Real-life stress level monitoring using smart bands in the light of contextual information

Can, YS; Chalabianloo, N; Ekiz, D; Fernandez-Alvarez, J; Repetto, C; Riva, G; Iles-Smith, HM; Ersoy, C

Abstract

An automatic stress detection system that uses unobtrusive smart bands will contribute to human health and well-being by alleviating the effects of high stress levels. However, there are a number of challenges for detecting stress in unrestricted daily life which results in lower performances of such systems when compared to semi-restricted and laboratory environment studies. The addition of contextual information such as physical activity level, activity type and weather to the physiological signals can improve the classification accuracies of these systems. We developed an automatic stress detection system that employs smart bands for physiological data collection. In this study, we monitored the stress levels of 16 participants of an EU project training every day throughout the eight days long event by using our system. We collected 1440 hours of physiological data and 2780 self-report questions from the participants who are from diverse countries. The project midterm presentations (see Figure 3) in front of a jury at the end of the event were the source of significant real stress. Different types of contextual information, along with the physiological data, were recorded to determine the perceived stress levels of individuals. We further analyze the physiological signals in this event to infer long term perceived stress levels which we obtained from baseline PSS-14 questionnaires. Session-based, daily and long-term perceived stress levels could be identified by using the proposed system successfully.

Citation

Can, Y., Chalabianloo, N., Ekiz, D., Fernandez-Alvarez, J., Repetto, C., Riva, G., …Ersoy, C. (2020). Real-life stress level monitoring using smart bands in the light of contextual information. IEEE Sensors Journal, 20(15), 8721-8730. https://doi.org/10.1109/JSEN.2020.2984644

• Journal Article Type: Article
• Acceptance Date: Mar 28, 2020
• Online Publication Date: Mar 31, 2020
• Publication Date: Aug 1, 2020
• Deposit Date: Nov 20, 2020
• Publicly Available Date: Nov 20, 2020
• Journal: IEEE Sensors Journal
• Print ISSN: 1530-437X
• Electronic ISSN: 1558-1748
• Publisher: Institute of Electrical and Electronics Engineers
• Volume: 20
• Issue: 15
• Pages: 8721-8730
• DOI: https://doi.org/10.1109/JSEN.2020.2984644
• Publisher URL: https://doi.org/10.1109/JSEN.2020.2984644
• Related Public URLs: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=7361
• Additional Information: Projects : AffecTech Personal Technologies for Affective Health