Multi-Sensory Stress Detection System

In the contemporary workplace, chronic stress is increasingly prevalent, bearing serious consequences such as health issues, workplace-related suicides, and fatalities. Addressing this urgent challenge, we present the “Multi-Sensory Stress Detection System,” a groundbreaking solution designed to proactively identify, analyze, and mitigate stress, thereby promoting a healthy work-life balance and preventing potential harm. Central to our strategy is the utilization of the WESAD (Wearable Stress and Affect Detection) dataset, capturing critical biological markers through wrist- and chest-worn (RespiBAN) devices. A robust preprocessing pipeline ensures data accuracy by transforming non-stationary data into a stationary format. We harness data-driven insights for stress detection using advanced Deep Learning Models, rigorously validated against existing research. The proposed CNN-based model achieves an accuracy of 98.73 %, underscoring the critical importance of precise stress detection in mitigating psychological impediments in an individual's life. This innovative technology carries significant implications for trauma recovery and workplace stress management, and it seamlessly integrates with the Internet of Things (IoT) to usher in a revolution in stress management in the contemporary environment. This innovation has broad applications in the healthcare industry, particularly for patients dealing with PTSD and autism.