Machine Learning-based Signal Processing by Physiological Signals Detection of Stress

Abstract

Stress is a normal part of daily life that most people experience at different times. However, chronic stress or high levels of stress will jeopardize our safety and disrupt our normal lives. As a result, the capacity to operate and manage in critical situations is greatly reduced. Therefore, it is necessary to understand pressures and design processes with an understanding of pressure. In this paper, we are introduced to the process of processing signals according to machine learning algorithms:We have used natural data collected, such as Respiration, GSR Hand, GSR Foot, Heart Rate, and EMG, from various studies in a variety of situations and areas while driving. After that, the data division at different times, such as 100, 200 seconds, and 300 seconds, was done differently.We have removed the mathematical features from the separated data and fed these features into the available separator. We used KNN, K's closest neighbor, and a vector support machine, which is very different.We divided the pressure into three levels: low, medium, and high.Our results show that the pressure level can be reached with an accuracy of 98.41% in 100 seconds and 200 seconds simultaneously and 99% with a time interval of 300 seconds.