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This paper describes an optimal mapping of the torus self‐organizing map for human forearm motion discrimination on the basis of myoelectric signals. This study uses the torus self‐organizing map (torus SOM) for motion discrimination. The normal SOM identifies input data of the same feature group by using all units of the map. But there is then a possibility of misrecognition of motion around the boundary lines of the motion groups. Therefore, this study proposes a mapping method of SOM in which learning units of the same motion concentrate on one local range and the learning unit groups of each motion are sufficiently separated. As a result, the variance in the same motion group becomes small and the variance between motion groups becomes large. Experiments on a myoelectric hand simulator show the effectiveness of the proposed motion discrimination method. © 2012 Wiley Periodicals, Inc. Electron Comm Jpn, 95(6): 24–31, 2012; Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/ecj.11369

This paper describes a human forearm motion discrimination method based on the myoelectric signal by the fuzzy inference. In the conventional studies, the neural network is often used to estimate motion intention by the myoelectric signal and realizes the high discrimination precision. On the other hand, this study uses the fuzzy inference for a human forearm motion discrimination based on the myoelectric signal. This study designs the membership function and the fuzzy rules from the average value and the standard deviation of the root mean square of the myoelectric potential for every channel of each motion. Some experiments on the myoelectric hand simulator show the effectiveness of the proposed motion discrimination method.

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This paper describes a robust motion discrimination method based on the myoelectric potential of human forearm by the adaptive fuzzy inference considering the muscle fatigue. In the conventional studies, a motion discrimination based on the myoelectric potential of human forearm realizes the high discrimination precision. However, the characteristic of the myoelectric potential gradually changes for muscle fatigue. Therefore the motion discrimination considering muscle fatigue is required. The purpose of this study is to correspond to the change in the myoelectric potential by the muscle fatigue and keep the high discrimination precision. This study proposes the redesign method of the fuzzy inference adapting to the dynamic change of the myoelectric potential by the muscle fatigue. Some experiments on the myoelectric hand simulator show the effectiveness of the proposed motion discrimination method.