TY - GEN
T1 - Investigating the role of context in perceived stress detection in the wild
AU - Mishra, Varun
AU - Hao, Tian
AU - Sun, Si
AU - Walter, Kimberly N.
AU - Ball, Marion J.
AU - Chen, Ching Hua
AU - Zhu, Xinxin
N1 - Publisher Copyright:
© 2018 Association for Computing Machinery.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2018/10/8
Y1 - 2018/10/8
N2 - The advances in mobile and wearable sensing have led to a myriad of approaches for stress detection in both laboratory and free-living settings. Most of these methods, however, rely on the usage of some combination of physiological signals measured by the sensors to detect stress. While these solutions work great in a lab or a controlled environment, the performance in free-living situations leaves much to be desired. In this work, we explore the role of context of the user in free-living conditions, and how that affects users' perceived stress levels. To this end, we conducted an'in-the-wild' study with 23 participants, where we collected physiological data from the users, along with'high-level' contextual labels, and perceived stress levels. Our analysis shows that context plays a significant role in the users' perceived stress levels, and when used in conjunction with physiological signals leads to much higher stress detection results, as compared to relying on just physiological data.
AB - The advances in mobile and wearable sensing have led to a myriad of approaches for stress detection in both laboratory and free-living settings. Most of these methods, however, rely on the usage of some combination of physiological signals measured by the sensors to detect stress. While these solutions work great in a lab or a controlled environment, the performance in free-living situations leaves much to be desired. In this work, we explore the role of context of the user in free-living conditions, and how that affects users' perceived stress levels. To this end, we conducted an'in-the-wild' study with 23 participants, where we collected physiological data from the users, along with'high-level' contextual labels, and perceived stress levels. Our analysis shows that context plays a significant role in the users' perceived stress levels, and when used in conjunction with physiological signals leads to much higher stress detection results, as compared to relying on just physiological data.
KW - Context aware
KW - Mental health
KW - Mobile health (mHealth)
KW - Stress detection
KW - Wearable sensing
UR - http://www.scopus.com/inward/record.url?scp=85058292111&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85058292111&partnerID=8YFLogxK
U2 - 10.1145/3267305.3267537
DO - 10.1145/3267305.3267537
M3 - Conference contribution
AN - SCOPUS:85058292111
T3 - UbiComp/ISWC 2018 - Adjunct Proceedings of the 2018 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2018 ACM International Symposium on Wearable Computers
SP - 1708
EP - 1716
BT - UbiComp/ISWC 2018 - Adjunct Proceedings of the 2018 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2018 ACM International Symposium on Wearable Computers
PB - Association for Computing Machinery, Inc
T2 - 2018 Joint ACM International Conference on Pervasive and Ubiquitous Computing, UbiComp 2018 and 2018 ACM International Symposium on Wearable Computers, ISWC 2018
Y2 - 8 October 2018 through 12 October 2018
ER -