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Finding Significant Stress Episodes in a Discontinuous Time Series of Rapidly Varying Mobile Sensor Data

Authors :
Matthew Tyburski
Moushumi Sharmin
Inbal Nahum-Shani
Hillol Sarker
Kenzie L. Preston
Karen Hovsepian
Adam J. Milam
Mahbubur Rahman
Santosh Kumar
C. Debra M. Furr-Holden
Mustafa al'Absi
David H. Epstein
Source :
CHI
Publication Year :
2016
Publisher :
ACM, 2016.

Abstract

Management of daily stress can be greatly improved by delivering sensor-triggered just-in-time interventions (JITIs) on mobile devices. The success of such JITIs critically depends on being able to mine the time series of noisy sensor data to find the most opportune moments. In this paper, we propose a time series pattern mining method to detect significant stress episodes in a time series of discontinuous and rapidly varying stress data. We apply our model to 4 weeks of physiological, GPS, and activity data collected from 38 users in their natural environment to discover patterns of stress in real-life. We find that the duration of a prior stress episode predicts the duration of the next stress episode and stress in mornings and evenings is lower than during the day. We then analyze the relationship between stress and objectively rated disorder in the surrounding neighborhood and develop a model to predict stressful episodes.

Details

Database :
OpenAIRE
Journal :
Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems
Accession number :
edsair.doi.dedup.....b2e2da8547326342634b7034c6bb30fb