A Study on Machine Learning-Based Stress Recognition System
Keywords:
Stress monitoring, machine learning, edge computing, real-time detection, psychological healthAbstract
Stress emerges as the body's reaction to shifts in the surroundings exert influence, manifesting through a multitude of cognitive, physiological, or affective reactions. Prolonged acute stress may disrupt both physiological and psychological well-being equilibrium, resulting in decreased work efficacy and a heightened risk of chronic ailments such as hypertension and anxiety disorders. As psychological stress increasingly becomes a global issue, impacting people of all ages, there is an urgent demand for effective monitoring systems. A dependable and economical an acute stress detection system could allow individuals to track and regulate their stress levels, thus alleviating long-term negative outcomes. This article examines and discusses literature centered on machine learning-driven strategies for stress detection, highlighting their potential for real-time oversight. Furthermore, we delve into existing solutions that incorporate edge computing technologies, improving the practicality and efficacy of stress monitoring in real-world scenarios. By amalgamating current research, this review aspires to underscore the progress in machine learning methodologies for stress detection and the significance of edge computing in delivering timely and actionable insights for stress management.
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