WEARABLE IOT DEVICE FOR STRESS MONITORING: EXPERIMENTAL DATA AND MATHEMATICAL MODELING OF PHYSIOLOGICAL REACTIONS

This paper presents a wearable IoT device for multiparametric monitoring of physiological responses to stress and exercise. Heart rate (HR), galvanic skin reaction (GSR), skin temperature, and blood oxygen saturation (SPO₂) were experimentally recorded in the phases of rest, stress, and recovery. Mathematical models are proposed to interpret the dynamics: exponential dependencies for HR and GSR, a linear-exponential function for temperature, and an exponential recovery model for SPO₂. Comparison with experimental data showed high agreement (mean R2 > 0.85), which confirms the adequacy of simple first-order equations to describe physiological processes. The results demonstrate the potential of integrating experimental validation and mathematical modeling to improve the accuracy and reliability of wearable stress monitoring systems, as well as their applications in the field of digital health and personalized digital twins.