Surface-EMG based wrist kinematics estimation using convolutional neural network

In the past decades, classical machine learning (ML) methods have been widely investigated in wrist kinematics estimation for the control of prosthetic hands. Currently deeper structures have shown great potential to further improve prediction accuracy. In this paper we present a single stream convolutional neural network (CNN) for mapping surface electromyography (sEMG) to wrist angles within three degrees-of-freedom (DOFs). Two types of two dimensional (2D) sEMG images are constructed in time domain and spectrum as CNN inputs, respectively. Six typical linear and nonlinear ML models are implemented for comparison, where four efficient time-spatial hand-crafted features are extracted to represent feature engineering. Experiment results with four able-bodied participants illustrate that CNN with 2D spectrum sEMG images can achieve highest accuracy in most testing sessions. In other sessions, it is still competitive to the most promising ML techniques. The core strength of deep learning (DL), i.e. feature learning via deep structures and efficient algorithms, is verified to be more powerful than classical feature engineering, particularly in smaller datasets.