Study on recognition of multi-mode hand gestures based on myoelectric signal

The development of multi-DOF prosthetic hand appeals for classfying more hand gestures based on myoelectric signals extracted from the forearm. Six surface electromyography (EMG) electrodes were used to acquire myoelectric signals. Each channel's sample means are used to constitute feature vectors for training the support vector machines (SVM). Then 19 modes of hand gestures can be discriminated effectively. Comparing with some traditional training methods that use the steady-state features of myoelectric signals, the new method improves the predicting accuracy both on full scale features and the ones between the mode transitions. This will benefit the on-line recognition of the hand gestures, therefore make the multi-DOF prosthetic hand's EMG control more intuitive and effective.