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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
Patrick Aubin 2/86
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
Patrick Aubin 6/86
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
Patrick Aubin 8/78
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
Patrick Aubin 10/86
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
Patrick Aubin 13/78
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
Patrick Aubin 15/86
Patrick Aubin 16/78
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
Patrick Aubin 17/78
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)
Patrick Aubin 18/78
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
Patrick Aubin 19/78
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
Patrick Aubin 20/78
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
Patrick Aubin 21/86
ICRB, Banff, Canada, 2008. NACOB, Ann Arbor, MI, 2008. WSMRF, Carmel, CA, 2008.
Patrick Aubin 22/78
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
Patrick Aubin 23/78
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
.)
Patrick Aubin 24/78
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
Patrick Aubin 25/86
Patrick Aubin 26/78
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
Patrick Aubin 27/78
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
Patrick Aubin 28/78
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
Patrick Aubin 31/78
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
Patrick Aubin 32/78
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
Patrick Aubin 33/78
Arthrodesis of the First MTPJ
RGS simulation at ½ body weight and 10 s
Methods
video
Patrick Aubin 34/78
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
Patrick Aubin 35/86
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
Patrick Aubin 37/78
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
Patrick Aubin 38/78
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
Patrick Aubin 41/78
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
+ +
Patrick Aubin 42/78
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
Patrick Aubin 43/78
Fuzzy logic 1.0 vGRF Control
Fuzzy sets manually tuned RGS simulations using modified single axis prosthetic
foot
Methods
Patrick Aubin 44/78
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
Patrick Aubin 46/78
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
Patrick Aubin 47/78
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
Patrick Aubin 48/78
Long Second Metatarsal
RGS simulation at ½ body weight and 10 s
Methods
video
Patrick Aubin 49/78
Long Second Metatarsal
vGRF tracking results
Results
Patrick Aubin 50/78
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
Patrick Aubin 51/86
NWBS, Seattle, WA , 2010ASB, Providence, RI, 2010iFAB, Seattle, WA , 2010
Patrick Aubin 52/78
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
Patrick Aubin 56/78
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
Patrick Aubin 57/78
Fuzzy Logic 2.0 vGRF ControlMethods
six feet, three learning trials, one final trial 2.7 s ¾ BW
video
Patrick Aubin 58/78
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%)
˟
Patrick Aubin 59/78
Fuzzy Logic 2.0 vGRF Control
No sig. diff. between in vivo and in vitro tibial kinematics (p<0.05)
Results
Patrick Aubin 60/78
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
Patrick Aubin 61/78
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
Patrick Aubin 62/86
NWBS, Seattle, WA , 2010ASB, Providence, RI, 2010iFAB, Seattle, WA , 2010
Patrick Aubin 63/78
Bony Motion
Bony motion useful to understand joint functionNon-invasive and invasive methods
Introduction
A. Leardini et al., 2006C. Nester et al., 2007
Patrick Aubin 64/78
Bony Motion
Study objectivesDevelop an anatomical multi-segment foot modelDetermine foot bony motion during the stance phase
of gait
Introduction
Patrick Aubin 65/78
Bony Motion
Six cadaveric feet RGS simulations in 2.7s at ¾ BW Multi-segment anatomical foot model
Methods
Patrick Aubin 66/78
Bony Motion
Anatomical multi-segment foot modeldigitized virtual pointsbone pins and quad clusters
Methods
Patrick Aubin 67/78
Bony Motion
RGS simulation at ¾ body weight and 2.7 s
Methods
video
Patrick Aubin 68/78
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
Patrick Aubin 69/78
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
Patrick Aubin 70/78
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
Patrick Aubin 71/78
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
Patrick Aubin 72/78
Acknowledgements Department of Veterans Affairs, Research Rehabilitation
and Development Service grant numbers A2661C, A3923R, A6669R and A4843C.
Patrick Aubin 73/78
Special thanks to:
Center of Excellence for Limb Loss Prevention and Prosthetic Engineering
Patrick Aubin 74/78
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.
Patrick Aubin 75/78
Extra slides
Patrick Aubin 76/78
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
Patrick Aubin 77/78
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
Patrick Aubin 78/78
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
Patrick Aubin 80/78
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
Patrick Aubin 81/78
Fuzzy Logic 2.0 vGRF ControlMethods
Stance phase eventsfoot flat
16.6%
COP under met heads43.5%
heel rise50%
peak TA force~18%
Patrick Aubin 82/78
Fuzzy Logic 2.0 vGRF Control
R2000 trajectory optimization to increase speed
Methods
Vicon Plate trajectory
Inverse kinematic
mapMotor velocity
Optimization Best TIB poseROB
Patrick Aubin 83/78
Fuzzy Logic 2.0 vGRF Control
Within subject variability
Results
Patrick Aubin 84/78
Fuzzy Logic 2.0 vGRF Control
Medial/lateral and anterior/posterior GRF similar to in vivo
Results
medial/lateral
anterior/posterior
Patrick Aubin 85/78
Fuzzy Logic 2.0 vGRF Control
Precise tibial kinematics
Results
Patrick Aubin 86/78
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
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