Stress Detection using Machine Learning Techniques: A review

In today's fast-paced society, stress is a prevalent issue that can have detrimental effects on one's health. This study aims to investigate machine learning-based techniques for detecting stress in humans. Participants who were engaged in a variety of their daily routine tasks were taken for dataset taking their physiological signs, including breathing rate, skin conductance, and heart rate variability into consideration. Then, in order to detect stress, these signals were utilized to train different models of machine learning like support vector machines, random forest and logistic regression. The accuracy, precision, recall, and F1 score were a few of the metrics used to assess performance of these methods. According to the findings, the random forest model had the highest level of stress detection accuracy at 89%. The development of wearable technology and other applications that can aid people in managing their stress levels causes significant impact on state-of-the-art technological advancements in this area.