Improvement of EMG Pattern Recognition by Eliminating Posture-Dependent Components

Recently, myoelectric interfaces have been intensively studied in various research fields. Because electromyography (EMG) is a bioelectrical signal, it can be influenced by many disturbing factors, e.g., electrode displacement, postural changes, and individual-dependent features like condition of muscles, subcutaneous fat, skin surface, etc., thus, it is difficult to realize high classification accuracy. To solve the problem, an EMG pattern classification method, which decomposes raw EMG signals into user/motion-dependent components by using a bilinear model, has been proposed. This enabled to reduce the time for classifier re-learning, however classification accuracy has not yet been sufficient. In the current study, we propose a signal decomposing method in consideration of the effect by forearm postures, in order to extract informative factors that correctly reflect hand gestures. We investigated the influences of postural changes exert on the classification accuracy of hand gestures, and tried to separate not only user dependent factor, but also posture-dependent component from EMG signals. As a result, we found that postural change decreases classification accuracy of approximately 20 % and we confirmed availability of our proposed method.