Compact Deep Neural Networks for Computationally Efficient Gesture Classification From Electromyography Signals

TL;DR

This paper introduces a small, efficient deep neural network for classifying gestures from electromyography signals, outperforming traditional SVMs and larger neural networks in accuracy and suitability for embedded systems.

Contribution

A novel compact deep neural network architecture is proposed, significantly reducing parameters while maintaining high classification accuracy for electromyography-based gesture recognition.

Findings

The compact deep net achieved 84.2% accuracy, outperforming SVMs.

The network has only 5,889 parameters, much fewer than existing models.

It demonstrated superior performance across different electrode sets and subjects.

Abstract

Machine learning classifiers using surface electromyography are important for human-machine interfacing and device control. Conventional classifiers such as support vector machines (SVMs) use manually extracted features based on e.g. wavelets. These features tend to be fixed and non-person specific, which is a key limitation due to high person-to-person variability of myography signals. Deep neural networks, by contrast, can automatically extract person specific features - an important advantage. However, deep neural networks typically have the drawback of large numbers of parameters, requiring large training data sets and powerful hardware not suited to embedded systems. This paper solves these problems by introducing a compact deep neural network architecture that is much smaller than existing counterparts. The performance of the compact deep net is benchmarked against an SVM and compared to other contemporary architectures across 10 human subjects, comparing Myo and Delsys Trigno electrode sets. The accuracy of the compact deep net was found to be 84.2 +/- 6% versus 70.5 +/- 7% for the SVM on the Myo, and 80.3+/- 7% versus 67.8 +/- 9% for the Delsys system, demonstrating the superior effectiveness of the proposed compact network, which had just 5,889 parameters - orders of magnitude less than some contemporary alternatives in this domain while maintaining better performance.