A comparative analysis of global optimization algorithms for surface electromyographic signal onset detection
Loading...
Date
2023-09
Open Access Location
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier Inc
Rights
(c) 2023 The Author/s
CC BY-NC-ND 4.0
CC BY-NC-ND 4.0
Abstract
Surface 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.
Description
Keywords
Surface electromyography, Onset detection, Metaheuristic optimization algorithm, Genetic algorithm, Ant colony optimization, Particle swarm optimization, Tabu search, Simulated annealing
Citation
Alam 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.