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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: John Deutero, Kisoi, Norfadzlan, Yusup, Syahrul Nizam, Junaini
Format: Article
Language:English
Published: SCIENCE & INFORMATION SAI ORGANIZATION LTD 2025
Subjects:
QA75 Electronic computers. Computer science
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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spelling my.unimas.ir-477152025-03-07T06:39:20Z http://ir.unimas.my/id/eprint/47715/ A Systematic Review of Metaheuristic Algorithms in Human Activity Recognition : Applications, Trends, and Challenges John Deutero, Kisoi Norfadzlan, Yusup Syahrul Nizam, Junaini QA75 Electronic computers. Computer science 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. SCIENCE & INFORMATION SAI ORGANIZATION LTD 2025-02-28 Article PeerReviewed text en http://ir.unimas.my/id/eprint/47715/1/Paper_74-A_Systematic_Review_of_Metaheuristic_Algorithms.pdf John Deutero, Kisoi and Norfadzlan, Yusup and Syahrul Nizam, Junaini (2025) A Systematic Review of Metaheuristic Algorithms in Human Activity Recognition : Applications, Trends, and Challenges. International Journal of Advanced Computer Science and Applications (IJACSA), 16 (2). pp. 731-742. ISSN 2156-5570 https://thesai.org/Publications/ViewPaper?Volume=16&Issue=2&Code=IJACSA&SerialNo=74 DOI: 10.14569/IJACSA.2025.0160274
institution Universiti Malaysia Sarawak
building Centre for Academic Information Services (CAIS)
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sarawak
content_source UNIMAS Institutional Repository
url_provider http://ir.unimas.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
John Deutero, Kisoi
Norfadzlan, Yusup
Syahrul Nizam, Junaini
A Systematic Review of Metaheuristic Algorithms in Human Activity Recognition : Applications, Trends, and Challenges
description 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.
format Article
author John Deutero, Kisoi
Norfadzlan, Yusup
Syahrul Nizam, Junaini
author_facet John Deutero, Kisoi
Norfadzlan, Yusup
Syahrul Nizam, Junaini
author_sort John Deutero, Kisoi
title A Systematic Review of Metaheuristic Algorithms in Human Activity Recognition : Applications, Trends, and Challenges
title_short A Systematic Review of Metaheuristic Algorithms in Human Activity Recognition : Applications, Trends, and Challenges
title_full A Systematic Review of Metaheuristic Algorithms in Human Activity Recognition : Applications, Trends, and Challenges
title_fullStr A Systematic Review of Metaheuristic Algorithms in Human Activity Recognition : Applications, Trends, and Challenges
title_full_unstemmed A Systematic Review of Metaheuristic Algorithms in Human Activity Recognition : Applications, Trends, and Challenges
title_sort systematic review of metaheuristic algorithms in human activity recognition : applications, trends, and challenges
publisher SCIENCE & INFORMATION SAI ORGANIZATION LTD
publishDate 2025
url 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
_version_ 1826079221112373248
score 13.244413

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