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

Issue title: Frontiers in Biomedical Engineering and Biotechnology – Proceedings of the 4th International Conference on Biomedical Engineering and Biotechnology, 18–21 August 2015, Shanghai, China

Guest editors: Feng Liu, Dong-Hoon Lee, Ricardo Lagoa and Sandeep Kumar

Article type: Research Article

Authors: Tian, Pan | Hu, Jie; * | Qi, Jin | Xia, Peng | Peng, Ying-Hong

Affiliations: State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, P.R. China

Correspondence: [*] Address for correspondence: Jie Hu, State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, P.R. China. Tel.: +86-21-34206552; Fax: +86-21-34206313; E-mail: [email protected].

Keywords: Electromyography (EMG), M-SVR, trajectory decoding, myoelectric control

DOI: 10.3233/BME-151348

Journal: Bio-Medical Materials and Engineering, vol. 26, no. s1, pp. S575-S582, 2015

Published: 2015
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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.

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