Forward Estimation of Joint Torque from EMG Signal through Muscle Synergy Combinations - LIRMM - Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier Access content directly
Conference Papers Year : 2013

Forward Estimation of Joint Torque from EMG Signal through Muscle Synergy Combinations

Abstract

Human movement is a result of synergetic combinations of multiple muscle contractions. The summation of motor unit action potentials can be measured through Electromyography (EMG), thus the processed EMG can be re- garded as the muscle activation to be employed to estimate joint movement or torque production. Such forward relationship for representing the joint torque can be established and identified through associated EMG/activations of extension and flexion muscle groups. On the other hand, muscle synergy always exists indicating how quantitatively central nervous system (CNS) drives correlated muscle groups to accomplish the joint torque generation. In this paper, we investigate the approaches of estimating the ankle joint torque from EMG/activatons of associated muscle groups. The approaches discussed fall into two main categories: i) full utilization of both of extension and flexion EMG/activations for estimating the joint torque; ii) exploitation of muscle synergy extraction of EMG/actvations and consequent usage of extracted components in reduced space for estimating the joint torque. Comparison is made between the two methods with experimental data of five able-bodied subjects. From the results we conclude that, method ii) with muscle synergy extraction may not degrade the performance of method i) but meanwhile show the the muscle synergic ratios for generating the joint torque, and involvement of joint position and velocity information can improve the estimation for both methods.
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Dates and versions

lirmm-00853197 , version 1 (22-08-2013)

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  • HAL Id : lirmm-00853197 , version 1

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Zhan Li, Mitsuhiro Hayashibe, David Guiraud. Forward Estimation of Joint Torque from EMG Signal through Muscle Synergy Combinations. NER: Neural Engineering, Nov 2013, San Diego, CA, United States. ⟨lirmm-00853197⟩
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