Proactive Stress Management Using LSTM Forecasting, Deep Reinforcement

In today's life, stress has become a big issue because of excessive working load, study pressure, and unhealthy lifestyle habits. It also impacts the physical health. Typical stress monitoring systems are largely reactive, meaning that they only detect stress once it has occurred, and thus cannot provide timely intervention. In this study, a proactive stress management model based on the wearable physiological signals and intelligent decision-making is proposed, with the aim of predicting or controlling the physiological changes caused by stress by using physiological signals as the input to the system. The proposed system combines three models: Bidirectional Long Short-Term Memory (Bi-LSTM) forecasting, Deep Reinforcement Learning (DRL), and SHAP explainability, to create a predictive and adaptive health support system.