Finding significant stress episodes in a discontinuous time series of rapidly varying mobile sensor data

Hillol Sarker, Matthew Tyburskir, M. D.Mahbubur Rahman, Karen Hovsepian, Moushumi Sharmin, David H. Epsteinr, Kenzie L. Prestonr, C. Debra Furr-Holden, Adam Milam, Inbal Nahum-Shani, Mustafa Al'Absi, Santosh Kumar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationCHI 2016 - Proceedings, 34th Annual CHI Conference on Human Factors in Computing Systems
PublisherAssociation for Computing Machinery
Pages4489-4501
Number of pages13
ISBN (Electronic)9781450333627
DOIs
StatePublished - May 7 2016
Event34th Annual Conference on Human Factors in Computing Systems, CHI 2016 - San Jose, United States
Duration: May 7 2016May 12 2016

Other

Other34th Annual Conference on Human Factors in Computing Systems, CHI 2016
CountryUnited States
CitySan Jose
Period5/7/165/12/16

Keywords

  • Intervention
  • Mobile health (mHealth)
  • Stress management

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Graphics and Computer-Aided Design
  • Software

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