Essential Tremor (ET) refers to involuntary movements of a part of the body. ET patients have serious difficulties in performing daily living activities. We have been developing a myoelectric controlled exoskeletal robot to suppress tremor. However, the EMG signal of ET patients contains not only signals from voluntary movements but also noise from involuntary tremors. Then, we have developed a signal processing method to suppress tremor noise present in the surface EMG signal. The proposed filter is based on the hypothesis that tremor noise could be approximate to powered sine wave. In this paper, we have integrated tremor canceling filter and neural network (NN) to recognize the tremor patient’s movement. According to the result, it was confirmed that the proposal filter increased accuracy of recognition, especially stable phase on elbow flexed position as “OFF”.

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