Machine Learning-based sEMG Signal Classification for Hand Gesture Recognition

TL;DR

This paper benchmarks EMG-based hand gesture recognition using novel feature extraction methods combined with machine learning and deep learning models, achieving high accuracy on public datasets.

Contribution

It introduces and evaluates new feature extraction techniques like fused time-domain, temporal-spatial, and wavelet features with advanced models for improved gesture classification.

Findings

1D Dilated CNN achieved 97% accuracy on Grabmyo dataset.

Random forest achieved 94.95% accuracy on FORS-EMG dataset.

Novel features improved classification performance.

Abstract

EMG-based hand gesture recognition uses electromyographic~(EMG) signals to interpret and classify hand movements by analyzing electrical activity generated by muscle contractions. It has wide applications in prosthesis control, rehabilitation training, and human-computer interaction. Using electrodes placed on the skin, the EMG sensor captures muscle signals, which are processed and filtered to reduce noise. Numerous feature extraction and machine learning algorithms have been proposed to extract and classify muscle signals to distinguish between various hand gestures. This paper aims to benchmark the performance of EMG-based hand gesture recognition using novel feature extraction methods, namely, fused time-domain descriptors, temporal-spatial descriptors, and wavelet transform-based features, combined with the state-of-the-art machine and deep learning models. Experimental investigations on the Grabmyo dataset demonstrate that the 1D Dilated CNN performed the best with an accuracy of $97\%$ using fused time-domain descriptors such as power spectral moments, sparsity, irregularity factor and waveform length ratio. Similarly, on the FORS-EMG dataset, random forest performed the best with an accuracy of $94.95\%$ using temporal-spatial descriptors (which include time domain features along with additional features such as coefficient of variation (COV), and Teager-Kaiser energy operator (TKEO)).