A Spatio-Temporal Graph Convolutional Network for Gesture Recognition from High-Density Electromyography

TL;DR

This paper introduces STGCN-GR, a spatio-temporal graph convolutional network for HD-sEMG gesture recognition, achieving 91.07% accuracy and surpassing existing methods.

Contribution

The study presents a novel spatio-temporal graph convolutional approach that effectively models spatial and temporal dependencies in HD-sEMG data for gesture recognition.

Findings

Achieved 91.07% gesture recognition accuracy.

Outperformed state-of-the-art deep learning methods on the same dataset.

Effectively modeled spatial topology and temporal dependencies in HD-sEMG data.

Abstract

Accurate hand gesture prediction is crucial for effective upper-limb prosthetic limbs control. As the high flexibility and multiple degrees of freedom exhibited by human hands, there has been a growing interest in integrating deep networks with high-density surface electromyography (HD-sEMG) grids to enhance gesture recognition capabilities. However, many existing methods fall short in fully exploit the specific spatial topology and temporal dependencies present in HD-sEMG data. Additionally, these studies are often limited number of gestures and lack generality. Hence, this study introduces a novel gesture recognition method, named STGCN-GR, which leverages spatio-temporal graph convolution networks for HD-sEMG-based human-machine interfaces. Firstly, we construct muscle networks based on functional connectivity between channels, creating a graph representation of HD-sEMG recordings. Subsequently, a temporal convolution module is applied to capture the temporal dependences in the HD-sEMG series and a spatial graph convolution module is employed to effectively learn the intrinsic spatial topology information among distinct HD-sEMG channels. We evaluate our proposed model on a public HD-sEMG dataset comprising a substantial number of gestures (i.e., 65). Our results demonstrate the remarkable capability of the STGCN-GR method, achieving an impressive accuracy of 91.07% in predicting gestures, which surpasses state-of-the-art deep learning methods applied to the same dataset.