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

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 0.05 s 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.