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Enhancing Wearable-Based Human Activity Recognition with Binary Nature-Inspired Optimization Algorithms for Feature Selection

This research paper explores the performance of binary nature-inspired optimization algorithms as feature selection to enhance the identification of human activities using wearable technology. Utilization of nature-inspired algorithms for feature selection, as documented in scholarly literature, pr...

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Main Authors: Norfadzlan, Yusup, Izzatul Nabila, Sarbini, Dayang Nurfatimah, Awang Iskandar, Azlan, Mohd Zain, Didik Dwi, Prasetya
Format: Article
Language:English
Published: Semarak Ilmu Publishing 2026
Subjects:
QA Mathematics
QA75 Electronic computers. Computer science
Online Access:http://ir.unimas.my/id/eprint/47674/1/ARASETV56_N1_PP1_12.pdf
http://ir.unimas.my/id/eprint/47674/
https://semarakilmu.com.my/journals/index.php/applied_sciences_eng_tech/article/view/6092
https://doi.org/10.37934/araset.56.1.112
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spelling my.unimas.ir-476742025-02-28T07:04:30Z http://ir.unimas.my/id/eprint/47674/ Enhancing Wearable-Based Human Activity Recognition with Binary Nature-Inspired Optimization Algorithms for Feature Selection Norfadzlan, Yusup Izzatul Nabila, Sarbini Dayang Nurfatimah, Awang Iskandar Azlan, Mohd Zain Didik Dwi, Prasetya QA Mathematics QA75 Electronic computers. Computer science This research paper explores the performance of binary nature-inspired optimization algorithms as feature selection to enhance the identification of human activities using wearable technology. Utilization of nature-inspired algorithms for feature selection, as documented in scholarly literature, presents a promising opportunity to enhance machine learning and data analysis tasks, given their effectiveness in identifying relevant features, resulting in models with reduced computational complexity, improved predictive accuracy and easier interpretation. In the experiment, we conducted an evaluation of the effectiveness and efficiency of four nature-inspired binary algorithms for optimization namely Binary Particle Swarm Optimization (BPSO), Binary Grey Wolf Optimization algorithm (BGWO), Binary Differential Evolution algorithm (BDE), and Binary Salp Swarm algorithm (BSS) - in the context of human activity recognition (HAR). The outcomes of this comprehensive experimentation, conducted on two distinct human activity recognition (HAR) datasets, provide valuable insights. BPSO algorithm emerges as an adaptable and well-rounded performer, achieving a competitive balance between feature selection quality and computational efficiency in SBHAR dataset. Conversely, for the PAMAP2 dataset, BDE algorithm displays superior feature selection quality and BPSO algorithm maintains competitive performance and adaptability. In both datasets, the nature-inspired optimization algorithms have achieved remarkable feature reduction, demonstrating reductions of 48% and 50% respectively. The experiment results show how these algorithms could be used to improve methods for recognizing human activities using wearables technology, such as feature selection, parameter adjustment, and model optimization. Semarak Ilmu Publishing 2026-02 Article PeerReviewed text en http://ir.unimas.my/id/eprint/47674/1/ARASETV56_N1_PP1_12.pdf Norfadzlan, Yusup and Izzatul Nabila, Sarbini and Dayang Nurfatimah, Awang Iskandar and Azlan, Mohd Zain and Didik Dwi, Prasetya (2026) Enhancing Wearable-Based Human Activity Recognition with Binary Nature-Inspired Optimization Algorithms for Feature Selection. Journal of Advanced Research in Applied Sciences and Engineering Technology, 56 (1). pp. 1-12. ISSN 2462-1943 https://semarakilmu.com.my/journals/index.php/applied_sciences_eng_tech/article/view/6092 https://doi.org/10.37934/araset.56.1.112
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 QA Mathematics
QA75 Electronic computers. Computer science
spellingShingle QA Mathematics
QA75 Electronic computers. Computer science
Norfadzlan, Yusup
Izzatul Nabila, Sarbini
Dayang Nurfatimah, Awang Iskandar
Azlan, Mohd Zain
Didik Dwi, Prasetya
Enhancing Wearable-Based Human Activity Recognition with Binary Nature-Inspired Optimization Algorithms for Feature Selection
description This research paper explores the performance of binary nature-inspired optimization algorithms as feature selection to enhance the identification of human activities using wearable technology. Utilization of nature-inspired algorithms for feature selection, as documented in scholarly literature, presents a promising opportunity to enhance machine learning and data analysis tasks, given their effectiveness in identifying relevant features, resulting in models with reduced computational complexity, improved predictive accuracy and easier interpretation. In the experiment, we conducted an evaluation of the effectiveness and efficiency of four nature-inspired binary algorithms for optimization namely Binary Particle Swarm Optimization (BPSO), Binary Grey Wolf Optimization algorithm (BGWO), Binary Differential Evolution algorithm (BDE), and Binary Salp Swarm algorithm (BSS) - in the context of human activity recognition (HAR). The outcomes of this comprehensive experimentation, conducted on two distinct human activity recognition (HAR) datasets, provide valuable insights. BPSO algorithm emerges as an adaptable and well-rounded performer, achieving a competitive balance between feature selection quality and computational efficiency in SBHAR dataset. Conversely, for the PAMAP2 dataset, BDE algorithm displays superior feature selection quality and BPSO algorithm maintains competitive performance and adaptability. In both datasets, the nature-inspired optimization algorithms have achieved remarkable feature reduction, demonstrating reductions of 48% and 50% respectively. The experiment results show how these algorithms could be used to improve methods for recognizing human activities using wearables technology, such as feature selection, parameter adjustment, and model optimization.
format Article
author Norfadzlan, Yusup
Izzatul Nabila, Sarbini
Dayang Nurfatimah, Awang Iskandar
Azlan, Mohd Zain
Didik Dwi, Prasetya
author_facet Norfadzlan, Yusup
Izzatul Nabila, Sarbini
Dayang Nurfatimah, Awang Iskandar
Azlan, Mohd Zain
Didik Dwi, Prasetya
author_sort Norfadzlan, Yusup
title Enhancing Wearable-Based Human Activity Recognition with Binary Nature-Inspired Optimization Algorithms for Feature Selection
title_short Enhancing Wearable-Based Human Activity Recognition with Binary Nature-Inspired Optimization Algorithms for Feature Selection
title_full Enhancing Wearable-Based Human Activity Recognition with Binary Nature-Inspired Optimization Algorithms for Feature Selection
title_fullStr Enhancing Wearable-Based Human Activity Recognition with Binary Nature-Inspired Optimization Algorithms for Feature Selection
title_full_unstemmed Enhancing Wearable-Based Human Activity Recognition with Binary Nature-Inspired Optimization Algorithms for Feature Selection
title_sort enhancing wearable-based human activity recognition with binary nature-inspired optimization algorithms for feature selection
publisher Semarak Ilmu Publishing
publishDate 2026
url http://ir.unimas.my/id/eprint/47674/1/ARASETV56_N1_PP1_12.pdf
http://ir.unimas.my/id/eprint/47674/
https://semarakilmu.com.my/journals/index.php/applied_sciences_eng_tech/article/view/6092
https://doi.org/10.37934/araset.56.1.112
_version_ 1825817114314801152
score 13.244413

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