Myoelectric control with fixed convolution-based time-domain feature extraction: exploring the spatio–temporal interaction

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dc.contributor.authorKhushaba, Rami N.
dc.contributor.authorAl-Timemy, Ali H.
dc.contributor.authorSamuel, Oluwarotimi Williams
dc.contributor.authorScheme, Erik J.
dc.date.accessioned2023-12-21T19:17:19Z
dc.date.available2023-12-21T19:17:19Z
dc.date.issued2022-02-24
dc.description.abstractThe 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.
dc.description.copyright© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.identifier.urihttps://unbscholar.lib.unb.ca/handle/1882/37615
dc.language.isoen
dc.publisherIEEE
dc.relationNational Natural Science Foundation of China
dc.relationShenzhen Governmental Basic Research
dc.relation.hasversionhttps://doi.org/10.1109/THMS.2022.3146053
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.subject.disciplineElectrical and Computer Engineering
dc.titleMyoelectric control with fixed convolution-based time-domain feature extraction: exploring the spatio–temporal interaction
dc.typejournal article
oaire.citation.endPage1257
oaire.citation.issue6
oaire.citation.startPage1247
oaire.citation.titleIEEE Transactions on Human-Machine Systems
oaire.citation.volume52
oaire.license.conditionother
oaire.versionhttp://purl.org/coar/version/c_ab4af688f83e57aa

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