Proactive Stress Management Using LSTM Forecasting, Deep Reinforcement (Paperback)

Abhijit Kumar Jha

ISBN 10: 6630078861 / ISBN 13: 9786630078862

Published by LAP Lambert Academic Publishing, 2026

About this Item

Paperback. 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Seller Inventory # 9786630078862

Bibliographic Details

• Title: Proactive Stress Management Using LSTM Forecasting, Deep Reinforcement (Paperback)
• Publisher: LAP Lambert Academic Publishing
• Publication Date: 2026
• Language: English
• ISBN 10: 6630078861
• ISBN 13: 9786630078862
• Binding: Paperback
• Condition: new

About this title

Synopsis

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.