부산대학교 도서관

Stress is a mental illness that impacts facets of life and can cause crucial health problems like sleep disorders and depression. In order to stay informed about one’s mental health, it is important to analyze one’s vitals. Wearable IoT devices collect and send the physiological parameters to an edge device for further processing and monitoring stress levels. Additionally, person-specific stress monitoring systems outperform generic ones, but they have limitations because person-specific models are not very adaptive to a wide range of people. Furthermore, creating a generic stress model is difficult because of different stress handling capacities. In this paper, we have proposed an IoT and Machine learning-based stress monitoring system. The proposed approach is a hybrid model that gives a relatively accurate prediction. We have demonstrated the proposed model on the WSEAD dataset and comparative analysis has been done with state-of-the-art methods.