Application of multi-output support vector regression on EMGs to decode hand continuous movement trajectory.

Journal: Bio-medical materials and engineering
Published Date: Jan 1, 2015

Abstract

Applications of neural machine interfaces have received increased attention during the last decades. It is crucial to realize the continuous control of prosthetic devices based on biological signals. In order to deal with the highly nonlinear relationship between the Electromyography (EMG) signals and motion, this study presents a novel decoding approach which employs multi-output support vector regression (M-SVR). The proposed M-SVR is compared with other popular regression techniques and the experimental results demonstrate the effectiveness of M-SVR in hand continuous movement trajectory reconstruction.

Authors

  • Pan Tian
    State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, P.R. China.
  • Jie Hu
    Corteva Agriscience, Farming Solutions and Digital, Indianapolis, IN, United States.
  • Jin Qi
    State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, P.R. China.
  • Peng Xia
    State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, P.R. China.
  • Ying-Hong Peng
    State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, P.R. China.

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