Upload
ton-van-den-bogert
View
270
Download
0
Tags:
Embed Size (px)
DESCRIPTION
Presented at World Congress of Biomechanics 2010. Abstract:
Citation preview
Optimal Feedback Control for Human Gait with Functional Electrical Stimulation
Ton van den BogertOrchard Kinetics LLC, Cleveland OH
Elizabeth HardinCleveland FES Center
Cleveland VA Medical Center
Functional Electrical Stimulation (FES) for gait
• Open loop stimulation patterns• Stability achieved via:
– upper body support– passive constraints on joint motion
• Long term goal: feedback control
Hardin, et al, J Rehabil Res Dev 44(3), 2007.
Model-based approach
• Musculoskeletal model• Make it walk with open loop control• Add feedback
– Muscle spindles (for comparison)– Joint angles– Joint angular velocities– Forefoot pressure
• Evaluate stability as function of– Feedback type– Feedback gain
+
Methods
Generic musculoskeletal model
• 2D, 7 segments, 9 degrees of freedom• 16 Hill-based muscles• 50 state variables x(t)
– 9 generalized coordinates– 9 generalized velocities– 16 muscle active states– 16 muscle contractile states
• 16 muscle stimulations u(t)• Dynamic model:
glutei
iliopsoas
hamstrings
rectus femoris
vasti
gastrocnemius
soleus
tibialis anterior
u)f(x,x
Open loop optimal control
• Make model walk like a human– Track joint angles & ground reaction forces– Minimal effort
• Find x(t),u(t) such that– Objective function is minimized:
and constraints are satisfied• Dynamics:• Periodicity:
– Solved via direct collocation method• (Ackermann & van den Bogert, J Biomech 2010)
u)f(x,x vTT )()( 0xx
N
i
N
i
M
jjieffort
V
j j
jiji uMN
Wms
NVJ
1 1 1
2
1
2
11
tracking effort
Sensors for feedback
• 30 sensor signals s(t)– 2 forefoot pressures– 16 spindle signals d/dt(fiber length)– 6 joint angles– 6 joint angular velocities
• Sensor signals are a function of system state:
s(t) = s(x(t)) sensor model
+
Model with feedback• Open loop optimal control solution xO(t), uO(t)• Feedback controller:
– u = uO(t) + G·[ s – s(xO(t)) ]• Gain matrix (16 x 30)
• Magnitude of gains was varied– Signs fixed, positive (●) or negative (●)
G =
feet ang.velanglesspindles
right side muscles
left side muscles
Formal stability analysis
• Linearization: (xk+1 – x*) = A·(xk – x*)• Matrix A calculated from model• Eigenvalues of A: Floquet multipliers λ (50)• Floquet exponents: μ = log(λ)/T
– Maximum Floquet Exponent: MFE (s-1) (stable: <0)
Dingwell & Kang, J Biomech Eng 2007.
Floquet analysisQuantify the growth/damping of perturbations from one gait cycle to the next
“Anecdotal” stability analysis
• Perturb forward velocity by 2%• Simulate half a gait cycle• By how much has the trunk fallen?
– Vertical Trunk Excursion (VTE)
initial state final state
VTE
Results and Discussion
Open loop optimal control solution
-10
0
10
20
30Hip Angle
[degre
es]
0
20
40
60Knee Angle
70
80
90
100Ankle Angle
File name: ./result100half.mat
Number of nodes: 100
Initial guess: ../007result.mat
Model used: ../../Legs2dMEX/CCFmodel
Gait data tracked: ../wintergaitdata.mat
Weffort: 1
Norm of constraints: 0.00092369
Cost function value: 0.029958
0
0.2
0.4
0.6
0.8
1
1.2 GRF Y
[BW
]
0 50 100
-0.2
-0.1
0
0.1
0.2GRF X
[BW
]
Time [% of gait cycle]
0400 Muscle Forces
Ilio
0
400
Glu
0
600
Ham
0150
RF
0
600
Vas
0
1500
Gas
01000
Sol
0 50 1000
800
TA
0
1
Ilio Muscle Activations
0
1
Glu
0
1
Ham
0
1R
F
0
1
Vas
0
1
Gas
0
1
Sol
0 50 1000
1
TA
Muscle spindle feedback
0 1 2 30
5
10
15
20
Spindle gain (m-1 s)
Max
Flo
quet
Exp
onen
t (s
-1)
0 1 2 30
0.05
0.1
0.15
0.2
Spindle gain (m-1 s)
Ver
tical
Tru
nk E
xcur
sion
(m
)Floquet VTE
gain = 1.96 m-1 sgain = 0
0 0.5 1 1.5 24
6
8
10
12
14
16
angle gain (rad-1)
Max
Flo
quet
Exp
onen
t (s
-1)
0 0.5 1 1.5 20
0.05
0.1
0.15
0.2
angle gain (rad-1)
Ver
tical
Tru
nk E
xcur
sion
(m
)
Joint angle feedback
Floquet VTE
gain = 0.7 rad-1gain = 0
Joint angular velocity feedback
0 0.1 0.2 0.3 0.4 0.50
5
10
15
angular velocity gain (rad-1 s)
Max
Flo
quet
Exp
onen
t (s
-1)
0 0.1 0.2 0.3 0.4 0.50
0.05
0.1
0.15
0.2
angular velocity gain (rad-1 s)
Ver
tical
Tru
nk E
xcur
sion
(m
)
Floquet VTE
gain = 0.22 rad-1 sgain = 0
0 1 2 3
x 10-3
10
15
20
25
30
35
40
GRF gain (N-1)
Max
Flo
quet
Exp
onen
t (s
-1)
0 1 2 3
x 10-3
0
0.05
0.1
0.15
0.2
GRF gain (N-1)
Ver
tical
Tru
nk E
xcur
sion
(m
)
Forefoot pressure feedbackFloquet VTE
gain = 0.00138 N-1gain = 0
Effect of simple feedback
• Feedback from each type of sensor could improve stability
• Agreement between Floquet analysis and finite perturbation response
• An optimal feedback gain always existed• Stability (MFE<0) was not yet achieved
– Feedback from combination of sensor types?
00.1
0.20.3
0.4
0
1
2-5
0
5
10
angular velocity gain (rad-1 s)angle gain (rad-1)
Max
. F
loqu
et E
xpon
ent
(s-1
)
Combined feedback
• Lowest MFE: −0.1482 s-1
– Angle gain 1.40 rad-1
– Angular velocity gain 0.12 rad-1 s
angle gain (rad -1) angular velocity gain (rad-1 s)
MFE (s-1)
Continuous walking with optimal combined feedback
• Why not stable, as predicted by MFE?• Limitations of Floquet analysis
– accuracy– linearization
Limitations of control system
• Sensors– All sensors in one group had same gain– Limited sensor combinations were tested– Missing sensors
• Vestibular, etc.
• Physiological feedback is not always linear– Threshold effects– Reflex modulation– Stumble response
Acknowledgments
• Programming: – Marko Ackermann
• U.S Department of Veterans Affairs– B4668R (Hardin)– B2933R (Triolo)
Forefoot pressure
• Can possibly:– help control timing of push off– help stabilize against forward fall
• Evidence in cats and humans– Pratt, J Neurophysiol 1995; Nurse & Nigg, Clin Biomech 2001
• Theoretically useful in control of posture and hopping– Prochazka et al, J Neurophysiol 1997; Geyer et al., Proc R Soc Lond 2003
• Gait?
+