TY - JOUR
T1 - A Probabilistic Analysis of Muscle Force Uncertainty for Control
AU - Berniker, Max
AU - Jarc, Anthony
AU - Kording, Konrad
AU - Tresch, Matthew
N1 - Funding Information:
This work was supported by NSF 1200830 (MB), NIH F31NS068030-01 (AJ), NIH 1R01-NS-063399 (KK), and NSF 0932263 and NIH R21NS061208 (MT).
Publisher Copyright:
© 2016 IEEE.
PY - 2016/11
Y1 - 2016/11
N2 - Background: We control the movements of our body and limbs through our muscles. However, the forces produced by our muscles depend unpredictably on the commands sent to them. This uncertainty has two sources: irreducible noise in the motor system's processes (i.e., motor noise) and variability in the relationship between muscle commands and muscle outputs (i.e., model uncertainty). Any controller, neural or artificial, benefits from estimating these uncertainties when choosing commands. Methods: To examine these benefits, we used an experimental preparation of the rat hindlimb to electrically stimulate muscles and measure the resulting isometric forces. We compare a functional electric stimulation (FES) controller that represents and compensates for uncertainty in muscle forces with a standard FES controller that neglects uncertainty. Results: Accounting for uncertainty substantially increased the precision of force control. Conclusion: Our study demonstrates the theoretical and practical benefits of representing muscle uncertainty when computing muscle commands. Significance: The findings are relevant beyond FES as they highlight the benefits of estimating statistical properties of muscles for both artificial controllers and the nervous system.
AB - Background: We control the movements of our body and limbs through our muscles. However, the forces produced by our muscles depend unpredictably on the commands sent to them. This uncertainty has two sources: irreducible noise in the motor system's processes (i.e., motor noise) and variability in the relationship between muscle commands and muscle outputs (i.e., model uncertainty). Any controller, neural or artificial, benefits from estimating these uncertainties when choosing commands. Methods: To examine these benefits, we used an experimental preparation of the rat hindlimb to electrically stimulate muscles and measure the resulting isometric forces. We compare a functional electric stimulation (FES) controller that represents and compensates for uncertainty in muscle forces with a standard FES controller that neglects uncertainty. Results: Accounting for uncertainty substantially increased the precision of force control. Conclusion: Our study demonstrates the theoretical and practical benefits of representing muscle uncertainty when computing muscle commands. Significance: The findings are relevant beyond FES as they highlight the benefits of estimating statistical properties of muscles for both artificial controllers and the nervous system.
KW - Biological control systems
KW - computational biology
KW - control design
KW - force control
KW - functional electrical stimulation
KW - muscle force uncertainty
KW - nonlinear control systems
KW - open loop systems
KW - optimal control
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U2 - 10.1109/TBME.2016.2531083
DO - 10.1109/TBME.2016.2531083
M3 - Article
C2 - 26890530
AN - SCOPUS:84994479776
VL - 63
SP - 2359
EP - 2367
JO - IRE transactions on medical electronics
JF - IRE transactions on medical electronics
SN - 0018-9294
IS - 11
M1 - 7410020
ER -