Real-Time EMG Signal Classification via Recurrent Neural Networks

TL;DR

This paper presents a real-time EMG signal classification method using RNN architectures combined with wavelet features, achieving high accuracy and low delay for prosthetic control.

Contribution

It introduces a hybrid time-frequency feature extraction with RNNs for improved real-time EMG classification accuracy and speed.

Findings

Achieved 96% classification accuracy in 600 ms

RNN architectures outperform state-of-the-art methods

Hybrid wavelet features enhance classification performance

Abstract

Real-time classification of Electromyography signals is the most challenging part of controlling a prosthetic hand. Achieving a high classification accuracy of EMG signals in a short delay time is still challenging. Recurrent neural networks (RNNs) are artificial neural network architectures that are appropriate for sequential data such as EMG. In this paper, after extracting features from a hybrid time-frequency domain (discrete Wavelet transform), we utilize a set of recurrent neural network-based architectures to increase the classification accuracy and reduce the prediction delay time. The performances of these architectures are compared and in general outperform other state-of-the-art methods by achieving 96% classification accuracy in 600 msec.