Hand Gesture Recognition Using Temporal Convolutions and Attention Mechanism

TL;DR

This paper introduces TC-HGR, a novel deep learning architecture using temporal convolutions and attention for hand gesture recognition from sEMG signals, achieving high accuracy with significantly fewer parameters.

Contribution

The paper presents a new temporal convolution-based model with attention mechanisms that reduces computational complexity while maintaining high gesture recognition accuracy.

Findings

Achieved over 80% classification accuracy for 17 gestures.

Reduced model parameters by nearly 12 times compared to state-of-the-art.

Effective for short window sizes of 200ms and 300ms.

Abstract

Advances in biosignal signal processing and machine learning, in particular Deep Neural Networks (DNNs), have paved the way for the development of innovative Human-Machine Interfaces for decoding the human intent and controlling artificial limbs. DNN models have shown promising results with respect to other algorithms for decoding muscle electrical activity, especially for recognition of hand gestures. Such data-driven models, however, have been challenged by their need for a large number of trainable parameters and their structural complexity. Here we propose the novel Temporal Convolutions-based Hand Gesture Recognition architecture (TC-HGR) to reduce this computational burden. With this approach, we classified 17 hand gestures via surface Electromyogram (sEMG) signals by the adoption of attention mechanisms and temporal convolutions. The proposed method led to 81.65% and 80.72% classification accuracy for window sizes of 300ms and 200ms, respectively. The number of parameters to train the proposed TC-HGR architecture is 11.9 times less than that of its state-of-the-art counterpart.