Wearable Tech for Mental Health Monitoring Real-Time Stress Detection Using Biometric Sensors

A major advancement in real-time stress identification is represented by technologies like wearables for mental health monitoring that incorporate biometric sensors like heart rate variability (HRV), galvanic skin response (GSR), and electrodermal activity (EDA). Predictable, subjective assessments are used in traditional techniques, which all too often lead to training responses that are too late. It provides a solution to these issues by using Machine Learning (ML) techniques to identify stress behaviors while monitoring physiological signals from a sensor. Real-time feedback is made possible by the results of factors, and individualized interventions are provided through a mobile application, which raises user engagement and provides preventative healthcare. Comparing the proposed system to existing system were able to improve its precision, recall, and accuracy to 91%, 93%, and 92%, respectively. Additionally, its accuracy in recognizing low, moderate, and high levels of stress is up to 93%, 90%, and 92%, respectively, demonstrating its efficacy in determining stress levels. The results demonstrate the enhanced performance of the proposed system in terms of adequate mental health management and real-time continuous stress detection.