Robust Feature Learning for Stress and Fatigue Recognition from Noisy Wearable Physiological Signals

Abstract

Wearable physiological sensing has become a central component of modern health monitoring, offering continuous access to signals that are relevant for stress and fatigue recognition. However, the practical utility of these systems is constrained by motion artifacts, sensor drift, environmental interference, and variability in skin contact, all of which introduce complex noise patterns that can undermine learned representations. This paper examines robust feature learning for stress and fatigue recognition from noisy wearable physiological signals from a system-level perspective. Rather than focusing narrowly on algorithmic performance, the discussion integrates architectural decisions, data governance, deployment constraints, sustainability, fairness, and policy implications. The paper analyzes the structural trade-offs associated with shallow and deep feature learners, temporal modeling, transfer learning, and domain adaptation under realistic sensing conditions. It further considers how data provenance, annotation uncertainty, demographic representation, and regulatory expectations shape the design of robust recognition infrastructures. The paper argues that robustness cannot be achieved solely through model refinement, but requires coordinated attention to sensing hardware, signal preprocessing, feature representation, validation protocols, and organizational oversight. A cross-domain perspective is adopted, drawing on experiences in clinical monitoring, human factors engineering, and large-scale artificial intelligence systems. The conclusion emphasizes that sustainable stress and fatigue recognition systems must be evaluated not only by aggregate accuracy, but also by their behavior under distributional shift, their resource demands, their interpretability, and their alignment with social and ethical standards.