Predictive Analysis of Stress Based on Vital Physiological Metrics

Stress comes from various sources, including personal struggles, professional expectations, and academic constraints. It is a significant aspect of a person’s life. Stress in the workplace affects many people, including corporate professionals and students, and it can have serious negative effects on one’s physical and emotional well-being. Older stress assessment methods are biased since they are frequently subjective or rely on self-reports. The physiological health metrics such as the body temperature, the heart rate, blood volume pulse, and electrodermal activity of a person are used in this research to suggest a new method of stress identification and treatment. We provide an unbiased and trustworthy evaluation of stress levels by using real-time data from wearable sensors of different students. We examine the relationship between stress and these health metrics using machine learning techniques, providing a prediction model for stress identification. The findings show that, in comparison to conventional techniques, our model can precisely and promptly detect stress events with very high and optimum performance, greatly enhancing the timeliness of stress monitoring. Future research will concentrate on providing user suggestions based on their stress level and providing stress-reduction strategies based on real-time data feedback.