MODELING FES PROTOCOLS FOR CORRECTING JOINT MOMENTS IN POST-STROKE SUBJECTS
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Muscle. 8:30, Room 103, Presentation 0387 S219 MODELING FES PROTOCOLS FOR CORRECTING JOINT MOMENTS IN POST-STROKE SUBJECTS Qi Shao, Daniel N. Bassett, Kurt Manal and Thomas S. Buchanan University of Delaware: Center for Biomedical Engineering Research; email: [email protected] INTRODUCTION EMG-driven neuromusculoskeletal models have been developed by many investigators to estimate joint moments and muscle forces during human movements. We have been exploring how to use these models as a platform for studying how to alter human movement by changing muscle activation patterns in patients with neurological disorders. Muscle activity can be changed (increased) using functional electrical stimulation. Hence, simulating the effects of changing muscle activation provides a useful tool to study the effects of this type of rehabilitation. modeling An ankle joint moment observed during gait in a moderately impaired post-stroke patient was used to determine if other (theoretical) muscle activation patterns could be employed to produce an improved moment profile taken from a healthy person. This simulates a therapeutic intervention whereby muscles are activated using FES. Note that the changes in activation can only be used to increase muscle activation, not to decrease it. METHODS Three types of data were collected from one healthy and one post-stroke subject during isokinetic and gait trials: (1) EMG from the tibialis anterior, medial gastrocnemius, lateral gastrocnemius, and soleus; (2) joint position, (3) and reaction forces (from the ground or dynamometer). Two methods were used to calculate joint moment: (1) forward dynamics using EMG and joint position data, and (2) inverse dynamics using joint position and reaction force data. The forward dynamics calculation was based on a series of unknown parameters and was comprised of three elements: (1) muscle activation dynamics, (2) muscle contraction dynamics, (3) musculoskeletal geometry. Muscle activation dynamics started from raw EMG and rectified, filtered, and normalized it then a discretized recursive filter gave neural activation which was non-linearized into muscle activation. Muscle contraction dynamics used a Hill-type model approach deriving muscle force from a combination of active, passive, and fiber-velocity-dependent force which were calculated from muscle activation and the SIMM [1] output muscle tendon lengths. Relevant musculoskeletal geometry was the muscle moment arms from SIMM which, along with muscle force, gave total joint moment [2]. The calibration process involved varying the unknown parameters necessary in forward dynamics by simulated annealing on a Beowulf cluster [3] to optimize the fit of the forward dynamics joint moment to the joint moment from inverse dynamics (Figure 1). Once the fit was achieved the parameters were used in the forward dynamic prediction of joint moment for trials the model had not been calibrated. A forward dynamics EMG-driven model was used to estimate forces in four muscles that cross the ankle. Gait analysis was used to estimate ankle joint moments using inverse dynamics, which was compared to model estimates of joint moments. Subjects were studied during a simple walking task. An altered joint moment profile was then artificially created and EMGs were then adjusted in the model to determine if a set of EMGs could be found to produce the new joint moments. The “new EMGs” were based on minimizing changes to the existing EMGs. RESULTS AND DISCUSSION The EMG-driven neuromusculoskeletal model predicted the ankle joint moment well. Through the comparison of forward dynamic joint moment with inverse dynamic joint moment, we found that the root mean square (RMS) value was 3.7 N m. Furthermore, we observed that it was possible to find a solution to the problem of matching a pre- determined joint moment profile by adding to the muscle activations. When the muscle activations were changed to yield the new joint moment profile, the error was similar in magnitude to that observed in the calibration process. Examination of the new muscle forces demonstrate that while some of the muscles required high levels of additional activation, a solution could be found that satisfied the constraint criteria (positive yet less than maximal force). CONCLUSIONS This demonstrates a novel yet practical way neuromusculoskeletal models can be used to estimate the amount of electrical stimulation necessary to correct giat abnormalities. It is particularly novel in that it allows for control/stimulation of individual muscles. Combining this model with FES tools [4] will provide a new means to model the protocols for stroke rehabilitation. REFERENCES 1. Delp SL, et al. Comput Biol Med 25(1), 21-34, 1995 2. Buchanan TS, et al. J App. Biomech, 20, 367-395, 2004 3. Higginson JS, et al. J Biomech, 238, 1938-1942, 2005. 4. Ding J, et al., Musc Nerve, 26, 477-485, 2002. ACKNOWLEDGEMENTS Supported, in part, by NIH R01-HD38582 (TSB). Tuned forward dynamics model Initial EMG Calculated joint moment M C Moment Comparision + + Optimization using constrained simulated annealing Cost function: EMG, F m 2 etc. 0 ) ( 2 D C M M EMG Desired joint moment M D Corrected joint angles Tuned forward dynamics model Initial EMG Calculated joint moment M C Moment Comparision + + Optimization using constrained simulated annealing Cost function: EMG, F m 2 etc. 0 ) ( 2 D C M M EMG Desired joint moment M D Corrected joint angles Figure 1: Model to estimate corrective changes in muscle activation patterns. XXI ISB Congress, Podium Sessions, Tuesday 3 July 2007 Journal of Biomechanics 40(S2)
Transcript of MODELING FES PROTOCOLS FOR CORRECTING JOINT MOMENTS IN POST-STROKE SUBJECTS
Muscle. 8:30, Room 103, Presentation 0387 S219
MODELING FES PROTOCOLS FOR CORRECTING JOINT MOMENTS IN POST-STROKE SUBJECTS
Qi Shao, Daniel N. Bassett, Kurt Manal and Thomas S. Buchanan University of Delaware: Center for Biomedical Engineering Research; email: [email protected]
INTRODUCTION EMG-driven neuromusculoskeletal models have been developed by many investigators to estimate joint moments and muscle forces during human movements. We have been exploring how to use these models as a platform for studying how to alter human movement by changing muscle activation patterns in patients with neurological disorders. Muscle activity can be changed (increased) using functional electrical stimulation. Hence, simulating the effects of changing muscle activation provides a useful tool to study the effects of this type of rehabilitation. modeling
An ankle joint moment observed during gait in a moderately impaired post-stroke patient was used to determine if other (theoretical) muscle activation patterns could be employed to produce an improved moment profile taken from a healthy person. This simulates a therapeutic intervention whereby muscles are activated using FES. Note that the changes in activation can only be used to increase muscle activation, not to decrease it.
METHODSThree types of data were collected from one healthy and one post-stroke subject during isokinetic and gait trials: (1) EMG from the tibialis anterior, medial gastrocnemius, lateral gastrocnemius, and soleus; (2) joint position, (3) and reaction forces (from the ground or dynamometer). Two methods were used to calculate joint moment: (1) forward dynamics using EMG and joint position data, and (2) inverse dynamics using joint position and reaction force data.
The forward dynamics calculation was based on a series of unknown parameters and was comprised of three elements: (1) muscle activation dynamics, (2) muscle contraction dynamics, (3) musculoskeletal geometry. Muscle activation dynamics started from raw EMG and rectified, filtered, and normalized it then a discretized recursive filter gave neural activation which was non-linearized into muscle activation. Muscle contraction dynamics used a Hill-type model approach deriving muscle force from a combination of active, passive, and fiber-velocity-dependent force which were calculated from muscle activation and the SIMM [1] output muscle tendon lengths. Relevant musculoskeletal geometry was the muscle moment arms from SIMM which, along with muscle force, gave total joint moment [2].
The calibration process involved varying the unknown parameters necessary in forward dynamics by simulated annealing on a Beowulf cluster [3] to optimize the fit of the forward dynamics joint moment to the joint moment from inverse dynamics (Figure 1). Once the fit was achieved the parameters were used in the forward dynamic prediction of joint moment for trials the model had not been calibrated.
A forward dynamics EMG-driven model was used to estimate forces in four muscles that cross the ankle. Gait analysis was used to estimate ankle joint moments using
inverse dynamics, which was compared to model estimates of joint moments. Subjects were studied during a simple walking task. An altered joint moment profile was then artificially created and EMGs were then adjusted in the model to determine if a set of EMGs could be found to produce the new joint moments. The “new EMGs” were based on minimizing changes to the existing EMGs.
RESULTS AND DISCUSSION The EMG-driven neuromusculoskeletal model predicted the ankle joint moment well. Through the comparison of forward dynamic joint moment with inverse dynamic joint moment, we found that the root mean square (RMS) value was 3.7 N m. Furthermore, we observed that it was possible to find a solution to the problem of matching a pre-determined joint moment profile by adding to the muscle activations. When the muscle activations were changed to yield the new joint moment profile, the error was similar in magnitude to that observed in the calibration process. Examination of the new muscle forces demonstrate that while some of the muscles required high levels of additional activation, a solution could be found that satisfied the constraint criteria (positive yet less than maximal force).
CONCLUSIONSThis demonstrates a novel yet practical way neuromusculoskeletal models can be used to estimate the amount of electrical stimulation necessary to correct giat abnormalities. It is particularly novel in that it allows for control/stimulation of individual muscles. Combining this model with FES tools [4] will provide a new means to model the protocols for stroke rehabilitation.
REFERENCES 1. Delp SL, et al. Comput Biol Med 25(1), 21-34, 1995 2. Buchanan TS, et al. J App. Biomech, 20, 367-395, 2004 3. Higginson JS, et al. J Biomech, 238, 1938-1942, 2005. 4. Ding J, et al., Musc Nerve, 26, 477-485, 2002.
ACKNOWLEDGEMENTS Supported, in part, by NIH R01-HD38582 (TSB).
Tuned forward dynamics model
Initial EMG
Calculated joint moment MC
Moment Comparision
+
+
Optimization using constrained simulated annealing
Cost function: EMG, Fm
2 etc.
0)( 2DC MM
EMG
Desired joint moment MD
Corrected joint angles Tuned forward
dynamics model
Initial EMG
Calculated joint moment MC
Moment Comparision
+
+
Optimization using constrained simulated annealing
Cost function: EMG, Fm
2 etc.
0)( 2DC MM
EMG
Desired joint moment MD
Corrected joint angles
Figure 1: Model to estimate corrective changes in muscle activation patterns.
XXI ISB Congress, Podium Sessions, Tuesday 3 July 2007 Journal of Biomechanics 40(S2)
