Personalized Stress Monitoring AI System for Healthcare Workers

ABSTRACT

In the current COVID-19 pandemic scenario, healthcare workers, in particular nurses, face prolonged exposure to stress. This intense duress takes a toll on their health overtime, affects their quality of life, and in turn impacts the quality of care provided to the patients. Hence, real-time detection and monitoring of stress is extremely important for early detection of stress patterns, prevention of burnouts and chronic conditions in healthcare workers as well as facilitate improved patient-care outcomes. In this paper, we present a proof-of-concept case study using machine learning (ML) and artificial intelligence (AI)-based stress detection model that determines a personalized assessment of stress level using heart rate, heart rate variability, and physical activity of the users. We used wearable electrocardiogram and inertial sensor to record heart activity and physical activity of nurses during their shifts. Our preliminary results indicate that the proposed stress tracking model can effectively predict any stress occurrences. This study is a pivotal attempt to emphasize the significance of stress-detection and relief for healthcare workers and provide them a tool for an effective assessment of personalized stress levels. © 2021 IEEE.