Mental Stress Detection Using Wearable Data

Abstract: Mental stress has emerged as a pervasive challenge in contemporary society, profoundly impacting physical health, emotional stability, and cognitive performance. This paper presents a robust, software-centric framework designed for the automated detection of mental stress by leveraging the rich biometric data streams generated by modern wearable technologies. By capturing and analyzing key physiological indicators—including Heart Rate (HR), Heart Rate Variability (HRV), Electrodermal Activity (EDA), and Peripheral Skin Temperature—the system creates a multidimensional profile of the user’s autonomic nervous system response. The core of the proposed solution utilizes a synergistic combination of Machine Learning (ML) and Deep Learning (DL) architectures to process high-frequency sensor data, filter signal noise, and extract discriminating features associated with physiological arousal. These models are trained to recognize complex, non-linear stress patterns that traditional diagnostic methods might overlook. Experimental results indicate that the system can distinguish between stressed and non-stressed states with high predictive accuracy, providing a reliable tool for real-time monitoring. Beyond simple detection, this approach facilitates a proactive paradigm in mental healthcare. By enabling continuous, non-invasive observation in daily life settings, the framework supports early intervention strategies and empowers individuals to manage their mental well-being more effectively. Ultimately, this research contributes to the development of scalable, intelligent health systems that bridge the gap between wearable hardware and actionable psychological insights.