From Sensors to Systems: Real-Time Human Performance and Health Monitoring

Wearable and environmental sensors are increasingly used to monitor human performance and health in real time, but their practical value remains limited when signals are interpreted in isolation or through context-independent algorithms. This Perspective focuses on real-time physiological monitoring in elite sport, using exertional heat illness risk and physiological strain as a high-stakes worked example. Our work further illustrates how such monitoring could move from device-level measurement toward fit-for-purpose sensor ecosystems. We argue that such ecosystems should be defined not by multimodal data collection alone, but by five core pillars: time-synchronised sensing, context-specific interpretation, multi-level validation, uncertainty-aware modelling and embedded governance-each evaluated against a defined high-stakes decision rather than device accuracy alone. Accurate decision-making in this context may require integration of thermoregulatory, cardiovascular, biomechanical and environmental data streams. Emerging artificial intelligence approaches may support prediction and decision support, but only if models are transparent, calibrated, prospectively validated and robust to individual and contextual variability. We also examine unresolved risks, including algorithmic bias, false reassurance, athlete surveillance, proprietary opacity, data asymmetry and regulatory constraints. While the framework and evidence presented here are anchored in elite sport, we discuss where its core validation and governance principles may extend to clinical, occupational and military settings, and where the underlying evidence remains sport-specific. The proposed framework is intended to complement, rather than replace, existing digital health, athlete management and cyber-physical systems by specifying the validation and governance requirements needed for high-stakes human performance monitoring.