Scalable Fuzzy Neural Networks (SFNN) for Multi-Output Human Activity Recognition
Zahra Ghorrati, Mohammad Mehdi Ebadzadeh, Ahmad Esmaeili, John A. Springer, Eric T. Matson · 2025
Human Activity Recognition (HAR) involves complex, multi-output datasets that demand models balancing efficiency, scalability, and interpretability. This paper proposes a Scalable Fuzzy Neural Network (SFNN), an adaptive hierarchical deep architecture with a multi-section learning mechanism. SFNN processes smaller input windows with effective dimensionality reduction, achieving competitive performance without the heavy computational cost of traditional deep models. Leveraging the transparency of fuzzy systems, it offers a more interpretable alternative to black-box approaches. The model’s theoretical convergence guarantees and strong results on the Opportunity dataset confirm its effectiveness for diverse HAR tasks.