A Systematic Review of Metaheuristic Algorithms in Human Activity Recognition : Applications, Trends, and Challenges
Metaheuristic algorithms have emerged as promising techniques for optimizing human activity recognition (HAR) systems. This systematic review examines the application of these algorithms in HAR by analyzing relevant literature published between 2019 and 2024. A comprehensive search across multiple d...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
SCIENCE & INFORMATION SAI ORGANIZATION LTD
2025
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/47715/1/Paper_74-A_Systematic_Review_of_Metaheuristic_Algorithms.pdf http://ir.unimas.my/id/eprint/47715/ https://thesai.org/Publications/ViewPaper?Volume=16&Issue=2&Code=IJACSA&SerialNo=74 |
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| Summary: | Metaheuristic algorithms have emerged as promising techniques for optimizing human activity recognition (HAR) systems. This systematic review examines the application of these algorithms in HAR by analyzing relevant literature published between 2019 and 2024. A comprehensive search across multiple databases yielded 27 studies that met the inclusion criteria. The analysis revealed that Genetic Algorithms (GA) exhibit classification accuracy rates ranging from 88.25% to 96.00% in activity recognition and up to 90.63% in localization tasks. Notably, Oppositional and Chaos Particle Swarm Optimization (OCPSO) combined with MI-1DCNN significantly improves detection accuracy, demonstrating a 2.82% improvement over standard PSO with Support Vector Machine (SVM) as classifier approaches. Our analysis highlights a growing trend toward hybrid metaheuristic approaches that enhance feature selection and classifier optimization. However, challenges related to computational cost and scalability persist, underscoring key areas for future research. These findings emphasize the potential of metaheuristic algorithms to significantly advance HAR. Future studies should explore the development of more computationally efficient hybrid models and the integration of metaheuristic optimization with deep learning architectures to enhance system robustness and adaptability. |
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