Early Prediction of Physiological Strain Using Multivariate Time-Series Data

The increasing availability of wearable physiological sensors has enabled continuous monitoring of human performance in safety-critical and high-demand environments. Predicting physiological strain in advance provides valuable support for proactive decision-making and risk mitigation in sociotechnical systems. This study investigates short-term forecasting of physiological strain using multivariate wearable time-series data. The objective is to predict a bounded strain index several minutes in advance using heart rate (HR), respiratory rate (RR), and core body temperature (CBT) measurements. The dataset comprises continuous physiological recordings from 86 participants with approximately 734,000 time points, standardized to a sampling rate of 1 Hz. This work builds upon data collected in the RTVitalMonitor project, which develops predictive models for monitoring psychophysiological strain and performance in military task simulations. A multi-horizon forecasting framework predicts strain levels at 5, 10, and 15 minute intervals using sequential deep learning models. The methodological approach extends prior work conducted on a public graded exercise testing dataset, where exhaustion prediction was formulated as a binary classification task. In contrast, the present study reframes the problem as a regression-based forecasting task using a Gated Recurrent Unit (GRU) network, enabling continuous early warning of physiological strain. Models are evaluated using mean absolute error (MAE), root mean squared error (RMSE), and coefficient of determination (R2). Results demonstrate that the GRU model achieves robust predictive performance across all forecast horizons and remains stable under parameter-reduction scenarios. The findings highlight the potential of cognitive computing approaches for privacy-aware, real-time physiological monitoring in sociotechnical systems. The proposed framework contributes to the development of predictive human performance monitoring solutions suitable for wearable and edgebased deployment.