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

doi:10.1145/2858036.2858218

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

Bloomberg School of Public Health

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

42 Scopus citations

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 language	English (US)
Title of host publication	CHI 2016 - Proceedings, 34th Annual CHI Conference on Human Factors in Computing Systems
Publisher	Association for Computing Machinery
Pages	4489-4501
Number of pages	13
ISBN (Electronic)	9781450333627
DOIs	https://doi.org/10.1145/2858036.2858218
State	Published - May 7 2016
Event	34th Annual Conference on Human Factors in Computing Systems, CHI 2016 - San Jose, United States Duration: May 7 2016 → May 12 2016

Other

Other	34th Annual Conference on Human Factors in Computing Systems, CHI 2016
Country/Territory	United States
City	San Jose
Period	5/7/16 → 5/12/16

Keywords

Intervention
Mobile health (mHealth)
Stress management

ASJC Scopus subject areas

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

Access to Document

10.1145/2858036.2858218

Cite this

Sarker, H., Tyburskir, M., Rahman, M. D. M., Hovsepian, K., Sharmin, M., Epsteinr, D. H., Prestonr, K. L., Furr-Holden, C. D., Milam, A., Nahum-Shani, I., Al'Absi, M., & Kumar, S. (2016). Finding significant stress episodes in a discontinuous time series of rapidly varying mobile sensor data. In CHI 2016 - Proceedings, 34th Annual CHI Conference on Human Factors in Computing Systems (pp. 4489-4501). Association for Computing Machinery. https://doi.org/10.1145/2858036.2858218

Finding significant stress episodes in a discontinuous time series of rapidly varying mobile sensor data. / Sarker, Hillol; Tyburskir, Matthew; Rahman, M. D.Mahbubur et al.

CHI 2016 - Proceedings, 34th Annual CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, 2016. p. 4489-4501.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Sarker, H, Tyburskir, M, Rahman, MDM, Hovsepian, K, Sharmin, M, Epsteinr, DH, Prestonr, KL, Furr-Holden, CD, Milam, A, Nahum-Shani, I, Al'Absi, M & Kumar, S 2016, Finding significant stress episodes in a discontinuous time series of rapidly varying mobile sensor data. in CHI 2016 - Proceedings, 34th Annual CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, pp. 4489-4501, 34th Annual Conference on Human Factors in Computing Systems, CHI 2016, San Jose, United States, 5/7/16. https://doi.org/10.1145/2858036.2858218

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AU - Prestonr, Kenzie L.

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AB - 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.

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

Abstract

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Keywords

ASJC Scopus subject areas

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