TY - JOUR
T1 - A computational model of limb impedance control based on principles of internal model uncertainty
AU - Mitrovic, Djordje
AU - Klanke, Stefan
AU - Osu, Rieko
AU - Kawato, Mitsuo
AU - Vijayakumar, Sethu
N1 - Copyright:
Copyright 2011 Elsevier B.V., All rights reserved.
PY - 2010
Y1 - 2010
N2 - Efficient human motor control is characterized by an extensive use of joint impedance modulation, which is achieved by co-contracting antagonistic muscles in a way that is beneficial to the specific task. While there is much experimental evidence available that the nervous system employs such strategies, no generally-valid computational model of impedance control derived from first principles has been proposed so far. Here we develop a new impedance control model for antagonistic limb systems which is based on a minimization of uncertainties in the internal model predictions. In contrast to previously proposed models, our framework predicts a wide range of impedance control patterns, during stationary and adaptive tasks. This indicates that many well-known impedance control phenomena naturally emerge from the first principles of a stochastic optimization process that minimizes for internal model prediction uncertainties, along with energy and accuracy demands. The insights from this computational model could be used to interpret existing experimental impedance control data from the viewpoint of optimality or could even govern the design of future experiments based on principles of internal model uncertainty.
AB - Efficient human motor control is characterized by an extensive use of joint impedance modulation, which is achieved by co-contracting antagonistic muscles in a way that is beneficial to the specific task. While there is much experimental evidence available that the nervous system employs such strategies, no generally-valid computational model of impedance control derived from first principles has been proposed so far. Here we develop a new impedance control model for antagonistic limb systems which is based on a minimization of uncertainties in the internal model predictions. In contrast to previously proposed models, our framework predicts a wide range of impedance control patterns, during stationary and adaptive tasks. This indicates that many well-known impedance control phenomena naturally emerge from the first principles of a stochastic optimization process that minimizes for internal model prediction uncertainties, along with energy and accuracy demands. The insights from this computational model could be used to interpret existing experimental impedance control data from the viewpoint of optimality or could even govern the design of future experiments based on principles of internal model uncertainty.
UR - http://www.scopus.com/inward/record.url?scp=78149424670&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78149424670&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0013601
DO - 10.1371/journal.pone.0013601
M3 - Article
C2 - 21049061
AN - SCOPUS:78149424670
VL - 5
JO - PLoS One
JF - PLoS One
SN - 1932-6203
IS - 10
M1 - e13601
ER -