AI-Based Stress and Fatigue Recognition Using Multimodal Wearable Physiological Signals
Keywords:
wearable physiological sensing; stress recognition; fatigue detection; artificial intelligence; multimodal fusion; data governance; fairness; deployment sustainabilityAbstract
Stress and fatigue recognition from wearable physiological signals has emerged as a significant interdisciplinary challenge involving sensing hardware, machine learning, human factors, data governance, and healthcare policy. This paper presents a system-level examination of AI-based recognition architectures that integrate multimodal physiological data such as electrodermal activity, heart rate variability, skin temperature, and motion context. The discussion emphasizes structural trade-offs in wearable sensing infrastructure, including edge processing, cloud offloading, sensor fusion, and longitudinal deployment. Rather than focusing on a single algorithm or dataset, the paper analyzes how data representation, model architecture, privacy preservation, and fairness interact to determine system performance and societal acceptability. Architectural choices such as centralized training, federated learning, attention-based fusion, and context-aware inference are discussed in terms of robustness, generalization, energy consumption, and regulatory alignment. The analysis further explores the challenges of ambulatory sensing, motion artifacts, individual variability, and demographic bias. Governance and policy implications are examined in relation to worker monitoring, mental health applications, and continuous health surveillance. The paper concludes by outlining future directions for sustainable, fair, and explainable stress and fatigue recognition systems that can operate responsibly beyond controlled laboratory settings.
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