Survey of Applications of Ml in Stress Detection

Abstract

Stress is a common and pervasive issue that affects millions of people worldwide. It can lead to a variety of negative health outcomes, including anxiety, depression, and physical health problems. Early detection of stress is crucial for effective management and prevention of these negative outcomes. Stress detection technologies using machine learning algorithms can provide individuals with valuable information about their stress levels and help them manage their stress in more effective ways. This can lead to improved mental and physical health outcomes, as well as increased productivity and overall well-being. Therefore, stress detection is an important area of research that has the potential to positively impact the lives of many people.  

             This paper presents a survey of techniques applicable to the field of stress detection using machine learning (ML) algorithms. We categorize these techniques based on the approach they take and discuss various challenges, open questions, and future work in this area. We present a taxonomy of existing research and finally discuss gaps and future directions of research to advance the study of stress management using most recent ML techniques. These technologies

provide individuals with valuable information about their stress levels and can help them manage their stress in more effective ways.