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This paper presents the development and implementation of a stress monitoring system and its associated health risks using IoT, GSM, and AI technologies. The designed system was implemented with IoT-enabled Particle Photon microcontrollers that read and processed data from biosensors and input switches to evaluate physiological factors including body thermal heat rate, systemic arterial pressure, heartbeat rate, electrical skin resistance, oxygen saturation level, pain or aches and breathing rate, as well as emotional and behavioral stress symptoms such as restlessness, being irritable or over-reactive, being upset or agitated, nervousness, and impatience. Real-time and distant access to the measured parameters were made feasible through IoT cloud platforms and customized smartphone apps. The system displayed the user's stress score and level (normal, mild, moderate, severe, and extremely severe), health risk level (zero, low, medium, or high), and stress and health management recommendations. When the resulting stress level was extremely high, the system generated a referral message and sent it by SMS to the guidance counselor for necessary mental health care support. As per the research findings, the IoT-based stress level detection prototype achieved an accuracy of 87.5% when compared to the data collected from the AI interactive stress evaluation mobile phone application. The developed application was based on the DASS-21 test and was administered to participants who volunteered from an institution in Oman. Furthermore, when compared to the NEWS2 early warning score system, the system's health risk level results had an accuracy of 93.75%. With the help of this research project, guidance counselors, psychologists, and medical professionals will be able to assess and address some of the mental and physical health concerns that university students and faculty members have, as well as raise mental health awareness among individuals.
Nagayo et al. (Fri,) studied this question.