Algorithm to demodulate an electromyogram signal modulated by essential tremor

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 daily living activities such as meal movement. We have been developing an electromyogram (EMG)-controlled exoskeleton to suppress tremors to support the movements of these patients. The problem is that the EMG signal of the patients is modulated by the tremor signal as multiplicative noise. In this paper, we proposed a novel signal processing method to demodulate patients’ EMG signals. We modelled the multiplicative tremor signal with a powered sine wave and the tremor signal in the EMG signal was removed by dividing the modelled tremor signal into the EMG signal. To evaluate the effectiveness of the demodulation, we applied the method to a real patient’s EMG signal, extracted from biceps brachii while performing an elbow flexion. We quantified the effect of the demodulation by root mean square error between two kinds of muscle torques, an estimated torque from the EMG signal and calculated torque from inverse dynamics based on the motion data. The proposed method succeeded in reducing the error by approximately 15–45% compared with using a low-pass filter, typical processing for additive noise, and showed its effectiveness in the demodulation of the patients’ EMG signal.