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

Area of Science:

• Physiological monitoring
• Sensor ecosystems
• Human performance optimization

Background:

• Wearable and environmental sensors offer real-time human performance and health data.
• Current limitations exist in interpreting isolated signals or using context-independent algorithms.
• Elite sport monitoring requires moving beyond device-level measurements to integrated sensor ecosystems.

Purpose of the Study:

• To propose a framework for fit-for-purpose sensor ecosystems in real-time physiological monitoring.
• To use exertional heat illness risk and physiological strain in elite sport as a high-stakes example.
• To outline five core pillars for effective sensor ecosystems: time-synchronised sensing, context-specific interpretation, multi-level validation, uncertainty-aware modelling, and embedded governance.

Main Methods:

• Focus on a high-stakes worked example: exertional heat illness risk and physiological strain in elite sport.
• Integration of thermoregulatory, cardiovascular, biomechanical, and environmental data streams.
• Evaluation of sensor ecosystems against high-stakes decisions, not just device accuracy.
• Examination of artificial intelligence (AI) approaches for prediction and decision support.

Main Results:

• Sensor ecosystems should be defined by five core pillars for effective high-stakes decision-making.
• Accurate decisions necessitate integrating multiple physiological and environmental data streams.
• AI can support decision-making if models are transparent, validated, and robust to variability.
• Unresolved risks include algorithmic bias, false reassurance, and data governance issues.

Conclusions:

• A framework for sensor ecosystems, emphasizing validation and governance, is proposed for high-stakes human performance monitoring.
• The principles extend beyond elite sport to clinical, occupational, and military settings.
• This framework complements existing digital health and management systems by specifying validation and governance needs.