A Bio-Inspired WASD Neural Network with Soft-Margin Fuzzy Inference for Human Activity Recognition in Assistive Technologies and Pattern Classification
4 affiliations
- 1Department of Mathematics and Statistics, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Saudi Arabia
- 2King Salman Center for Disability Research, Riyadh 11614, Saudi Arabia
- 3Department of Mathematics and Natural Sciences, Gulf University for Science and Technology, West Mishref 32093, Kuwait
- 4Department of Economics, National and Kapodistrian University of Athens, Sofokleous 1 Street, 10559 Athens, Greece
Abstract
Human Activity Recognition (HAR) in Assistive Technologies represents a crucial application framework, focusing on motion tracking adjustments to safeguard the daily autonomy of individuals with physical disabilities. However, traditional gradient-based learning setups frequently face significant limitations, such as poor convergence speed, a strong tendency to become trapped in sub-optimal local minima, and boundary data uncertainties. This study introduces a novel bio-inspired Weight and Structure Determination (WASD) network framework combined with a soft-margin three-membership fuzzy inference controller to bypass these intrinsic optimization flaws. Particularly, by incorporating the stochastic exploratory mechanisms of the Beetle Antennae Search (BAS) metaheuristic algorithm, we are able to resolve the optimal hidden layer neuron number, the power exponents of the activation function, and the internal bias profiles simultaneously. The connections and algebraic output vectors are directly resolved in a single step using the non-iterative direct weight-determination process, which is a sub-algorithm of WASD and lowers the total computational complexity. HAR motion tracking is evaluated as the primary domain to examine the potential applicability of the proposed model to mobility-support systems, while quantitative finance asset classification is framed as an essential secondary task to assess the network’s predictive generalizability under non-stationary conditions. Numerical experiments and statistical comparisons indicate that the hybrid network model achieves high precision and delivers competitive performance or outperforms multiple benchmark machine learning classifiers, presenting in this way a robust alternative for real-world binary decision tasks.
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BibTeX
@article{alqahtani2026bioinspired,
title = {{A Bio-Inspired WASD Neural Network with Soft-Margin Fuzzy Inference for Human Activity Recognition in Assistive Technologies and Pattern Classification}},
author = {Rubayyi T. Alqahtani and Theodore E. Simos and Spyridon D. Mourtas and Vasilios N. Katsikis},
journal = {Mathematics},
year = {2026},
month = oct,
doi = {10.3390/math14193595},
url = {https://www.mdpi.com/2227-7390/14/19/3595},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - A Bio-Inspired WASD Neural Network with Soft-Margin Fuzzy Inference for Human Activity Recognition in Assistive Technologies and Pattern Classification
AU - Rubayyi T. Alqahtani
AU - Theodore E. Simos
AU - Spyridon D. Mourtas
AU - Vasilios N. Katsikis
PY - 2026
DA - 2026/10/03
JO - Mathematics
DO - 10.3390/math14193595
UR - https://www.mdpi.com/2227-7390/14/19/3595
AB - Human Activity Recognition (HAR) in Assistive Technologies represents a crucial application framework, focusing on motion tracking adjustments to safeguard the daily autonomy of individuals with physical disabilities. However, traditional gradient-based learning setups frequently face significant limitations, such as poor convergence speed, a strong tendency to become trapped in sub-optimal local minima, and boundary data uncertainties. This study introduces a novel bio-inspired Weight and Structure Determination (WASD) network framework combined with a soft-margin three-membership fuzzy inference controller to bypass these intrinsic optimization flaws. Particularly, by incorporating the stochastic exploratory mechanisms of the Beetle Antennae Search (BAS) metaheuristic algorithm, we are able to resolve the optimal hidden layer neuron number, the power exponents of the activation function, and the internal bias profiles simultaneously. The connections and algebraic output vectors are directly resolved in a single step using the non-iterative direct weight-determination process, which is a sub-algorithm of WASD and lowers the total computational complexity. HAR motion tracking is evaluated as the primary domain to examine the potential applicability of the proposed model to mobility-support systems, while quantitative finance asset classification is framed as an essential secondary task to assess the network’s predictive generalizability under non-stationary conditions. Numerical experiments and statistical comparisons indicate that the hybrid network model achieves high precision and delivers competitive performance or outperforms multiple benchmark machine learning classifiers, presenting in this way a robust alternative for real-world binary decision tasks.
SN - 2227-7390
ER -