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

Zhan Li 1, 2 Mitsuhiro Hayashibe 1, 2 David Guiraud 2
2 DEMAR - Artificial movement and gait restoration
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
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.
Type de document :
Communication dans un congrès
NER: Neural Engineering, Nov 2013, San Diego, CA, United States. 6th Annual International IEEE EMBS Conference on Neural Engineering, 2013
Liste complète des métadonnées

https://hal-lirmm.ccsd.cnrs.fr/lirmm-00853197
Contributeur : Zhan Li <>
Soumis le : jeudi 22 août 2013 - 10:28:03
Dernière modification le : jeudi 24 mai 2018 - 15:59:23

Identifiants

  • HAL Id : lirmm-00853197, version 1

Collections

Citation

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. 6th Annual International IEEE EMBS Conference on Neural Engineering, 2013. 〈lirmm-00853197〉

Partager

Métriques

Consultations de la notice

403