Underwater sEMG-based Recognition of Hand Gestures using Tensor Decomposition

Abstract

Amputees have limited ability to complete specific movements because of the loss of hands. Prosthetic hands can help amputees as an effective human-computer interaction system in their daily lives, and some amputees need to use the prosthetic hands for underwater operations. Therefore, it is necessary to solve the problem of using prosthetic hands underwater. There are two main problems in underwater surface Electromyogram (sEMG) signal recognition. The underwater sEMG signals are disturbed by noise, and the traditional sEMG features are easily affected by noise, decreasing the recognition accuracy of underwater sEMG signals. It is difficult for subjects to acquire quantity training data underwater, and satisfactory sEMG recognition accuracy needs to be obtained based on small datasets. Tensor decomposition has the advantage of finding potential features of signals, and it is widely used in many fields. Tucker tensor decomposition was used for feature extraction and recognition of underwater sEMG signals. Seven subjects were selected to complete four hand gestures underwater and two-channel sEMG signals were collected. Wavelet transform was applied to generate a three-dimensional tensor and extracted signal features by tensor decomposition. The recognition accuracy based on K-Nearest Neighbor reaches 96.43%. The results show that the proposed sEMG feature extraction method based on tensor decomposition helps improve the recognition accuracy of underwater sEMG signals, which provides a basis for applying prosthetic hands in a water environment.