Detection of Stress with Deep Learning and Health Parameters Monitoring Using Raspberry Pi

Abstract

Now in day-to-day life, every individual faces some sort of stress due to workload, school, or form their lifestyle. Stress is an unwanted portion of life that is seen in people. As stress can have some positive effects, but it has adverse effects on your health if it is for a long time. Therefore, it is crucial to detect a person’s stress, as it can lead one to depression. It is observed that stress leads to worse health situations. It is also found that stress can affect health by increasing heart rate, decrease in oxygen level of the body, and increase in weight. It can lead to obesity. Regular change in BMI also indicates that a person is stressed. Stress is threatening mankind’s well-being. Due to stress, body releases a hormone that speeds up your heart rate and breathing. In the detection of stress, facial expressions have a pivotal spot. The proposed system will detect stress based on three stress-related facial expressions sad, angry, and fear. The proposed work in this paper is to make individuals aware of their physical condition. The aim of this paper is to detect stress by using deep learning concepts and health parameters monitoring such as heart rate, oxygen level, and BMI using Raspberry pi. And the measured results of the different persons with the proposed system and the existing system are given in the table.