A physics-informed digital twin framework for personalized stress prediction using wearable sensors, machine learning, and Bayesian data assimilation

Stress-related disorders constitute one of the leading causes of reduced productivity, burnout, anxiety, depression, and diminished quality of life. Although wearable devices and smartphones continuously collect rich physiological and behavioral information, current prediction systems are predominantly data-driven, lack mechanistic interpretability, and rarely provide personalized forecasts that evolve with an individual's changing physiological state. Recent digital twin research in mental healthcare has demonstrated the potential of continuously updated virtual patient models constructed from multimodal sensing data, yet mathematical formulations capable of integrating physiological dynamics, machine learning, Bayesian inference, and uncertainty quantification remain largely unexplored. This paper develops a novel personalized Digital Twin framework for stress prediction by coupling nonlinear physiological differential equations with machine learning, Bayesian parameter estimation, Kalman filtering, and uncertainty quantification. The digital twin continuously assimilates multimodal information including heart rate variability, sleep quality, physical activity, smartphone usage patterns, cortisol concentration, and psychological questionnaire scores to estimate hidden physiological states and predict future mental health trajectories. The proposed framework combines physics-based modeling with data-driven learning to forecast impending stress episodes, anxiety attacks, burnout, and depressive relapse while simultaneously identifying patient-specific model parameters. Bayesian updating and sequential filtering enable real-time adaptation of the digital twin as new sensor observations become available, thereby improving predictive accuracy and robustness under measurement uncertainty. The resulting mathematical framework establishes a foundation for next-generation personalized mental healthcare through interpretable, continuously evolving digital twins.