Human Stress Detection Technologies: An In‐Depth Comparison of Machine Learning Algorithms and Applications

ABSTRACT Workload pressure, examination stress, family responsibilities, and a variety of other factors all contribute to an increase in stress in the body. Stress weakens the human mind and body by accelerating several health disorders. Therefore, the number of approaches for early projection of stress plays a vital role in the healthcare sector. Stress prediction techniques can be broadly categorised as questionnaire‐based, where a psychiatrist provides a feedback form to the user to identify the status of stress. In sensor‐based stress measurement methods, stress will be measured by some symbolic constraints like heart rate, skin conductance, pupil diameter, and a list of questions. For collecting the crucial data for stress detection, skin temperature (ST), electrocardiogram (ECG), and electrodermal activity (EDA) are keenly monitored during the experiment. In this manuscript, a comparative study was performed using the results of various techniques to predict human stress. This article presents various techniques for stress prediction and various opportunities to improve their performance.