The Robotic Gait Simulator: A Dynamic Cadaveric Foot and Ankle Model forBiomechanics Research

Patrick M. Aubin

Department of Biomechanics,Vilnius Gediminas Technical University, Vilnius Lithuania

Department of Electrical Engineering, University of Washington , Seattle, WA

RR&D Center of Excellence, Department of Veterans Affairs, Seattle, WA

Introduction The RGS Iterative learning vGRF control

Prosthetic gait simulation. IEEE Trans. Biomedical Eng., vol. 55, 2008.

Manual vGRF control Flatfoot simulation. J. of Biomechanical Engineering, in review.

Open loop vGRF control Arthrodesis simulation. J. Bone & Joint Surg., vol. 94, 2010.

Fuzzy logic v 1.0 vGRF control Long second metatarsal study. J. Bone & Joint Surg., submitted, 2010.

Fuzzy logic v 2.0 vGRF control. IEEE T. on Robotics, submitted 2010. Bony motion study. Gait & Posture, submitted 2010.

Outline

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Patrick Aubin 3/78

Motivation

Cadaveric models

IntroductionOlson SL, 2003, Muscular imbalances resulting in a clawed hallux.RGSRembrandt, 1632

The Anatomy Lesson of Dr. Nicolaes Tulp

Fidelity

Utility

R. Bahr, 1998,Ligament force and joint motion in the intact ankle: a cadaveric study.

Patrick Aubin 4/78

State of the Art

Challenges for gait simulatorscontrol the vertical GRF scaled body weighttibia degrees of freedomspeed

Introduction

Cleveland Clinic

Medical School at Hannover, Germany

U. of Salford and Iowa State U.

Patrick Aubin 5/78

General Problem Statement

Develop an RGSin vitro tibia kinematics, tendon forces, and ground

reaction force (GRF) Use the RGS to

evaluate novel biomedical devices (e.g. prosthetic feet)model normal and pathological gaitevaluate surgical treatment strategiesdetermine optimal surgical objectiveselucidate disease etiologydetermine biological function

Introduction

Introduction The RGS Iterative learning vGRF control

Prosthetic gait simulation. IEEE Trans. Biomedical Eng., vol. 55, 2008.

Manual vGRF control Flatfoot simulation. J. of Biomechanical Engineering, in review.

Open loop vGRF control Arthrodesis simulation. J. Bone & Joint Surg., vol. 94, 2010.

Fuzzy logic v 1.0 vGRF control Long second metatarsal study. J. Bone & Joint Surg., submitted, 2010.

Fuzzy logic v 2.0 vGRF control. IEEE T. on Robotics, submitted 2010. Bony motion study. Gait & Posture, submitted 2010.

Outline

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Patrick Aubin 7/78

Methods

RGS

in vivo gait trial R2000

RGS

GRFtendonactuation

muscle model

tend

on fo

rce

plantar pressure

cadaveric foot model

GRF

foot & tibia kinematics

EMG, PCSA

from literature

living subject

kine

mat

ics

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R2000 parallel robot Force plate (C) Cadaveric foot (D) Tibia mounting frame

(F) Steel frame (A) Tendon actuation (G)

9 brushless DC motorsSeries load cells

3D motion tracking camera system (H)

Methods

RGS

Patrick Aubin 9/78

The R2000

6-DOF 25 microns repeatability 120°/s yaw

Methods

© Mikrolar Inc.

video

Introduction The RGS Iterative learning vGRF control

Prosthetic gait simulation. IEEE Trans. Biomedical Eng., vol. 55, 2008.

Manual vGRF control Flatfoot simulation. J. of Biomechanical Engineering, in review.

Open loop vGRF control Arthrodesis simulation. J. Bone & Joint Surg., vol. 94, 2010.

Fuzzy logic v 1.0 vGRF control Long second metatarsal study. J. Bone & Joint Surg., submitted, 2010.

Fuzzy logic v 2.0 vGRF control. IEEE T. on Robotics, submitted 2010. Bony motion study. Gait & Posture, submitted 2010.

Outline

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Patrick Aubin 11/78

Iterative Learning Control

Iteration domain vertical GRF control Simulation analyze vertical GRF adjust

motion repeat

Methods

R2000

tendonactuators

prosthetic foot

plantar surface

tendonsGRF

target kinematics ground

motion

target GRF

target tibia kinematics

iterativelearning

controller

Introduction The RGS Iterative learning vGRF control

Prosthetic gait simulation. IEEE Trans. Biomedical Eng., vol. 55, 2008.

Manual vGRF control Flatfoot simulation. J. of Biomechanical Engineering, in review.

Open loop vGRF control Arthrodesis simulation. J. Bone & Joint Surg., vol. 94, 2010.

Fuzzy logic v 1.0 vGRF control Long second metatarsal study. J. Bone & Joint Surg., submitted, 2010.

Fuzzy logic v 2.0 vGRF control. IEEE T. on Robotics, submitted 2010. Bony motion study. Gait & Posture, submitted 2010.

Outline

Patrick Aubin 12/86

ASB, Blacksburg, VA, 2006NWBS, Seattle, WA, 2006

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Prosthetic Gait Simulation

Kinematics recorded from transtibial amputee

Methods

video

Patrick Aubin 14/78

Simulation results (1.5s)ILC: 6 iterations to vGRF

tracking4.1% BW RMS error:

simulated vs. in situ

Results

Prosthetic Gait Simulation

P.M. Aubin, et al., IEEE Transactions on Biomedical Engineering, vol. 55, Mar. 2008

Introduction The RGS Iterative learning vGRF control

Prosthetic gait simulation. IEEE Trans. Biomedical Eng., vol. 55, 2008.

Manual vGRF control Flatfoot simulation. J. of Biomechanical Engineering, in review.

Open loop vGRF control Arthrodesis simulation. J. Bone & Joint Surg., vol. 94, 2010.

Fuzzy logic v 1.0 vGRF control Long second metatarsal study. J. Bone & Joint Surg., submitted, 2010.

Fuzzy logic v 2.0 vGRF control. IEEE T. on Robotics, submitted 2010. Bony motion study. Gait & Posture, submitted 2010.

Outline

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Manual vGRF Control

Motivated to study the foot and ankle Improvements for cadaveric simulation

Tendon force actuation○ Tibia mounting frame○ Liquid nitrogen freeze clamps

Collaboration with Lyle Jackson UW medical student research training program

Motivation

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Tendon Force Actuation Nine motors + load cells + freeze clamp Force feedback PID control Matlab Simulink model

Methods

A/D

tend

on fo

rce

torq

ue c

omm

and

targ

et fo

rce

Gc(z)+- ZOH 1

curre

nt

saturationPID drive actuatortendon system

D/A

load cell

1

G(s)

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Manual vGRF Control

Manual control block diagram

Methods

R2000

tendonactuators

target tendon force

cadaveric foot

plantar surface

tendons

GRF

target kinematics ground

motion

tendon force

target GRF

target tibia kinematics

manual control

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Manual vGRF Control

Control heuristics0-40% of stance phase

○ vGRF achieved by translating the mobile platform50-90% of stance phase

○ vGRF achieved by adjusting the Achilles tendon force

Methods

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Manual vGRF Control

In vitro vertical GRF matched in vivo data

Results

0 10 20 30 40 50 60 70 80 90 1000

0.2

0.4

0.6

0.8

1

1.2

In-Vivo vs. In-Vitro Vertical GRF

in vivo

in vitro

stance phase (%)

No

rma

lize

d f

orc

e (

N/B

W)

Introduction The RGS Iterative learning vGRF control

Prosthetic gait simulation. IEEE Trans. Biomedical Eng., vol. 55, 2008.

Manual vGRF control Flatfoot simulation. J. of Biomechanical Engineering, in review.

Open loop vGRF control Arthrodesis simulation. J. Bone & Joint Surg., vol. 94, 2010.

Fuzzy logic v 1.0 vGRF control Long second metatarsal study. J. Bone & Joint Surg., submitted, 2010.

Fuzzy logic v 2.0 vGRF control. IEEE T. on Robotics, submitted 2010. Bony motion study. Gait & Posture, submitted 2010.

Outline

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ICRB, Banff, Canada, 2008. NACOB, Ann Arbor, MI, 2008. WSMRF, Carmel, CA, 2008.

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Flatfoot Simulation

Motivationflatfoot incidence ~5%, (Ferciot, 1972)

investigate effectiveness of reconstructive surgeries Methods

manual vGRF controltarget tibial kinematics and GRF recorded from 10 flat

foot subjectscadaveric flat foot

○ ligament attenuation○ 15,000 cycles

Introduction

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Flatfoot Simulation

In vitro tibia angles matched in vivo data

Results

0 10 20 30 40 50 60 70 80 90 100-40

-30

-20

-10

0

10

20

30

In Vivo vs. In Vitro Sagittal Plane

in vivo

in vitro

Stance phase (%)

Ro

tati

on

an

gle

(d

eg

.)

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Flatfoot Simulation

Collapse of the medial arch

Results

0 10 20 30 40 50 60 70 80 90 100

-35

-30

-25

-20

-15

-10

-5

0

5

10

15

First Metatarsal to Talus, Sagittal PlanePre Flat

Post Flat

Percent Stance Phase

De

gre

es

Introduction The RGS Iterative learning vGRF control

Prosthetic gait simulation. IEEE Trans. Biomedical Eng., vol. 55, 2008.

Manual vGRF control Flatfoot simulation. J. of Biomechanical Engineering, in review.

Open loop vGRF control Arthrodesis simulation. J. Bone & Joint Surg., vol. 94, 2010.

Fuzzy logic v 1.0 vGRF control Long second metatarsal study. J. Bone & Joint Surg., submitted, 2010.

Fuzzy logic v 2.0 vGRF control. IEEE T. on Robotics, submitted 2010. Bony motion study. Gait & Posture, submitted 2010.

Outline

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Open Loop vGRF Control

Manual vGRF control was non-dynamicpoorly approximates a dynamic system

Improvements for dynamic simulationfaster tendon force actuator rise and settling timesynchronizationRGS software

○ data analysis, left and right foot, dynamic tendon forcetrajectory path planning

Introduction

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Open Loop vGRF Control

vGRF Heuristics

Methods

FAchilles = G·PCSA·MST·EMG

∆x

ROB

R2000

tendonactuators

target tendon force

cadaveric foot

plantar surface

tendons

GRF

target kinematics ground

motion

tendon force

target GRF

target tibia kinematics

∆x

GRGS operator

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Results

Open Loop vGRF Control

vertical GRF

video

Patrick Aubin 29/78

Results

Open Loop vGRF Control

1

stance phase (%)

For

ce (

N/ ½

BW

)

100

in vivo

in vitro

vertical GRF

Introduction The RGS Iterative learning vGRF control

Prosthetic gait simulation. IEEE Trans. Biomedical Eng., vol. 55, 2008.

Manual vGRF control Flatfoot simulation. J. of Biomechanical Engineering, in review.

Open loop vGRF control Arthrodesis simulation. J. Bone & Joint Surg., vol. 94, 2010.

Fuzzy logic v 1.0 vGRF control Long second metatarsal study. J. Bone & Joint Surg., submitted, 2010.

Fuzzy logic v 2.0 vGRF control. IEEE T. on Robotics, submitted 2010. Bony motion study. Gait & Posture, submitted 2010.

Outline

Patrick Aubin 30/86

ORS, Las Vegas, NV 2009NWBS, Pullman, WA 2009

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Metatarsalphalangeal joint (MTPJ) arthrodesis simulations

Arthrodesis indicationsosteoarthritisprevious failed surgeries

Ahmad Bayomy CollaboratorUW medical student research

training program

Introduction

Arthrodesis of the First MTPJ

MTPJ

Modified from http://www.eorthopod.com

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Arthrodesis of the First MTPJ Literature suggests 20° to 25° of dorsiflexion

Above 25°: Shoe wear difficultyBelow 20°: Abnormal hallux pressure

Dorsal fixation plate to simulate arthodesis Vary DF measure PP

Introduction

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Arthrodesis of the First MTPJ

RGS simulation at ½ body weight and 10 s

Methods

video

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Arthrodesis of the First MTPJ

The fusion angle that minimizes peak pressure under the hallux and first metatarsal was 24.0°.

Methods

5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.00

10

20

30

40

50

60 Hallux1st MTHHallux regression1st MTH regression

Dorsiflexion Angle (°)

Peak P

ressu

re (

N/c

m2)

Introduction The RGS Iterative learning vGRF control

Prosthetic gait simulation. IEEE Trans. Biomedical Eng., vol. 55, 2008.

Manual vGRF control Flatfoot simulation. J. of Biomechanical Engineering, in review.

Open loop vGRF control Arthrodesis simulation. J. Bone & Joint Surg., vol. 94, 2010.

Fuzzy logic v 1.0 vGRF control Long second metatarsal study. J. Bone & Joint Surg., submitted, 2010.

Fuzzy logic v 2.0 vGRF control. IEEE T. on Robotics, submitted 2010. Bony motion study. Gait & Posture, submitted 2010.

Outline

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Patrick Aubin 36/78

Fuzzy logic 1.0 vGRF Control

MotivationvGRF fidelity

Introduction

stance phase (%)

Fo

rce

(N

/ ½ B

W)

100

in vivo

in vitro

manual control results

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Fuzzy logic 1.0 vGRF Control

A fuzzy logic controller can addresses four major challenges:

non-linear, time variant: heel strike (contact events), material properties

ill-defined: knowledge is qualitative and descriptive, not analytical

underdetermined: vGRF= f (nine tendons, tibia kinematics)

limited number of simulations allowedneural networks and genetic algorithms not appropriate

As a model-free paradigm a fuzzy rule based controller is well suited for highly nonlinear MIMO systems, [Ross, 2004].

Introduction

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Fuzzy logic 1.0 vGRF Control

Fuzzy logic controller replaces RGS operator

Introduction

R2000

tendonactuators

target tendon force

cadaveric foot

plantar surface

tendons

GRF

target kinematics ground

motion

tendon force

target GRF

target tibia kinematics

RGS operator

fuzzy logic

controller

Patrick Aubin 39/78

DefuzzificationCompositionInferenceFuzzification

membership function rule table max center of

gravity

fuzzy logic vertical GRF controller

Methods

Fuzzy logic 1.0 vGRF Control

early late stancepercent stance

input variables fuzzy sets

negativezeropositive

vGRFerror

∑vGRFerror

input variables fuzzy sets

large neg.… zero …large pos.

∆FAchilles

output variables fuzzy sets

Patrick Aubin 40/78

DefuzzificationCompositionInferenceFuzzification

membership function rule table max center of

gravity

fuzzy logic vertical GRF controller

Methods

Fuzzy logic 1.0 vGRF Control

if stance is late and vGRFerror is positive and ∑vGRFerror is positive

then change in Achilles tendon force is large positive

If…. then … rules. min implication

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DefuzzificationCompositionInferenceFuzzification

membership function rule table max center of

gravity

fuzzy logic vertical GRF controller

Methods

Fuzzy logic 1.0 vGRF Control

Combine fuzzy output subsets

+ +

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DefuzzificationCompositionInferenceFuzzification

membership function rule table max center of

gravity

fuzzy logic vertical GRF controller

Methods

Fuzzy logic 1.0 vGRF Control

Determine crisp output via center of gravity

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Fuzzy logic 1.0 vGRF Control

Fuzzy sets manually tuned RGS simulations using modified single axis prosthetic

foot

Methods

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Fuzzy logic 1.0 vGRF Control

vGRF tracking performance1.7% BW RMS tracking error between 50-100%

stance.

Results

Introduction The RGS Iterative learning vGRF control

Prosthetic gait simulation. IEEE Trans. Biomedical Eng., vol. 55, 2008.

Manual vGRF control Flatfoot simulation. J. of Biomechanical Engineering, in review.

Open loop vGRF control Arthrodesis simulation. J. Bone & Joint Surg., vol. 94, 2010.

Fuzzy logic v 1.0 vGRF control Long second metatarsal study. J. Bone & Joint Surg., submitted, 2010.

Fuzzy logic v 2.0 vGRF control. IEEE T. on Robotics, submitted 2010. Bony motion study. Gait & Posture, submitted 2010.

Outline

Patrick Aubin 45/86

ORS, New Orleans, LA, NV, 2009NWBS, Pullman, WA , 2009ASB, College State, PA, 2009

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Long Second Metatarsal

Crossover toe deformitysecond metatarsophalangeal joint (MTPJ)proposed etiology: long second metatarsal Hypothesis: second metatarsal length is positively

correlated with increased plantar pressure Joel Weber Collaborator

MSRTP

Introduction

MTPJ

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Long Second Metatarsal Surgically lengthen second metatarsal Measure plantar pressure Measure second metatarsal angle Repeated measures design (6 feet, 5 lengths) Achilles tendon force from in vivo measurement

Methods

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Long Second Metatarsal

RGS simulation at ½ body weight and 10 s

Methods

video

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Long Second Metatarsal

vGRF tracking results

Results

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Long Second Metatarsal

Second met head peak pressure was significantly associated with an increase in second met length (p=0.0005)

Results

Introduction The RGS Iterative learning vGRF control

Prosthetic gait simulation. IEEE Trans. Biomedical Eng., vol. 55, 2008.

Manual vGRF control Flatfoot simulation. J. of Biomechanical Engineering, in review.

Open loop vGRF control Arthrodesis simulation. J. Bone & Joint Surg., vol. 94, 2010.

Fuzzy logic v 1.0 vGRF control Long second metatarsal study. J. Bone & Joint Surg., submitted, 2010.

Fuzzy logic v 2.0 vGRF control. IEEE T. on Robotics, submitted 2010. Bony motion study. Gait & Posture, submitted 2010.

Outline

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NWBS, Seattle, WA , 2010ASB, Providence, RI, 2010iFAB, Seattle, WA , 2010

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Fuzzy Logic 2.0 vGRF Control

Motivationimprove vGRF fidelity

Introduction

R2000

tendonactuators

target tendon force

cadaveric foot

plantar surface

tendons

GRF

target kinematics ground

motion

tendon force

target GRF

target tibia kinematics

RGS operator

fuzzy logic

controller

Patrick Aubin 53/78

Fuzzy Logic 2.0 vGRF Control

Three inputs and three outputs Three controllers in parallel Heuristics based on stance phase events

Methods

Achilles

tibialis anterior

R2000

fuzzy logic controller∆FACH

∆FTA

∆x

vGRFerror

∑vGRFerror

percent stance

Patrick Aubin 54/78

Fuzzy Logic 2.0 vGRF Control

Heel strikeno fuzzy logic output

Load responsetibialis anteriorR2000 trajectory

MidstanceR2000 trajectory

Late stanceAchilles

Methods

Heuristics:if vGRFerror is positive and ∑vGRFerror is positive then ∆ tibialis tendon force is large positive

if vGRFerror is positive and ∑vGRFerror is positive then ∆x is large positiveif vGRFerror is positive and ∑vGRFerror is positive then ∆Achilles tendon force is large positive

Patrick Aubin 55/78

Methods

Fuzzy Logic 2.0 vGRF Control

R2000robot

PIDtendon

forcecontroller

electricmotortendon

actuators

cadavericfoot

tendons

plantar surface

load cell

DA

AD

AD

fuzzylogicvGRF

controller

∆FAch

trajectorygenerator

vGRFtarget

+

∆xj

FAch

Ftendonx 7

in vivo tibial kinematics

force platevGRFactual

Σ

+

_

vGRF

FTA

+∆FTA

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Fuzzy Logic 2.0 vGRF Control

Statistics methods in vitro versus in vivo Linear mixed effects regression

vertical GRF Two-sample t-tests

tibia angles

Methods

˟˟

˟

min time

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Fuzzy Logic 2.0 vGRF ControlMethods

six feet, three learning trials, one final trial 2.7 s ¾ BW

video

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Fuzzy Logic 2.0 vGRF ControlResults

mean RMS vGRF tracking error was 5.9% BW sig. diff. (p<.05)

minimum (5.9%)vGRF int. (2.0%)

˟

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Fuzzy Logic 2.0 vGRF Control

No sig. diff. between in vivo and in vitro tibial kinematics (p<0.05)

Results

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Fuzzy Logic 2.0 vGRF Control

Tendon force tracking

Results

3.6 N RMS30.6% peak

3.8 N RMS5.0% peak

in vivo estimatein vitro mean

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Fuzzy Logic 2.0 vGRF Control

Close loop fuzzy logic vGRF control improvement over open loop control

Increased speed to 2.7s Accurate reproduction of

tibial kinematicsvGRFtendon forces

Discussion

Introduction The RGS Iterative learning vGRF control

Prosthetic gait simulation. IEEE Trans. Biomedical Eng., vol. 55, 2008.

Manual vGRF control Flatfoot simulation. J. of Biomechanical Engineering, in review.

Open loop vGRF control Arthrodesis simulation. J. Bone & Joint Surg., vol. 94, 2010.

Fuzzy logic v 1.0 vGRF control Long second metatarsal study. J. Bone & Joint Surg., submitted, 2010.

Fuzzy logic v 2.0 vGRF control. IEEE T. on Robotics, submitted 2010. Bony motion study. Gait & Posture, submitted 2010.

Outline

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NWBS, Seattle, WA , 2010ASB, Providence, RI, 2010iFAB, Seattle, WA , 2010

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Bony Motion

Bony motion useful to understand joint functionNon-invasive and invasive methods

Introduction

A. Leardini et al., 2006C. Nester et al., 2007

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Bony Motion

Study objectivesDevelop an anatomical multi-segment foot modelDetermine foot bony motion during the stance phase

of gait

Introduction

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Bony Motion

Six cadaveric feet RGS simulations in 2.7s at ¾ BW Multi-segment anatomical foot model

Methods

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Bony Motion

Anatomical multi-segment foot modeldigitized virtual pointsbone pins and quad clusters

Methods

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Bony Motion

RGS simulation at ¾ body weight and 2.7 s

Methods

video

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Bony Motion

Motion of 17 joints recorded Midfoot joints have substantial motion

Results

Range of motion: 23.2± 4.6

Range of motion: 12.2± 2.2

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Bony Motion

In vitro results consistent with invasive in vivo data

Results indicate limitations of simplified rigid body models

Better understanding ofmidtarsal jointmidfoot motioninter-metatarsal mobility

Discussion

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Conclusion

Dynamic vGRF tracking performance

Stance phase (%)

Fo

rce

(N

/ ½ B

W)

100

in vivo

in vitro

open loop Fuzzy v 1.0 Fuzzy v 2.0

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Conclusion

vGRF control Clinical studyspeed

1.5 s • prosthetic gait simulationiterative learning

static • flatfoot simulationmanual

10 s • arthrodesis of first MTPJopen loop

10 s • long second metatarsalfuzzy logic 1.0

2.7 s • foot bony motionfuzzy logic 2.0

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Acknowledgements Department of Veterans Affairs, Research Rehabilitation

and Development Service grant numbers A2661C, A3923R, A6669R and A4843C.

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Special thanks to:

Center of Excellence for Limb Loss Prevention and Prosthetic Engineering

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References

Ferciot CF. Clin Orthop 85:7–10, 1972. Kaz, AJ. Foot Ankle Int. 28: 1223-1237, 2007. Nester, CJ. J. of Biomechanics 40: 3412–23 2007. Leardini, A. Gait & Posture 25: 453-462, 2007.

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Extra slides

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MotivationIntroduction

scientificmethod

Hypothesis

ExperimentData(Results)

Conclusion

The function of A is B.Condition A causes disease B (etiology).Treatment A has a better outcome than treatment B.

Gray's Anatomy of the Human Body

Living subjects Computational

Cadaveric

Model

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State of the Art

Dynamic cadaveric gait simulators

Introduction

Vertical GRF control Trial and errorTibia DOF 3Speed 12 s

Vertical GRF control Trial and errorTibia DOF 3Speed 2 s

Vertical GRF control: Trial and errorTibia DOF 3Speed 20 s

Vertical GRF control: Force controlTibia DOF 3Speed 60 s

Pennsylvania State U. U. of Salford and Iowa State U. Medical School at Hannover, GermanyU. of Wisconsin-Milwaukee and

Mayo Clinic Cleveland Clinic

Vertical GRF control: iterative controlTibia DOF 6Speed 3.2 s

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Open Loop vGRF Control

Achilles tendon dictates vGRF

Results

AchillesvGRF

1.0

0.5

F

orce

(N

/ ½ B

W)

Patrick Aubin 79/78

Fuzzy logic 1.0 vGRF Control

RGS Block diagram with fuzzy logic controller

Methods

R2000

PIDtendon

forcecontroller

electricmotortendon

actuators

cadavericfoot

tendons

plantar surface

load cell

DA

AD

AD

fuzzylogicvGRF

controller∆FAch

trajectorygenerator

vGRFtarget+

∆xj

FAch

Ftendonx 8

in vivo tibial kinematics

force platevGRFactual

Σ

+

_

vGRF

operator

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Long Second Metatarsal

↑ second met length ↑ PP and pressure time integral (PTI) under second

met head ○ (p=0.005, p<0.0001)

↓ PP and PTI under first met head○ (p=0.029, p=0.024)

↑ second toe transverse plane angle○ (p=0.003)

Results

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Fuzzy Logic 2.0 vGRF ControlMethods

Stance phase eventsfoot flat

16.6%

COP under met heads43.5%

heel rise50%

peak TA force~18%

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Fuzzy Logic 2.0 vGRF Control

R2000 trajectory optimization to increase speed

Methods

Vicon Plate trajectory

Inverse kinematic

mapMotor velocity

Optimization Best TIB poseROB

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Fuzzy Logic 2.0 vGRF Control

Within subject variability

Results

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Fuzzy Logic 2.0 vGRF Control

Medial/lateral and anterior/posterior GRF similar to in vivo

Results

medial/lateral

anterior/posterior

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Fuzzy Logic 2.0 vGRF Control

Precise tibial kinematics

Results

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Conclusion

Gait simulator comparison

system vGRF control vGRF (%) speed (s)tibial DOF

Pennsylvania State Univ. open loop 100 12 3Univ. of Salford & Iowa State Univ.

open loop 50 2 3

Medical School of Hannover, Germany

force control 50 60 3

Univ. of Wisconsin- Milwaukee & Mayo Clinic

open loop 40 20 3

Cleveland Clinic iterative control 66 - 100 3.2 6

VA RR&D fuzzy logic 75 2.7 6