of velocity and angles

were usually applied in some researches. By applying a simple threshold to the

acceleration and using a tri-axial accelerometer worn on the chest, Lee, Nguyen, Cho

(2009) detected falls with 98. Bourke and Lyons (2008) usedbiaxial gyroscope worn

on the chest, they applied threshold to the peaks in the angular velocity, angular

acceleration and the change of angle.

In addition to applying threshold for fall detection, machine-learning algorithm

seems to be a good method instead of threshold algorithm algorithm. One example for

using machine learning is Shan (2010) and Yuan and Zhang (2006), both studies

which used tri-axial accelerometer wornon the waist. They used SVM algorithm to

classify various features from accelerometer sensor data.

Especially, Particular interest to us is research of Lie (2009). Although their

result of fall detection is lower than previously research, it may be due to the more

experimental data. They apply threshold, angles and velocities. Based on this,

potential fall and the activity will be detected after fall. Their methods sometimes do

not lie down quickly. And these situations will be addressed by the classify

accelerometer sensor on this thesis.


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2.1. Fall Detection using 3D Head Trajectory Extracted from aSignal Camera Video Sequence

Falls are one of the greatest dangers for elderly people living alone. They may

be unconscious after suffering the fall. Computer vision system provides an automatic

solution to overcome the limitations of researchers. Some research has developed fall

detection system by using the image sensor. One example is the work of Lee and

Mihailidis who detect falls by using a camera mounted on the ceiling determine the

specific silhouette [14]. This is a new method using 3D data of the head trajectory for

monitoring the movement of the fall.

The method of this research will be based on three steps:

Head tracking: because of the fact that the head can be often seen in thepicture and there is a large movement during a fall

3D tracking: the head is monitored with a particle filter to extract a 3D

trajectory

Fall Detecting: a fall is detected by using 3D velocities which are computed

from the 3D trajectory of the head.


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Figure 2-1: Reading trunk pitches rolocity V v and horizontal velocity Vh. These

velocities were obtained without markers from the 3D trajectory of a person who

brutally sits down and falls. The different action are: the person(a) stands

up,(b)sits down, (c) is seated, (d)stands up again, (e)remains stand up and (f) falls

This method required a set of video in various situations such falls and normal

situations like sitting down or squatting. Based on the characteristics of the image

sequences, the result will be showed after analyzing with OpenCV library (Intel Open

Source Computer Vision Library)

The result obtained in this research is quite high. However, in some cases it

depends on the location and specific circumstance. Camera is not always everywhere.

Moreover, using camera usually makes the user feel unnatural and lose freedom in the

daily activities. It affects the privacy of individuals. Consequently, using accelerationsensor for fall detection is an effective approach to ensure personal information can be

kept secure and applied in every situation.


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2.2. Fall Detection using accelerometer sensorAccelerometer usually provides the acceleration readings in direction of x, y, z

axis. Accelerations are these directions are presented by xA , yA , zA respectively. As

illustrated inFigure 2-2

Figure 2-2: Acceleromter Sensor

X-axis has positive direction toward the right side of the device

Y-axis has positive direction toward the top of the device

Z-axis has positive direction toward the front of the device

Noury and Rumeau indicate two approaches to detect fall in their research [4].

The first place, which is the more common approach, is an analytical method and the

second place is with machine learning techniques. An example for machine learning

approach is the research ofMitja and Bostjan [3].This system is a visual basic system.

By using markers, they tagged different point in body of users and used it as reference

points. They found that the angle and the reference points between them is very

reliable data source for extracting features. With other machine-learning algorithm,

Support Vector machine (SVM) was the best choice for research and followed by

Random Forest.

In our research for fall detection is mainly based on the machine learning

techniques and some use analytical models.


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2.2.1 Analytical and Threshold Method

Analytical method is based on the manual or research experience about data

sets and set-up our own parameters (thresholds). There is a need for a thorough study

of falls and ego stage for better understanding and results. What this study focuses onis presented in this section.

With fall detection - principles and methods [4], many falls end lying on the

ground, the simplest approach is to detect the lying position, from a horizontal

inclination sensor. This method is very suitable for monitoring an "isolated a person",

but less suitable for the detection of falls of an elder person such as the irregular

sleeping hours. Therefore this method is prone to many "false positives", i.e. detection

as falls of situations that are not falls.In fall detection system, we can use fall velocity, body orientation, body

posture, angular acceleration, angular velocity and fall acceleration in the critical

phases as the determining factors for distinguishing fall and non-falls activities (ADL

and ADLS).

The simplest approach to detect a fall is by detecting the lying position (GPS).

This is based on the fact that most falls, but eventually not all of them, end up falling

in this lying positing. Both researches of StanKovic, Noury and Fleury indicate thatindirect detection of the lying posture during post-fall can be called [1], [2]. That when

the foot is no longer in contact with the ground also can be another method to detect

fall.

2.2.1.1. Human fall detection algorithm using bi-axial gyroscope sensorA threshold-based algorithm to distinguish between Activities of Daily Living

and falls is described. A threshold-based fall detection algorithm using a bi-axial

gyroscope sensor is used[13]. With trunk pitch and roll gyroscope are read during

separate simulatedfall and Activities of Daily Living (ADL) studies, the result will be

compared through a threshold-based algorithm


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.

Figure 2-3: Reading trunk pitches and roll gyroscope

This research focus on studying about the ADL study, Data acquisition set-up,

sensor location and signal conditioning. In signal conditioning, low-pass filter would

be used a second-order low-pass Butterworth 2-pass digital filter, with a cut-offfrequency of 100Hz for each pitch and roll angular velocity signal. The resultant

vector was derived by from taking the root sum of square of roll and pitch angular

velocities.

Fall detection will apply a threshold to the Peak value of lowest fall and highest

ADL from the resultant angular velocity signals. The result of recording data will

result in one of two scenarios:

Figure 2-4: Overlapping and non-overlapping sampe Fall and ADL data

InFigure 2-4(A) the peak values recorded ADL will not overlap with the

recorded fall peak. Falls from ADL which may be distinguished by a

single threshold, level of which would be placed at the lowest fall peak

value.


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In Figure 2-4(B) the peak values recorded ADL will overlap with the

recorded fall values. In this case applying one threshold is not sufficient

to distinguish falls from ADL, so continuing to investigate additional

aspects of the signals is required.

There are three thresholds to determine for a fall which can be distinguished

from an ADL. If the resultant angular velocity is greater than 3.1 rads/s (Fall

threshold 1), the resultant angular acceleration is greater than 0.05 rads/s (Fall

threshold 2) and change the result in changing trunk angle is greater than 0.59 rads

(Fall threshold 3), a fall will be detected. The results indicate that falls can be

distinguished from ADL with 100% accuracy for a total data set of 480 movements.

Figure 2-5: Bi-axial gyroscope fall-detection algorithm flow chart


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2.2.1.2.Human fall-detection algorithm using tri-axial accelerometer sensorMost previous researchers and others combined the devices total acceleration

from the X, Y and Z axis [10]. This is called Root Sum of Squares or RSS which is

presented by the formal:2 2 2

RSS x y z (2.4)

This is sometimes referred to as the dynamic total acceleration2

9.81 / dRSS RSS m s

Checking the acceleration in early part of the critical phased is the simplest

algorithm for fall detection. When standing upright, we will have a total acceleration

(RSS) of 1G. This value will drop to 0G in free fall until finally reaching impact.2

1 9.81 / g m s

This method will use a threshold which is set based on training session. There

are two thresholds to compare with data from ADLs or other non-fall activities. There

are called Lower Fall Peak (LFP) and Upper Fall Peak (UFP). In training period, LFP

and UFP will give the Lower Fall Threshold (LFT) and Upper Fall Threshold (UFT),

respectively.

Figure 2-6: Fall detection algorithm operation example for upper and lower fall

threshold, using an artificial example signal

Normally, in some case, UFT usually have a better result than LFT.


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Profiling algorithm in [11] is also to measure the falling edge time and rising

edge time. With the falling edge time ( FEt ) is the time when the RSS signal last goes

below the LFT until it reach the UFT. The rising edge time ( REt ) is always a subset

and smaller than the falling edge time ( FEt ).A specify example, we can imagine thatwith t0 the value of RSS first will go below the Lower Fall Threshold (LFT) and keeps

under it until0 2 00ms

t . The time from 0 20 0mst to 0 5 00 mst the RSS value will increase

until the Upper Fall Threshold (UFT) reached. UFT will reach to peak in 0 5 00mst .

0 500 0mst t is the galling edge time ( FEt ) and 0 500 0 200ms mst t is the rising edge time. In

addition we also check for the profile LFT + the falling edge time ( FEt ) and + the rising

edge time ( REt ).Both LFT and UFT are a prerequisite for using the falling edge time

and the rising edge time. However, profiling algorithms only use LFT and UFT can be

further expanded to also use the falling and rising edge time.

There are some algorithms that is similar with profiling algorithm such as in

[12].this algorithm use the time window to find the UFP and LFP and take the

difference between UFP and LFP ( UFP LFPRSS ). If both of them reach to their respective

threshold and LFP happens before the UFP. Fall will be flagged.

UFP LFP UFP LFPRSS t t

with t = time of occurrences (2.5)Improving the result of a fall detection system is extreme important so many

algorithms check the body posture to improve on the specificity. Most of these

algorithms assume with many case of posture such as making a threshold similar to

UFT and LFT when the body is sitting or lying or running. We will compute the

Lower Sitting threshold and the Upper Sitting threshold. After achieving both LFT and

UFT 10 or 15 seconds, if both Lower Sitting threshold and Upper Sitting threshold are

achieved, fall will be flagged.

Another way for falling detection is measuring the velocity which is computed

by the formula.

dRSS dt (2.6)

The velocity will be added in the profiling algorithm and based on the integral

from the time of the start of a fall until impact.


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In addition, we can also use the fall index such as in 2.7 or make fall thresholds.

Especially, we can also make use both in three different types of algorithm.

First algorithm checks posture and impact. With impact, it will be based on Z2,

UFT, dUFT or UFP LFPRSS .The second algorithm uses LFT + UFT (within frame of 1 second) or threshold

based on Z2 + monitoring.

The third algorithm uses LFT + threshold with the velocity and UFT (within

time frame of 1 second) or threshold based on Z2 + monitoring of posture.

Postured will be detected 2 seconds after the impact using the vertical axis

acceleration. The sitting and lying posture are usually lower than 0.4G. The

result of the first algorithm when using Z2 based threshold + posture or UFT isusually 97%. The fall index will be calculated by the formula:

2

1

, , 19

(( ) ( ) )i

i k i k i

k x y z i

FI A A (2.7)

Where x, y, z = acceleration from the X, Y and Z axis

2 2 2RSS RSS G

22G

dZ (2.8)

The scalar product of two acceleration vectors can also measured for Posture to

fin the angle between them. It maybe is between the current gravity vector and the

reference gravity vector or the gravity and the vertical axis (Ay).

arccos|| || || ||

referemcegravity currentgravity

referencgravity x currentgravity (2.9)

arccos|| || || ||

d

y

A gravityA x gravity

(2.10)

Some devices have orientation sensors or more accurate gyroscopes which then

can be used instead [10]. Both formula 2.5 and 2.11 are used.

| sin sin cos cos |x z y y z y z

Orientation A A A (2.11)

Where x , y , z = the data from the orientation sensor


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X is the front-back axis, y is the horizontal axis (left and right) and z is the

vertical axis (up and down).

2.2.1 Learning Methods for fall detectionMachine learning can be broadly classified into two fields, supervised learning

and unsupervised learning. In front, the machine will attempt to identify the groups of

similar data from a larger dataset. Besides, it also tries to from clusters of data based

on some criteria such as cost function. It has not prior knowledge of the data layers. It

only tries to identify natural clusters or groups of data. With supervised learning, it

learns from the test containing classified data and predicts data layers invisible. With

Fall-detection system, it will be more natural when using supervised learning

techniques.

Without any analytical algorithm, we can still carry out an intuitive approach

the development of machine learning based fall detection systems from a training

period and then classification. However, It may be is necessary to establish criteria for

classification.

The most important in supervised learning is the quality of accurate and

exhaustive of training data. Because of these reasons, in the period of training, it is

important to be able to simulate how falls as close and how the real falls for othergroups of users who will use the fall detection system.

With some machine-learning algorithm out there, it may be cause confusing

about selecting the right one for certain applications. Even thought, some research also

studies about comparing the result of them to try to choose the best algorithm [18],[19]

To sum up, a developer can be chosen an application method that is suitable with his

(her) own discretion. An evident with fall detection system is a sensitive application. It

is advised to compare and test between a numbers of algorithms to try picking the bestchoice.

Ralhan in University of Saskachewan compared five different supervised

learning methods for fall detection system [6]. Naive Bayesian classifier, Radial basis

Function (RBF), C4.5 and Ripple down Rule Learner, Support Vector machine are

used. Eight scenarios (four falls, three ADLS and one near fall) are used to getting data

from test the algorithms against. With Nave Bayes classifier, the highest result is 97%


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and taking the least time for building model and then it was chosen as the best choice

for research.

In addition, Fall detection by embedding an accelerometer a method that of

Ralhan in University of Saskachewan used with machine learning algorithm [5]. Theyused the group integrated a device with tri-axial accelerometer, a MCU device and

some other peripherals with a mobile phone. One class SVM for preprocessing the

signals and KFD (Kernel Fisher Discriminant) and K-NN (Nearest neighbor) was used

with fall detection for precise classification. The result of research is 92.3% for a

limited number of cases.


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Chapter 3

Fall Detection Process

In the period of studying for fall detection and the installation of experiment,

we found that the fall detection algorithms based on threshold still have the following

limitations:

They have not been built a specific training data on typical actions falls.

The techniques of experimental process based on the characteristic time

domain and or frequency domain were not mentioned. They have not

been proper interest in these methods.

In addition as mentioned in the previous chapter about the theory of fall

detection. In the data collection, human undesired movement can cause

large changes in intensity dramatically at that moment. It affects directly

on the result obtained when comparing with the threshold.

To solve the above disadvantages, we propose the method to extract feature

combined building layer model for fall detection system. In particular, the model will

be proposed for using extracted techniques based on the information on the time

domain and the characteristic value in accelerometer sensor. The following thesis will

present general model for classification


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Figure 3-1: The general model cycle

The components constitute the layer model using in this thesis. This model isbased on the criteria that the thesis proposed to decrease limitations of the above

methods. Accelerometers data will be collected directly from the user through specific

cases which described. Next part is to expand intensity and assign label for datasets. In

this part, some other information of accelerometer data on the time domain will be

extracted which are used in the classifying phase.

3.1.Accelerometer Data collectionIn this section, we introduce first how the data collected. After that, we will

present fall detection performance achieved by different algorithms. We collected data

of falls from different directions (x, y, z axes) and different environment (Bedroom,

kitchen, outdoor garden, living room) with other cases of fall. The data from activities

of daily living (ADL) such as walking, jogging, sitting or standing are also be

collected. This below sample data acquired from accelerometer and fall annotation for

a single fall:


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Table 3.1: Sample data acquired from acceleromter

x-axis y-axis z-axis time

0.459687 1.37906 9.80665 57223591098406

0.459687 1.532289 9.346964 57223619296739

0.153229 1.225831 9.95987 572236779434050.459687 1.225831 9.040505 57223741578404

-0.30646 0.459687 10.72602 57223799416739

0.153229 0.459687 9.500193 57223862593408

0.153229 0.153229 9.500193 57223921180075

0.153229 -0.45969 10.5728 57223982330075

0.153229 -0.30646 9.500193 57224039275074

-0.15323 -0.76614 9.193734 57224100655074

-0.1533 -1.53229 9.346964 572241619600720.612916 1.225831 9.959879 57223529871740

0.459687 1.37906 9.8066 57223591098406

0.459687 1.532289 9.346964 57223619296739

0.153229 1.225831 9.959879 57223677943405

Figure 3-2: Fall annotation for a single fall

In getting data for fall detection system, the cell phone was put in the shirt

pocket of participants. In each case of position, every participant falls 7 times in

different directions and environment. To sum up, we recorded 5 examples of the

behavior from 15 persons and collecting Activities of daily living for 2 or 3 minutes


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from each person with some cases such as sitting, running, lying or walking. Mitja and

Igone [17] also gave some multiple activities:

3 x 15 recordings of falling, consisting of standing/ walking, falling and

lying. Fall detection is one of the main goals of our project.3 x 10 recordings of lying down, consisting of standing/walking, lying

down and lying. Lying down is same as falling down, so we wanted to

verify whether the classification can distinguish between the two.

3 x 10 recordings of sitting down, consisting of walking, sitting down

and sitting. Sitting down may also resemble falling and is a common

action .It is important for the analysis of the users behavior.

3 x10 recordings of walking. Walking is also popular and we wanted

clearly examples of it to test the classification. Recognizing word using

trial cutting and beam search

3.2.Algorithm for Fall Detection

Figure 3-3: Fall Detection Flow Chart

Figure 3-3 describes a framework for processing data. It is namely pre-noise

processing data, attribute computation and feature selection. Especially, Featureselection is an important prior step to any classification problem which reduces the

dimensionality and thus the amount of data required for training. They will be

presented as follows:


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3.2.1.Pre-noise processing dataBecause the data is received from sensor (usually is noisy data), so the

technique for filtering data was applied. There are several techniques that perform

signal filter. In this thesis, we applied a low-pass and high-pass filter.

A simple low-pass filter for the time domain is a smoothing function. In other

work, the signal is smoother and less dependent on short changes. We use low-pass

filter to reduce the influence of sudden change on the accelerometer data.

It also can filter a series such as the low-frequency variations and reduced and

the high-frequency is unaffected. This type is called a high-pass filter. This is

particularly important in the acceleration data. This type also allows us to remove thegravity component and take into consideration the sudden change in acceleration.

The below algorithm shows the low-pass and high-pass filter that we use in this

thesis. It uses a low-value filtering factor to generate a value that uses 20% of

the unfiltered acceleration data and 80% of the previously filtered value. This

factor was chosen empirically.

Figure 3-4: Low-pas and high-pass filter algorithm


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3.2.2.Action SegmentationIn this section, we describe the process of action segmentation. After that, these

attributes are combined to create the final attribute vector for using in the machine

learning classification. Sliding window is a common approach to solve the problems offall recognition. Normally, the algorithms do not attempt to recognize the pattern that

is received from sensor but trying to recognize some patterns in the data which is over

time in the window.

2 4{ ,..... }

t w t w t I I I

With:

W is the size of sliding window

t is the index of current aceleration

When analyzing series of time, we chose a window size of 10 which is one

second time interval. We decided for one-second time because some transitional

activities usually last from one to five seconds.

3.2.3.Feature ExtractionTo detect fall based on accelerometer sensor with machine learning algorithm,

we will use a sliding window to transform stream of acceleration data into instances

for machine learning. The following attributes were derived from the data within

sliding window. We will against on some method to calculation:

Length of the acceleration vector

The variance of the length of the acceleration vector within the window

The average acceleration along the x, y and z axes within the window

The speed of change in acceleration between the maximum and minimum

along the x, y, z axes.

The maximum and the minimum acceleration along the x, y, z-axes

The acceleration vector changes (AVC)


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The accelerometer inclination angles

3.2.3.1. Length of the acceleration vector

The first computed attribute is length of the acceleration vector. It is a simplebut it is very useful attribute. Moreover, it is also used further in the process of

extraction the new attributes. It is not used as a separate attribute in the final vector

because of the sliding window technique. Its definition is:

2 2 2RSS x y z

Figure 3-5: RSS plot of a forward fall

During static posture this attribute is constant with the value equal to the

Earchs gravity (RSS = 1g). In dynamic activities the acceleration vector is changing

the direction and its length.

3.2.3.2.The variance of the length of the acceleration vectorWith these characteristics, based on the average, maximum and minimum

acceleration along the y-axis, we compute the variance of the length of the acceleration

vector. The variance with in sliding window will be defined as follows:

2

2 1

( )n

i

i

a a

N(4.1)


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With N is the number of acceleration data within the window,i

a is the length

of the thi acceleration vector and a is the average length of the acceleration vector of

the person)

3.2.3.3.The speed of change in acceleration along the z-axisThe speed of change in acceleration along the z-axis within a window was

defined as follows:

max( ) min( )tan

(max( ) (min( ))

z z

z

z z

a aspd

t a t a(4.2)

For this attribute, the value of raw materials for the length of the accelerationvector is used instead of low-pass value .Max ( za ) and min ( za ) are the maximum and

minimum acceleration along the z axis within the window, and t (max (z

a )) and t (min

( za )) are the time stamp of the data.

3.2.3.4. The acceleration vector changes (AVC)When the person's body is static, single accelerometer response only to the

gravity, producing a constant length of acceleration is 1g. In the moving accelerationsproduce a changing in acceleration signal and drastically change the motion. Using

changes in the acceleration vector, an attribute is calculated to detect the motion

accelerometer: Acceleration Vector Changes (AVC). The AVC value of this attribute

increased as the acceleration (walking, going down, stand up, etc ...). This attribute

take into consideration the data from the current window (ten data samples). It

combines ten different length of the vector of length acceleration vector and divided

the sum by the time interval (one second) of data. The AVC is computed as followed:

1

1

0

| |n

i i

i

n

length length

AVCT T

(4.3)

0T is the time stamp for the first data sample in the window, and nT is the time

stamp of the last data sample. The fall can be detected by using this attribute. With this

attribute, the movement of people can be detected: it distinguishes static from the

dynamic active. For this attribute, the value of raw materials for the length of the


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acceleration vector is used instead of low-pass value. The reason for this is that we are

more interested in small changes in the acceleration signal and the smooth of low-pass

filter.

3.2.3.5. The accelerometer inclination anglesOther important features to be recognized as a static body posture are the

orientation angles of the accelerometer. The orientation angles are calculated as the

angles between the actual acceleration and each of the axes (x, y and z axes). (Figure

3-6)

Figure 3-6: Accelerometer inclination angle

For instance, the angle x between the acceleration vector and the x axis

(perpendicular to the ground) is computed as follows:

2 2 2arccos( )x

x y z

ax

a a a (4.4)

Where the value ax, ay, az represent the actual acceleration vector. In this

attributes, lower-pass filter should be used. Because of the changes of the angle is less

and less variation. Without the low-pass filter, the angles were sensitive angles to each

small change of the accelerometer. These angles improve the classification of activities

that have different accelerometer angle inclinations.

3.2.4.Forming feature vectorIn our work, feature vector is formed for each sliding window (sub-regions) in the fall

data sequence and thus, each sequence is represented by a set of feature vector. The

process in which feature are formed is illustrated in the following:


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From each input sample, we use low-pass and high-pass filter as described in

section 3.2.1

Each feature is then divided into 10 non-overlapping sub-regions.

The value of attributes in section 3.2.3 is calculated for each sub-region.

The feature vector is created by concatenating all attribute computed from sub-

regions ( each sliding window)

3.3.Action learningIn this thesis, we make use of the famous Support Vector Machine (SVM) for action

learning and recognition.

3.3.1.Support Vector Machine (SVM)Support Vector Machine is one of the most famous machine learning techniques

which has been used widely in not only Computer Vision but also in Artificial Intelligent and

other fields. Especially, with classification, SVM is the tool for finding good hyperplanes for

separating different classes of instance.

Considering binary classification problem with L training point { ix , iy } where:

1, 1( ),....,( , ) {1, 1}

m mx y x y X

and test set1,.....,

mx x X

With a given training set1 2{( , ) | 1... }, ( , ,... ) , { 1, 1}

n

i i i i i in iD x y i m x x x x R y which is

separable, we need to find a hyper plane which separates two object classes +1, -1 and

predicts bets with data not in the training set. Figureillustrates a hyper plane separating two

types of data objects.


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Figure 3-7: Separating hyper plane

Optimal hyper plane has the equation:

0)(1

m

i

ii bxwbxwxf

Satisfying constraints:

mibxwyi ,...,1,1)(

Then, an objectx is classified into +1 class iff(x) 0 and into1 class otherwise.

The distance from margin to optimal hyper plane is w1

. The problem here is to find w

so that ||w|| has a minimum value satisfying constraints ,1)( bxwyi i= 1 m

with the hope that the larger margin, the better classifier.

This problem can be solved by solving its dual problem. Lagrangian for the

primal problem is:

m

i

iii xwywwbwL

1

]1)([

2

1),,( (3.3.1)

where i 0 is Lagrange multiplier. We can transform the primal into a dual

by simply setting to zero the derivatives of the Lagrangian with respect to the primal

variables, and substituting the relations so obtained back into the Lagrangian, hence

removing the dependence on the primal variables:


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26

m

i

ii

m

i

iii

yb

bwL

xyww

bwL

1

1

),,(

),,(

Then, we have:

01

1

i

m

i

i

m

i

iii

y

xyw

Replace them into (3.3.1), we have:

m

i

iii xwywwbwL1

]1)([2

1),,(

m

ji

jijiji

m

i

i

m

i

i

m

ji

jijiji

m

ji

jijiji

xxyy

xxyyxxyy

1,1

11,1,

2

1

2

1

The formula above is the dual representation of primal optimization problem. In

optimization theory, solving primal problem is equivalent to solving its dual problem.

Assuming * is the solution of the following dual optimization problem:

Maximizei ji

jijijiiD xxyyL,2

1

where i 0, i = 1m and 01

i

m

i

iy

Then vector of weights ism

i

iii xyw1

** and

Ii

n

j

jijji xxyy

I

b1||

1

whereIis set ofi so that i > 0. The classification function is sgn(f(x)) where:

*)(*

bxxyxfIi

iii


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3.3.2. LearningIn this work, we also apply grouping technique for the learning phase:

Assume training example iX has iN sub-regions which are represented by iN different feature vectors. Each of these feature vectors will be labeled with the corresponding

class of the sample. Therefore, with n samples of two action class (fall and non-fall), a

training set D is described as:

{ , , }iNn

ij ij ij i

i j

D x y y y

With:

ijx is theth

j feature vector of the thi example

ijy is the label for

ijx

iy is the action class of the thi example ( {0,1}iy )

.


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Chapter 4

Experimental Setup and evaluation

This chapter presents the experiments we carry out to build the standard

datasets for fall detection and analyze the performance of the fall detection method. In

this section, we first describe how we set up our experiments, explain the choice of

parameters for fall detection and finally show the result of our method in comparison

with other related methods.

4.1. Experiment SetupThe following briefly presents the basic concept of fall in our daily activities and

describe the process of data collection which is used in our experiments.

4.1.1.Background of fall and non-fall actionAs mentioned in Chapter 2, we have presented some fall detection system that

uses various approaches in the study. Their disadvantages and advantages were

discussed. There is need for a precision of fall detection system that wishes train and

test. Because of this, studying for falls is an important issue with the thesis.


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4.1.1.1.Study of fallDefinition a fall

Many definitions about a fall are given. This is because fall for one specific

group of users such as young people, elder people or different other groups. This thesis

focuses on distinguishing fall and non-fall activities (it may be called Activity of Daily

Livings or ADLs). Now as mentioned, types of fall of each group of people always are

different (elderly people or childetc).

Figure 4-1: The stage of fall

In our thesis, we will distinguish between two kinds of falls. The first place is

falls which are caused by external factor (such as behavioral or environmental). The

second place is falls that caused by internal factors (such as biology).

Lying down/ Standing/ Walking

Fall Forward/Backward/Left/Right

Near Fall

Cause of falling

The normal changes of aging, like poor vision and poor hearing. They can make

you more likely to fall.Diseases and physical conditions can affect your strength and

balance.

There are some risk factors for cause of falling. In this thesis, we divide these

factors into intrinsic (biology) and extrinsic (behavioral and environmental). We use

both of factors to detect falling.


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Firstly, biological factors may be are:

1. Age2. Medical condition such as Parkinsons disease, patients.3. Muscle Weakness4. Visual impairment.5. Foot problem.

Secondly, Behavioral and Environment factors are:

1. Sedentary2. Medication intake.3. Alcohol misuse.

Beside two types of groups for falling, we also research some trends of falls.

They are as follows:

1. Time2.

Climate/weather

3. Location4. Race5. Depression

Phases of falling

There are four phases of falling: the pre-fall, the critical phases, the post-fall phase

and the recovery phase

Figure 4-2: The four phases of a fall event


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The pre-fall phase corresponds movement of daily life, sometimes with

sudden movements directed towards the ground such as sitting down or

bending over. These activities should not create an alarm with a fall

detection system.

The critical phase, corresponding to the fall, is highly short. This phase

can be detected by the motion of the body toward the ground or by the

shock of impact with the floor

The post-fall phase is usually characterized by a real person on the

ground immediately after the fall. It can be detected by a lying position

or by an absence of significative motion

A recovery phase may eventually occur if the person can stand alone or

with the help of another person

4.1.1.2.The simulated-fall studyThe simulated fall study related to healthy young subjects and carrying out

under the supervision. Tri-axial accelerometers signals recorded from the trunk and

thigh in a fall event simulation. Each subject performed eight different kinds of fall

and each type was repeated three times.

The fall types used in the testing process for the current study were selected tosimulated common types of fall in elderly people. The most common causes of falls

are the trips, slips, and loss of balance. ONeill et al.[20]indicated that 60% of falls in

older people were falling in the forward direction. Laterally directed fall also were a

major pose of threat. Laterally directed fall causes the bad impact such as the potential

to fracture every time it happens. Therefore, falls from all directions should be

considered when validating fall detection system. We also should attempt to make

mimicking the realistic falls. Thus, simulation of performance were backward falls,

lateral falls right, lateral fall left and forward falls.

4.1.1.3.The ADL study (Activity of Daily Living)The second study involved older people performing ADL, in their own homes,

while equipped with the same sensor configuration. Ten subjects of the elderly

community, four females and six males, were monitored. They ranged in age from 60

to 80 years old. Each ADL was performed three times by each older person. The ADL

were a task that could produce impacts or sudden changes in movement and result of a


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mistake caused by a threshold-based fall detection algorithm. This below picture

shows activities of daily living:

Figure 4-3: Two types of non-fall(ADL)

The second study involved every activity of living from children to elderly

people. Each ADL was performed three times by each person. To evaluate fall

detection algorithm, Bourke, OBrien and Lyons showed some types of ADL in their

research [7]:

1 Sitting down and standing up from an arm-chair2 Sitting down and standing up from a kitchen-chair.3 Sitting down and standing up from a toilet seat.4 Sitting down and standing up from a low stool5 Getting in and out of a car seat6 Sitting down on and standing up from a bed7 Lying down and standing up from a bed8 Walking 5m9 Cycling


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4.1.2.Data CollectionWith intent and purposes of the system that we want to make. We define a fall to be a

sudden change of body position coming to rest on the ground, it does not include intentional

change, and then is inactivity. This means that the most serious falls where user loses a

balance after hitting the ground. They also lose the ability for getting help.

To have database to train and test the character recognizer, we designed a

number of this scenario was designed to investigate the events which can be difficult

to recognize such as fall and non-fall. It includes 9 different events which are showed

in Table 4.1. They are recorded in single recordings that it interspersed with short

periods of walk, each record lasted from 3 to 5 minutes. An example for specific

image of the event can be view inhttp://dis.ijs.si/confidence/iaai.html
http://dis.ijs.si/confidence/iaai.htmlhttp://dis.ijs.si/confidence/iaai.htmlhttp://dis.ijs.si/confidence/iaai.htmlhttp://dis.ijs.si/confidence/iaai.html


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Table 4.1: Events Sequence in the test Scenario

No Description Fall Ending position

1 Sitting down normally on the chair No

2 Tripping, landing normally on the

bed

Yes

3 Lying down normally on the bed No

4 Falling slowly( trying to hold onto

furniture),landing flat on the ground

Yes

5 Sitting down quickly on the chair No

6 Falling when trying to stand up,

landing sitting of the ground

Yes

7 Lying down quickly on the bed No

8 Falling slowly when trying to stand

up(trying to hold onto furniture),

landing sitting of the ground

Yes

9 Searching for something on the

ground on all fours any lying

No

Some types of falls were selected from a list of typical falls. As shown in the

choosing on related work. Accelerometer sensor can accurately detect typical falls.

Because of this, we gave a fall (such as event 2) to prove that the system can recognize

.In addition, we also choose three atypical falls (event 4, 6 and 8) to test the use ofinformation in each circumstance. A specific example that a person is not expected to

lie or sit on the ground (in contrast with the bed or the chair). They are atypical in

speed (event 4 and 8) and starting/ending posture (event 6 and 8). With each subject,

we would repeat 2 or 3 times for each. Therefore, there were about 300 sequences of

data (120 fall data and 180 ADL data)


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The experiments with groups of real persons are conducted. Both activities of daily

living and falls are tested and training. However, the fall situation is dangerous to

human body, especially to the elderly people, so we cannot test falls with elderly

people, the elderly volunteers only attended with non-fall (ADL). We received data

from 2 groups of subject:

Ten young subjects (6 male and 4 female, age 256 years, body mass

5311.5 kg and height 16610.5 cm)

Table 4.2: The detail of ten young volunteers

Volunteers Age

(years)

Weight

(kg)

Height

(cm)

Subject 1 22 53 169

Subject 2 19 54 168

Subject 3 33 58 164

Subject 4 25 56 165

Subject 5 28 63 174

Subject 6 21 55 170

Subject 7 25 59 166

Subject 8 23 60 171

Subject 9 22 50 167

Subject 10 22 58 165

Ten elderly subjects and children (6 subjects for age 646 years, body mass

525kg, and height 1645cm,4 subjects for age 104 years old).


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4.2.Performance measuresFall detection is either positive if the detector properly recognizes a fall event or

negative if it does not. The quality of fall detection cannot be evaluated from a single

test; instead, it is necessary carry out from a series of test. It will include four possible.

True positive (TP): fall occurs, the device detect it

True Negative (TN): a normal movement (non-fall) is performed. The

device does not declare a fall.

Fall Positive (TP): The device declares a fall, but fall does not occur

Fall Negative (FN): a normal movement (non-fall) is performed. The device

declares a fall.

Based on four situations, we proposed two criteria to evaluate the fall detection

system:

Sensitivity is the capability to detect a fall. It can be expressed:

ensTP+FN

TPS itivity

Specificity is the capability to detect an ADL (Activity of Daily Living). Itcan be expressed:

TNSpecificity

TN FP

4.3. Experimental results of the fall detection systemWe tried various machine learning algorithms to train classifiers for classifying

the fall and non-fall. In our experiment, 20 volunteers were selected to attend in the

experiments. The detail of them was mentioned in the previous section. The

experiment was performed with the scenario in Table 4.1.From 300 samples received

from the testing scenario, we conducted experiment with four categories. With these

samples, we randomly selected 3/4 of samples for training and other model for testing.


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It was classified with libSVM function in the software of toolkit WEKA. As shown in

Table 4.3 is the detail of four categories.

Table 4.4: The distribution of the samples

Category total samples samples for

training

sample for

test

1 90 66 24

2 70 52 18

3 60 45 15

4 80 60 20

The test results are shown in Table 4.5, and the mean ratio of accuracy mr is 92.07

percent, where:

1

1

Nm i

i

r r

N is the number of categories, ir is ratio of accuracy ofthi category.

Table 4.6: The test result

Category 1 2 3 4

Accuracy (%) 91.36 94.0 90.32 92.6

Sensitivity (%) 91.3 94 91.3 92.6

Specificity (%) 91.4 93.9 90.3 92.6

F-measure (%) 91.3 93.8 87.7 92,6

When using an accelerometer for fall detection, the machine learning based

methods somewhat outperformed threshold algorithm. Considering the simplicity of

these methods, threshold may be simpler than machine learning methods, but the

related work shows that in some case, machine learningbased methods gave the better

result because this may seem surprising, but the related work shows that threshold-

based methods were apparently able to learn the pattern of acceleration during the

falling.


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Chapter 5

Conclusion

Because our goal is to detect fall, most of the non-fall (ADL) we have obtained

samples are more specific than daily activities (such as cooking, sleeping). In other

words, the probability of fall-actions is higher than in everyday of life. Compare with

other methods in [21]. The result of our study is more specific. The correct ratio is

94%.

This thesis studies to build a standard database and typical attributes for fall

events based on accelerometer sensor. We studied the fundamental knowledge about

fall detection such as data feature extraction, machine learning models, support vector

machine and how to combine them.

This thesis has achieved the following results:

Study to build a model of fall detection based on accelerometer sensor

Studying and learning the typical case of falls, the processing techniques and

the characteristic of the accelerometer sensor are used.

About theory and methods, in terms of the thesis, we explore several detection

techniques identify different falls including pre-processing techniques

accelerometer sensor, extract selected characteristic, standardized features and

classification techniques using in signal classification problems.

Experimentally, based on the study of acceleration sensor and the method of

digital signal processing, we have built and installed four experimental models.


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In particular, we proposed a model to improve the accuracy of fall detection

based on accelerometer sensor to ensure objectivity, the proposed model was

compared with the evaluation results of other fall detection algorithms in

previous studies.

In the future, we will continue to improve the recognition quality with a large

enough data for a fully evaluated general methods and experimental models.

Especially, we also try to fix the standard signal from many different users more fully.


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Appendix A

A. Fall Scenarios1. Backward Fall

2. Fall to the Left

3. Fall to the right


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4. Fall to the right

5. Forward fall collapse

6. Forward fall collapse


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B, ADL (Non-Fall)

1. From standing position, sit down (normal speed) on chair , remain 5 secondsand then stand up

2. Kneel down and pick up an item

3. Standing position, bend-down and pick up an item on the floor. Rise-up


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4. Standing position, quickly fall down facing the floor, push up 3-5 times then riseup

5. Lie down and remain 5 seconds. Rotate 180 degree, remain 5 second , then rise up

6. Standing position, back to original position

7. Standing position and jump


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8. Standing position and run from A to B.

9. Lie down on the table

10.Climb up on the table, remain 5 second and then jump down

Ngo Duy Kien _Thesis Version 10_27

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