Myoelectric Control With Fixed Convolution-Based Time-Domain Feature Extraction: Exploring the Spatio-Temporal Interaction

• Published in: IEEE transactions on human-machine systems Vol. 52; no. 6; pp. 1247 - 1257
• Main Authors: Khushaba, Rami N., Al-Timemy, Ali H., Samuel, Oluwarotimi Williams, Scheme, Erik J.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Computing costs; Convolution; Convolutional neural networks; Convolutional neural networks (CNN); Data models; Deep learning; Electromyography; Feature extraction; Machine learning; Matrix representation; Myoelectric control; myoelectric signals; Myoelectricity; Pattern recognition; spatio-temporal models; Spatiotemporal phenomena; Time domain analysis; Training
• DOI: 10.1109/THMS.2022.3146053
• ISSN: 2168-2291

The role of feature extraction in electromyogram (EMG) based pattern recognition has recently been emphasized with several publications promoting deep learning (DL) solutions that outperform traditional methods. It has been shown that the ability of DL models to extract temporal, spatial, and spatio-temporal information provides significant enhancements to the performance and generalizability of myoelectric control. Despite these advancements, it can be argued that DL models are computationally very expensive, requiring long training times, increased training data, and high computational resources, yielding solutions that may not yet be feasible for clinical translation given the available technology. The aim of this paper is, therefore, to leverage the benefits of spatio-temporal DL concepts into a computationally feasible and accurate traditional feature extraction method. Specifically, the proposed novel method extracts a set of well-known time-domain features into a matrix representation, convolves them with predetermined fixed filters, and temporally evolves the resulting features over a short and long-term basis to extract the EMG temporal dynamics. The proposed method, based on Fixed Spatio-Temporal Convolutions, offers significant reductions in the computational costs, while demonstrating a solution that can compete with, and even outperform, recent DL models. Experimental tests were performed on sparse-and high-density EMG (HD-EMG) signals databases, across a total of 44 subjects performing a maximum of 53 movements. Despite the simplification compared to deep approaches, our results show that the proposed solution significantly reduces the classification error rates by 3% to 10% in comparison to recent DL models, while being efficient for real-time implementations.