Portable hand motion classifier for multi-channel surface electromyography recognition using grey relational analysis

Authors:
Yi-Chun Du;Chia-Hung Lin;Liang-Yu Shyu;Tainsong Chen
Affiliations:
Institute of Biomedical Engineering, National Cheng Kung University, Tainan 70101, Taiwan;Department of Electrical Engineering, Kao-Yuan University, Kaohsiung 821, Taiwan;Department of Biomedical Engineering, Chung-Yuan Christian University, Chung-Li 320, Taiwan;Institute of Biomedical Engineering, National Cheng Kung University, Tainan 70101, Taiwan
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

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Abstract

This paper proposes the portable hand motion classifier (HMC) for multi-channel surface electromyography (SEMG) recognition using grey relational analysis (GRA). SEMG provides information on motion detection of flexion and extension of fingers, wrist, forearm, and arm. A portable HMC is developed to identify hand motion from the SEMG signals with an electrode configuration system (ECS) and GRA-based classifier. The ECS consists of seven active electrodes place around the forearm to acquire the multi-channel SEMG signals of corresponding muscle groups. Six parameters are extracted from each electrode channel and various 42 (7 Channels by 6 Parameters) parameters could be constructed as specific patterns. Sequentially, these patterns are sent to the GRA-based classifier to recognize 11 hand motions. Twelve subjects including eight males and four females participate in this study. Compared with the multi-layer neural networks (MLNNs) based classifier, GRA demonstrates the processing time, computational efficiency, and accurate recognition for recognizing SEMG signals. It takes about 0.05s CPU time to identify each hand motion which is close to a real-time process. Therefore, the GRA-based classifier could be further recommend to implement in prosthesis control, robotic manipulator or hand motion classification applications.