AI-Driven Stress Monitoring for Older Adults: A Wearable IOT Solution

Stress is a state of increased physical and psychological tension that can significantly affect an individual’s health and well-being. Various physiological, psychological, environmental, and emotional factors contribute to stress, and poor management can lead to serious health consequences. If not addressed well on time, stress may lead to different neurological disorders which can be detrimental to human health. This paper reviews existing research on stress detection and reduction, examining different methodologies and technologies in the field. Despite advances in stress monitoring solutions, most studies focus on younger populations, workplace settings, or general healthcare, with limited attention to elderly individuals in residential care. To address this gap, this paper proposes an IoT (Internet of Things)-enabled wearable wristband designed for the unique needs of elderly residents in care facilities. The device integrates multiple physiological sensors, including Galvanic Skin Response (GSR), skin temperature, Heart Rate Variability (HRV), accelerometer, and gyroscopic sensors, for real-time stress detection using an adaptive fuzzy logic algorithm. Unlike conventional methods, this system offers personalized interventions such as guided relaxation, breathing exercises, music therapy, and light physical activities, tailored to the user’s real-time physiological state. The user-centric design prioritizes comfort, ease of use, and effective stress management for elderly users. By bridging the gap between existing stress management technologies and the specific needs of elderly individuals, this approach aims to enhance mental well-being and improve quality of life. Future work will focus on further developing the proposed system, including rigorous testing and evaluating its effectiveness in real-world scenarios to ensure reliability, adaptability, and optimal stress management outcomes.