A Comparative Analysis of Global Optimization Algorithms for Surface Electromyographic Signal Onset Detection

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aut.relation.articlenumber100294
aut.relation.endpage100294
aut.relation.journalDecision Analytics Journal
aut.relation.startpage100294
aut.relation.volume8
dc.contributor.authorAlam, S
dc.contributor.authorZhao, X
dc.contributor.authorNiazi, IK
dc.contributor.authorAyub, MS
dc.contributor.authorKhan, MA
dc.date.accessioned2024-08-15T04:37:44Z
dc.date.available2024-08-15T04:37:44Z
dc.date.issued2023-09-01
dc.description.abstractSurface Electromyography (sEMG) is a technique for measuring muscle activity by recording electrical signals from the surface of the body. It is widely used in fields such as medical diagnosis, human–computer interaction, and sports injury rehabilitation. The detection of the onset and offset of muscle activation is a longstanding challenge in sEMG analysis. This study pioneers the implementation, configuration, and evaluation of Particle Swarm Optimization (PSO) against other optimization algorithms for sEMG signal detection, including Genetic algorithms (GA), Simulated Annealing (SA), Ant Colony Optimization (ACO), and Tabu Search (TS). The results show that the PSO algorithm achieves the highest median accuracy and F1-Score and is the fastest among the selected algorithms but has lower stability compared to Genetic algorithms and Ant colony optimization. The design and value of the cost function had a significant impact on the results, with optimal results obtained when the cost value was between 0.1203 and 0.1384. The use of these algorithms improved detection efficiency and reduced the need for manual parameter adjustment. To the best of our knowledge, no published studies have utilized Simulated Annealing, Ant colony optimization, and Tabu search meta-heuristic algorithms to detect sEMG signal onsets.
dc.identifier.citationDecision Analytics Journal, ISSN: 2772-6622 (Print); 2772-6622 (Online), Elsevier BV, 8, 100294-100294. doi: 10.1016/j.dajour.2023.100294
dc.identifier.doi10.1016/j.dajour.2023.100294
dc.identifier.issn2772-6622
dc.identifier.issn2772-6622
dc.identifier.urihttp://hdl.handle.net/10292/17894
dc.languageen
dc.publisherElsevier BV
dc.relation.urihttps://www.sciencedirect.com/science/article/pii/S2772662223001340
dc.rights© 2023 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
dc.rights.accessrightsOpenAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject4605 Data Management and Data Science
dc.subject46 Information and Computing Sciences
dc.subject4602 Artificial Intelligence
dc.titleA Comparative Analysis of Global Optimization Algorithms for Surface Electromyographic Signal Onset Detection
dc.typeJournal Article
pubs.elements-id520864
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