Tremor frequency based filter to extract voluntary movement of patients with essential tremor

Essential Tremor (ET) refers to involuntary oscillations of a part of the body. ET patients face serious difficulties in performing daily living activities. Our motivation is to develop a system that can enable ET patients to perform their daily living activities; hence we have been developing a myoelectric controlled exoskeletal robot for ET patients. However, the EMG signal of ET patients contains not only voluntary movement signals but also tremor signals. Accordingly, to control this robot correctly, tremor signals must be removed from the EMG signal of ET patients. To date, we have been developing a filter to remove tremor signals, which has been largely effective in this. However, tremor signals are generated both while voluntary movement is being performed and while a posture is being maintained, and the filter ended up attenuating both these signals. But, to control this robot accurately, the signal generated during performance of voluntary movement is expected not to be attenuated. Therefore, in this paper, we propose a method that attenuates only tremor signals arising during maintenance of a posture. To accomplish this objective, we focus on the frequency of tremor signals. From the experiment, we confirmed the characteristic that the frequency of tremor signals changed depending on the state of the patient's movement. We then used frequency as a switch to activate the previously proposed filter by setting a threshold. As an evaluation, signals processed by the proposed method were input to a time delay neural network. The proposed method succeeded in partly improving recognition due to reduction of attenuation during performance of voluntary movement. However, the proposed method failed recognition in cases where the frequency of tremor signals varied widely. As a future work we will review the method to calculate the frequency of tremor signals and improve recognition.