Development of a filtering algorithm to demodulate electromyogram signal of essential tremor patients

Essential tremor is a disorder that causes involuntary oscillations in patients both while they are engaged in actions and when maintaining a posture. Such patients face serious difficulties in performing such daily living activities as eating, drinking, and writing. We have been developing an electromyogram (EMG)-controlled exoskeleton to suppress tremors and to support the movements of these patients. In this paper, we proposed a novel signal processing method to demodulate the patients' EMG signal, which contains the voluntary movement signal and the tremor noise. This is a real-time signal processing method for multiple types of noise. From a technical point of view, this processing is essential because, at present, there are no real-time methods addressing the multiple noise sources. Also, from an application point of view, this method is essential for the accurate control of the exoskeleton depending on the patients' voluntary movement intention. We confirmed that the proposed method showed feasibility to demodulate the EMG signal which is modulated by the tremor signal.