1. The Robotic Gait Simulator: A Dynamic Cadaveric Foot and Ankle Model for Biomechanics 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

3. Motivation Cadaveric models Introduction RGS
Olson SL, 2003, Muscular imbalances resulting in a clawed hallux.
R. Bahr, 1998,
Ligament force and joint motion in the intact ankle: a cadaveric study.
Rembrandt, 1632
The Anatomy Lesson of Dr. Nicolaes Tulp
RGS
Cadaveric models
Utility
Fidelity

4. State of the Art Challenges for gait simulators
Introduction
State of the Art
Medical School at Hannover, Germany
Challenges for gait simulators
control the vertical GRF
scaled body weight
tibia degrees of freedom
speed
U. of Salford and Iowa State U.
Cleveland Clinic

5. General Problem Statement
Introduction
General Problem Statement
Develop an RGS
in 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 gait
evaluate surgical treatment strategies
determine optimal surgical objectives
elucidate disease etiology
determine biological function

7. RGS Methods living subject in vivo gait trial R2000
GRF
tendon
actuation
muscle model
tendon force
plantar pressure
cadaveric foot model
foot & tibia kinematics
EMG, PCSA
from literature
living subject
kinematics

8. RGS R2000 parallel robot Force plate (C) Cadaveric foot (D)
Methods
RGS
R2000 parallel robot
Force plate (C)
Cadaveric foot (D)
Tibia mounting frame (F)
Steel frame (A)
Tendon actuation (G)
9 brushless DC motors
Series load cells
3D motion tracking camera system (H)

9. The R2000 6-DOF 25 microns repeatability 120°/s yaw Methods
© Mikrolar Inc.
video

11. Iterative Learning Control
Methods
Iterative Learning Control
Iteration domain vertical GRF control
Simulation  analyze vertical GRF  adjust motion  repeat 
R2000
tendon
actuators
prosthetic foot
plantar surface
tendons
GRF
target kinematics
ground
motion
target GRF
target tibia kinematics
iterative
learning
controller

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

13. Prosthetic Gait Simulation
Methods
Prosthetic Gait Simulation
Kinematics recorded from transtibial amputee
video

14. Prosthetic Gait Simulation
Results
Prosthetic Gait Simulation
Simulation results (1.5s)
ILC: 6 iterations to vGRF tracking
4.1% BW RMS error: simulated vs. in situ
P.M. Aubin, et al., IEEE Transactions on Biomedical Engineering, vol. 55, Mar. 2008

16. Manual vGRF Control Motivated to study the foot and ankle
Motivation
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

17. Tendon Force Actuation
Methods
Tendon Force Actuation
Nine motors + load cells + freeze clamp
Force feedback PID control
Matlab Simulink model
A/D
tendon force
torque command
target force
Gc(z) + -
ZOH 1
current
saturation
PID
drive
actuator
tendon system
D/A
load cell
G(s)

18. Manual vGRF Control Manual control block diagram Methods R2000 tendon
actuators
target tendon force
cadaveric foot
plantar surface
tendons
GRF
target kinematics
ground
motion
tendon force
target GRF
target tibia kinematics
manual control

19. Manual vGRF Control Control heuristics 0-40% of stance phase
Methods
Manual vGRF Control
Control heuristics
0-40% of stance phase
vGRF achieved by translating the mobile platform
50-90% of stance phase
vGRF achieved by adjusting the Achilles tendon force

20. Results
Manual vGRF Control
In vitro vertical GRF matched in vivo data

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

22. Flatfoot Simulation Motivation Methods
Introduction
Flatfoot Simulation
Motivation
flatfoot incidence ~5%, (Ferciot, 1972)
investigate effectiveness of reconstructive surgeries
Methods
manual vGRF control
target tibial kinematics and GRF recorded from 10 flat foot subjects
cadaveric flat foot
ligament attenuation
15,000 cycles

23. Results
Flatfoot Simulation
In vitro tibia angles matched in vivo data

24. Results
Flatfoot Simulation
Collapse of the medial arch

26. Open Loop vGRF Control Manual vGRF control was non-dynamic
Introduction
Open Loop vGRF Control
Manual vGRF control was non-dynamic
poorly approximates a dynamic system
Improvements for dynamic simulation
faster tendon force actuator rise and settling time
synchronization
RGS software
data analysis, left and right foot, dynamic tendon force
trajectory path planning

27. FAchilles = G·PCSA·MST·EMG
Methods
Open Loop vGRF Control ∆x
ROB
vGRF Heuristics
FAchilles = G·PCSA·MST·EMG
R2000
tendon
actuators
target tendon force
cadaveric foot
plantar surface
tendons
GRF
target kinematics
ground
motion
tendon force
target GRF
target tibia kinematics ∆x G
RGS operator

28. Results
Open Loop vGRF Control
vertical GRF
video

29. Open Loop vGRF Control Results vertical GRF in vivo Force (N/ ½ BW)
stance phase (%)
Force (N/ ½ BW)
100
in vivo
in vitro
vertical GRF 1

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

31. Arthrodesis of the First MTPJ
Introduction
Arthrodesis of the First MTPJ
Metatarsalphalangeal joint (MTPJ) arthrodesis simulations
Arthrodesis indications
osteoarthritis
previous failed surgeries
Ahmad Bayomy Collaborator
UW medical student research training program
MTPJ
Modified from

32. Arthrodesis of the First MTPJ
Introduction
Arthrodesis of the First MTPJ
Literature suggests 20° to 25° of dorsiflexion
Above 25°: Shoe wear difficulty
Below 20°: Abnormal hallux pressure
Dorsal fixation plate to simulate arthodesis
Vary DF measure PP

33. Arthrodesis of the First MTPJ
Methods
Arthrodesis of the First MTPJ
RGS simulation at ½ body weight and 10 s
video

34. Arthrodesis of the First MTPJ
Methods
Arthrodesis of the First MTPJ
The fusion angle that minimizes peak pressure under the hallux and first metatarsal was 24.0°.

36. Fuzzy logic 1.0 vGRF Control
Introduction
Fuzzy logic 1.0 vGRF Control
Motivation
vGRF fidelity
stance phase (%)
Force (N/ ½ BW)
100
in vivo
in vitro
manual control results

37. Fuzzy logic 1.0 vGRF Control
Introduction
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 allowed
neural 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].

38. Fuzzy logic 1.0 vGRF Control
Introduction
Fuzzy logic 1.0 vGRF Control
Fuzzy logic controller replaces RGS operator
R2000
tendon
actuators
target tendon force
cadaveric foot
plantar surface
tendons
GRF
target kinematics
ground
motion
tendon force
target GRF
target tibia kinematics
fuzzy
logic
controller
RGS operator

39. Fuzzy logic 1.0 vGRF Control
Methods
Fuzzy logic 1.0 vGRF Control
Defuzzification
Composition
Inference
Fuzzification
membership function
rule table
max
center of gravity
fuzzy logic vertical GRF controller
large neg.
… zero …
large pos.
∆FAchilles
output variables
fuzzy sets
early
late stance
percent stance
input variables
fuzzy sets
negative
zero
positive
vGRFerror
∑vGRFerror
input variables
fuzzy sets

40. Fuzzy logic 1.0 vGRF Control
Methods
Fuzzy logic 1.0 vGRF Control
Defuzzification
Composition
Inference
Fuzzification
membership function
rule table
max
center of gravity
fuzzy logic vertical GRF controller
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

41. Fuzzy logic 1.0 vGRF Control
Methods
Fuzzy logic 1.0 vGRF Control
Defuzzification
Composition
Inference
Fuzzification
membership function
rule table
max
center of gravity
fuzzy logic vertical GRF controller
Combine fuzzy output subsets +

42. Fuzzy logic 1.0 vGRF Control
Methods
Fuzzy logic 1.0 vGRF Control
Defuzzification
Composition
Inference
Fuzzification
membership function
rule table
max
center of gravity
fuzzy logic vertical GRF controller
Determine crisp output via center of gravity

43. Fuzzy logic 1.0 vGRF Control
Methods
Fuzzy logic 1.0 vGRF Control
Fuzzy sets manually tuned
RGS simulations using modified single axis prosthetic foot

44. Fuzzy logic 1.0 vGRF Control
Results
Fuzzy logic 1.0 vGRF Control
vGRF tracking performance
1.7% BW RMS tracking error between % stance.

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

46. Long Second Metatarsal
Introduction
Long Second Metatarsal
Crossover toe deformity
second metatarsophalangeal joint (MTPJ)
proposed etiology: long second metatarsal
Hypothesis: second metatarsal length is positively correlated with increased plantar pressure
Joel Weber Collaborator
MSRTP
MTPJ

47. Long Second Metatarsal
Methods
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

48. Long Second Metatarsal
Methods
Long Second Metatarsal
RGS simulation at ½ body weight and 10 s
video

49. Long Second Metatarsal
Results
Long Second Metatarsal
vGRF tracking results

50. Long Second Metatarsal
Results
Long Second Metatarsal
Second met head peak pressure was significantly associated with an increase in second met length (p=0.0005)

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

52. Fuzzy Logic 2.0 vGRF Control
Introduction
Fuzzy Logic 2.0 vGRF Control
Motivation
improve vGRF fidelity
fuzzy
logic
controller
R2000
tendon
actuators
target tendon force
cadaveric foot
plantar surface
tendons
GRF
target kinematics
ground
motion
tendon force
target GRF
target tibia kinematics
RGS operator

53. Fuzzy Logic 2.0 vGRF Control
Methods
Fuzzy Logic 2.0 vGRF Control
Three inputs and three outputs
Three controllers in parallel
Heuristics based on stance phase events
Achilles
tibialis anterior
R2000
fuzzy logic controller
∆FACH
∆FTA ∆x
vGRFerror
∑vGRFerror
percent stance

54. Fuzzy Logic 2.0 vGRF Control
Methods
Fuzzy Logic 2.0 vGRF Control
Heel strike
no fuzzy logic output
Load response
tibialis anterior
R2000 trajectory
Midstance
Late stance
Achilles
Heuristics:
if vGRFerror is positive and ∑vGRFerror is positive then ∆Achilles tendon force is large positive
if vGRFerror is positive and ∑vGRFerror is positive then ∆x is large positive
if vGRFerror is positive and ∑vGRFerror is positive then ∆ tibialis tendon force is large positive

55. Fuzzy Logic 2.0 vGRF Control
Methods
Fuzzy Logic 2.0 vGRF Control
R2000
robot
PID
tendon
force
controller
electric
motor
actuators
cadaveric
foot
tendons
plantar surface
load cell DA AD
fuzzy
logic
vGRF
∆FAch
trajectory
generator
vGRFtarget +
∆xj
FAch
Ftendonx 7
in vivo tibial kinematics
force plate
vGRFactual Σ _
FTA
∆FTA

56. Fuzzy Logic 2.0 vGRF Control
Methods
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 ˟ ˟ ˟
min time

57. Fuzzy Logic 2.0 vGRF Control
Methods
Fuzzy Logic 2.0 vGRF Control
six feet, three learning trials, one final trial
2.7 s
¾ BW
video

58. Fuzzy Logic 2.0 vGRF Control
Results
Fuzzy Logic 2.0 vGRF Control
mean RMS vGRF tracking error was 5.9% BW
sig. diff. (p<.05)
minimum (5.9%)
vGRF int. (2.0%) ˟

59. Fuzzy Logic 2.0 vGRF Control
Results
Fuzzy Logic 2.0 vGRF Control
No sig. diff. between in vivo and in vitro tibial kinematics (p<0.05)

60. Fuzzy Logic 2.0 vGRF Control
Results
Fuzzy Logic 2.0 vGRF Control
Tendon force tracking
3.6 N RMS
30.6% peak
3.8 N RMS
5.0% peak
in vivo estimate
in vitro mean

61. Fuzzy Logic 2.0 vGRF Control
Discussion
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 kinematics
vGRF
tendon forces

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

63. Bony Motion Bony motion useful to understand joint function
Introduction
Bony Motion
Bony motion useful to understand joint function
Non-invasive and invasive methods
C. Nester et al., 2007
A. Leardini et al., 2006

64. Bony Motion Study objectives
Introduction
Bony Motion
Study objectives
Develop an anatomical multi-segment foot model
Determine foot bony motion during the stance phase of gait

65. Bony Motion Six cadaveric feet RGS simulations in 2.7s at ¾ BW
Methods
Bony Motion
Six cadaveric feet
RGS simulations in 2.7s at ¾ BW
Multi-segment anatomical foot model

66. Bony Motion Anatomical multi-segment foot model
Methods
Bony Motion
Anatomical multi-segment foot model
digitized virtual points
bone pins and quad clusters

67. Methods
Bony Motion
RGS simulation at ¾ body weight and 2.7 s
video

68. Bony Motion Motion of 17 joints recorded
Results
Bony Motion
Motion of 17 joints recorded
Midfoot joints have substantial motion
Range of motion: 23.2± 4.6
Range of motion: 12.2± 2.2

69. Bony Motion In vitro results consistent with invasive in vivo data
Discussion
Bony Motion
In vitro results consistent with invasive in vivo data
Results indicate limitations of simplified rigid body models
Better understanding of
midtarsal joint
midfoot motion
inter-metatarsal mobility

70. Conclusion Dynamic vGRF tracking performance open loop Fuzzy v 1.0
Stance phase (%)
Force (N/ ½ BW)
100
in vivo
in vitro
open loop
Fuzzy v 1.0
Fuzzy v 2.0

71. Conclusion iterative learning manual open loop fuzzy logic 1.0
speed
vGRF control
Clinical study
1.5 s
prosthetic gait simulation
iterative learning
static
flatfoot simulation
manual
10 s
arthrodesis of first MTPJ
open loop
10 s
long second metatarsal
fuzzy logic 1.0
2.7 s
foot bony motion
fuzzy logic 2.0

72. Acknowledgements
Department of Veterans Affairs, Research Rehabilitation and Development Service grant numbers A2661C, A3923R, A6669R and A4843C.

73. Special thanks to:
Center of Excellence for Limb Loss Prevention and Prosthetic Engineering

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

75. Extra slides

76. Motivation Introduction Hypothesis Conclusion scientific method
Gray's Anatomy of the Human Body
The function of A is B.
Condition A causes disease B (etiology).
Treatment A has a better outcome than treatment B.
Experiment
Data
(Results)
Cadaveric
Model
Living subjects
Computational

77. State of the Art Dynamic cadaveric gait simulators Introduction
Pennsylvania State U.
U. of Salford and Iowa State U.
Medical School at Hannover, Germany
U. of Wisconsin-Milwaukee and Mayo Clinic
Cleveland Clinic
Vertical GRF control:
iterative control
Tibia DOF 6
Speed
3.2 s
Vertical GRF control:
Force control
Tibia DOF 3
Speed
60 s
Vertical GRF control
Trial and error
Tibia DOF 3
Speed
2 s
Vertical GRF control
Trial and error
Tibia DOF 3
Speed
12 s
Vertical GRF control:
Trial and error
Tibia DOF 3
Speed
20 s

78. Open Loop vGRF Control Achilles tendon dictates vGRF Results Achilles
1.0
Force (N/ ½ BW)
0.5

79. Fuzzy logic 1.0 vGRF Control
Methods
Fuzzy logic 1.0 vGRF Control
RGS Block diagram with fuzzy logic controller
R2000
PID
tendon
force
controller
electric
motor
actuators
cadaveric
foot
tendons
plantar surface
load cell DA AD
fuzzy
logic
vGRF
∆FAch
trajectory
generator
vGRFtarget +
∆xj
FAch
Ftendonx 8
in vivo tibial kinematics
force plate
vGRFactual Σ _
operator

80. Long Second Metatarsal
Results
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)

81. Fuzzy Logic 2.0 vGRF Control
Methods
Fuzzy Logic 2.0 vGRF Control
Stance phase events
foot flat
16.6%
COP under met heads
43.5%
heel rise
50%
peak TA force
~18%

82. Fuzzy Logic 2.0 vGRF Control
Methods
Fuzzy Logic 2.0 vGRF Control
R2000 trajectory optimization to increase speed
Vicon
Plate trajectory
Inverse kinematic map
Motor velocity
Optimization
Best TIB pose
ROB

83. Fuzzy Logic 2.0 vGRF Control
Results
Fuzzy Logic 2.0 vGRF Control
Within subject variability

84. Fuzzy Logic 2.0 vGRF Control
Results
Fuzzy Logic 2.0 vGRF Control
Medial/lateral and anterior/posterior GRF similar to in vivo
medial/lateral
anterior/posterior

85. Fuzzy Logic 2.0 vGRF Control
Results
Fuzzy Logic 2.0 vGRF Control
Precise tibial kinematics

86. Conclusion Gait simulator comparison open loop 100 12 3 50 2
system
vGRF control
vGRF (%)
speed (s)
tibial DOF
Pennsylvania State Univ.
open loop
100 12 3
Univ. of Salford & Iowa State
Univ. 50 2
Medical School of Hannover,
Germany
force control 60
Univ. of Wisconsin-
Milwaukee & Mayo Clinic 40 20
Cleveland Clinic
iterative control
3.2 6
VA RR&D
fuzzy logic 75
2.7