Wearable Healthcare Devices for Monitoring Stress and Attention Level in Workplace Environments

TL;DR

This paper reviews wearable devices and algorithms for non-intrusive monitoring of stress and attention in workplaces, emphasizing their potential to improve employee well-being and prevent burnout.

Contribution

It provides a comprehensive comparison of current wearable sensors and algorithms for assessing stress and concentration in real-world workplace environments.

Findings

Various sensors can accurately measure stress and attention levels.

Algorithms show promising performance but need further validation in uncontrolled settings.

Integration with healthcare systems can enhance workplace mental health management.

Abstract

Wearable devices have revolutionized healthcare monitoring, allowing us to track physiological conditions without disrupting daily routines. Whereas monitoring physical health and physical activities have been widely studied, their application and impact on mental health are significantly understudied. This work reviews the state-of-the-art, focusing on stress and concentration levels. These two can play an important role in workplace humanization. For instance, they can guide breaks in high-pressure workplaces, indicating when and how long to take. Those are important to avoid overwork and burn-out, harming employees and employers. To this end, it is necessary to study which sensors can accurately determine stress and attention levels, considering that they should not interfere with their activities and be comfortable to wear. From the software point of view, it is helpful to know the capabilities and performance of various algorithms, especially for uncontrolled workplace environments. This work aims to research, review, and compare commercially available non-intrusive measurement devices, which can be worn during the day and possibly integrated with healthcare systems for stress and concentration assessment. We analyze the performance of various algorithms used for stress and concentration level assessment and discuss future paths for reliable detection of these two parameters.