Abstract
Eustress is literally the "good stress" that associated with positive feelings and health benefits. Previous studies focused on general stress, where the concept of eustress has been overlooked. This paper presents a novel approach towards stress recognition using data collected from wearable sensors, smartphones, and computers. The main goal is to determine if behavioral factors can help differentiate eustress from another kind of stress. We conducted a natural experiment to collect user smartphone and computer usage, heart rate and survey data in situ. By correlation and principle component analysis, a set of features could then be constructed. The performance was evaluated under leave-one-subject-out cross-validation, where the combined behavioral and physiological features enabled us to achieve 84.85% accuracy for general stress, 71.33% one kind of eustress as an urge for better performance, and 57.34% for eustress as a state of better mood. This work provided an encouraging result as an initial study for measuring eustress.
Original language | English |
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Title of host publication | UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing |
Publisher | Association for Computing Machinery, Inc |
Pages | 1209-1217 |
Number of pages | 9 |
ISBN (Electronic) | 9781450344623 |
DOIs | |
Publication status | Published - 12 Sept 2016 |
Event | 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2016 - Heidelberg, Germany Duration: 12 Sept 2016 → 16 Sept 2016 |
Conference
Conference | 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2016 |
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Country/Territory | Germany |
City | Heidelberg |
Period | 12/09/16 → 16/09/16 |
Keywords
- Eustress
- MHealth
- Stress
- Ubiquitous Computing
ASJC Scopus subject areas
- Hardware and Architecture
- Software
- Information Systems
- Computer Networks and Communications
- Human-Computer Interaction