A comparative analysis of global optimization algorithms for surface electromyographic signal onset detection

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dc.citation.volume8
dc.contributor.authorAlam S
dc.contributor.authorZhao X
dc.contributor.authorNiazi IK
dc.contributor.authorAyub MS
dc.contributor.authorKhan MA
dc.date.accessioned2024-06-19T01:04:45Z
dc.date.available2024-06-19T01:04:45Z
dc.date.issued2023-09
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.description.confidentialfalse
dc.edition.editionSeptember 2023
dc.identifier.citationAlam S, Zhao X, Niazi IK, Ayub MS, Khan MA. (2023). A comparative analysis of global optimization algorithms for surface electromyographic signal onset detection. Decision Analytics Journal. 8.
dc.identifier.doi10.1016/j.dajour.2023.100294
dc.identifier.eissn2772-6622
dc.identifier.elements-typejournal-article
dc.identifier.number100294
dc.identifier.piiS2772662223001340
dc.identifier.urihttps://mro.massey.ac.nz/handle/10179/69904
dc.languageEnglish
dc.publisherElsevier Inc
dc.publisher.urihttps://www.sciencedirect.com/science/article/pii/S2772662223001340
dc.relation.isPartOfDecision Analytics Journal
dc.rights(c) 2023 The Author/s
dc.rightsCC BY-NC-ND 4.0
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectSurface electromyography
dc.subjectOnset detection
dc.subjectMetaheuristic optimization algorithm
dc.subjectGenetic algorithm
dc.subjectAnt colony optimization
dc.subjectParticle swarm optimization
dc.subjectTabu search
dc.subjectSimulated annealing
dc.titleA comparative analysis of global optimization algorithms for surface electromyographic signal onset detection
dc.typeJournal article
pubs.elements-id479866
pubs.organisational-groupOther
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