Review on Classification Techniques used in Biophysiological Stress Monitoring

TL;DR

This review paper analyzes various machine learning classification techniques used in biophysiological stress monitoring, discussing their applications, parameters, and factors influencing classifier choice in stress detection devices.

Contribution

It provides a comprehensive overview of classification methods applied to stress-related physiological signals and discusses criteria for selecting appropriate classifiers.

Findings

Various physiological parameters are used in stress monitoring devices.

Classifier selection depends on multiple factors beyond accuracy.

The review highlights common machine learning techniques in stress detection.

Abstract

Cardiovascular activities are directly related to the response of a body in a stressed condition. Stress, based on its intensity, can be divided into two types i.e. Acute stress (short-term stress) and Chronic stress (long-term stress). Repeated acute stress and continuous chronic stress may play a vital role in inflammation in the circulatory system and thus leads to a heart attack or to a stroke. In this study, we have reviewed commonly used machine learning classification techniques applied to different stress-indicating parameters used in stress monitoring devices. These parameters include Photoplethysmograph (PPG), Electrocardiographs (ECG), Electromyograph (EMG), Galvanic Skin Response (GSR), Heart Rate Variation (HRV), skin temperature, respiratory rate, Electroencephalograph (EEG) and salivary cortisol, used in stress monitoring devices. This study also provides a discussion on choosing a classifier, which depends upon a number of factors other than accuracy, like the number of subjects involved in an experiment, type of signals processing and computational limitations.