Stress-Lysis: A DNN-Integrated Edge Device for Stress Level Detection in the IoMT

Psychological stress affects physiological parameters of a person. Prolonged exposure to stress can have detrimental effects which might require expensive treatments. Acute levels of stress in people who are already diagnosed with borderline personality disorder or schizophrenia, can cost them their lives. To self-manage this important health problem in the framework of smart healthcare, a deep learning based novel system (Stress-Lysis) is proposed in this article. The learning system is trained such that it monitors stress levels in a person through human body temperature, rate of motion and sweat during physical activity. The proposed Stress-Lysis has been trained with a total of 26,000 samples per dataset and demonstrates accuracy as high as 99.7%. The collected data are transmitted and stored in the cloud which can help in real time monitoring of a person's stress levels, thereby reducing the risk of death and expensive treatments. The proposed system has the ability to produce results with an overall accuracy of 98.3% to 99.7%, is simple to implement and its cost is moderate. Stress-Lysis can not only help in keeping an individual self-aware by providing immediate feedback to change the lifestyle of the person in order to lead a healthier life but also plays a significant role in the state-of-the-art by allowing computing on the edge devices.