Filtering Essential Tremor noise on surface EMG based on squared sine wave approximation

Essential Tremor (ET) refers to involuntary movements of a part of the body. ET patients have serious difficulties in performing daily living activities. Our ultimate goal is to develop a system that can enable ET patients to perform daily living activities. We have been developing an exoskeleton robot for ET patients. We make use of the electromyogram (EMG) signal to control this robot. However, the EMG signal of ET patients contains not only signals from voluntary movements but also noise from involuntary tremors. In this paper, we focus on developing a signal processing method to suppress tremor noise present in the surface EMG signal. The proposed filter detected attenuation ratio by the correlation between the last EMG data and one period squared sine wave. The filtered EMG signals indicated that essential tremor noise of the elbow flexed posture while holding a water-filled bottle was suppressed. In addition, voluntary information was less affected by the filter. Welch's t-value test confirmed that ease of extraction of voluntary movement was increased by the proposed filter.