A Systematic Review of Technology-Aided Stress Management Systems: Automatic Measurement, Detection and Control

Abstract

Even though stress response is a defense mechanism of the body to deal with adverse daily situations, prolonged exposure to these effects can trigger significant detriments to physical and mental health. The aim of this systematic review is to identify the use of technological tools in stress management, with a special focus on feedback control systems that include detection, control, and intervention phases. The databases selected for this systematic review, which applies the PRISMA protocol, are Scopus, IEEE Xplore, Web of Science, and Science Direct. We include research works that have experiments involving automated physiological data collection through non-invasive methods and an intervention technique to manage stress. Applying these criteria, a total of 75 articles are included in the final analysis. The quality of the included articles was assessed in the search strategy, the selection process and the data collection process, following the eligibility criteria. Summarizing some results, almost half of the studies included fifty or fewer participants in the experiments and twelve physiological variables were identified, being HR and ECG the most important ones. The most used technique of stress management was breathing and 16 articles used some type of feedback control, mainly biofeedback. Several promising physiological variables and intervention techniques are identified for implementing stress management systems. Although using machine learning in stress detection is common, its application to develop feedback control systems is limited. Moreover, it was found that the theory of control in dynamical systems has not been applied yet to design automatic stress management systems.

Keywords

• Automatic measurement
• feedback systems
• intervention techniques
• physiological signals
• stress management
• systematic review