Machine Learning-Based Fatigue Assessment Using Continuous Physiological and Motion Signals
Keywords:
fatigue assessment; machine learning; wearable sensing; physiological signals; system architecture; fairness; real-time monitoringAbstract
Fatigue is a multidimensional state with physiological, cognitive, and behavioral components that can impair human performance and increase the likelihood of errors in safety-critical environments. Continuous wearable sensing now enables the collection of physiological and motion signals outside controlled laboratory settings, creating new opportunities for machine learning-based fatigue assessment. However, translating raw sensor streams into reliable, fair, and actionable fatigue predictions requires more than algorithmic innovation. This paper presents a systems-level analysis of fatigue assessment infrastructure, integrating perspectives from sensing, machine learning, deployment engineering, governance, and policy. The discussion identifies structural trade-offs in edge, cloud, and federated architectures, examines data processing and signal quality challenges, and compares feature-based and representation learning approaches. It further considers robustness under real-world variability, fairness across population groups, and accountability mechanisms for high-stakes applications. The paper argues that fatigue assessment systems should be designed as governed socio-technical infrastructures rather than isolated classification tools. Long-term sustainability depends on data provenance, drift monitoring, human oversight, and alignment with regulatory and labor protections. The analysis offers a conceptual framework for researchers and system designers seeking to build fatigue assessment systems that are not only accurate but also trustworthy, interpretable, and institutionally responsible.
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