Embracing Uncertainty in Human Activity Recognition: A Fuzzy Logic Framework for Interpretable and Context-aware Reasoning

Abstract

Human Activity Recognition (HAR) in real-world environments is inherently uncertain — shaped by ambiguous sensor signals, behavioral variability, and contextual dynamics that challenge traditional machine learning approaches. Despite growing interest in robustness and explainability, most current systems still treat uncertainty as a nuisance to be minimized rather than a structural feature to be modeled. This paper proposes a conceptual shift: positioning fuzzy logic as the core paradigm for designing HAR systems that are interpretable, adaptive, and uncertainty-aware. We present a theoretical framework in which fuzzy reasoning is integrated throughout the HAR pipeline— from sensor abstraction and context modeling to soft, interpretable activity inference and natural language explanations. By framing uncertainty as a representational and inferential asset, rather than a limitation, our approach enables systems that align more closely with the complexity of human behavior and the demands of human-centered AI. The framework is modular, extensible, and designed for transparency— making it suitable for long-term deployment in smart environments, particularly in domains like elderly care, remote monitoring, and assistive technologies. This work contributes a structured foundation for building next-generation HAR systems that move beyond black-box classification, supporting ethical, explainable, and context-sensitive activity recognition.