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

Authors:

Matthew Tyburski,

Md Mahbubur Rahman,

Karen Hovsepian,

Moushumi Sharmin,

David H. Epstein,

Kenzie L. Preston,

C. Debra Furr-Holden,

Inbal Nahum-Shani,

Mustafa al'Absi,

Santosh KumarAuthors Info & Claims

CHI '16: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems

Pages 4489 - 4501

https://doi.org/10.1145/2858036.2858218

Published: 07 May 2016 Publication History

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.

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Index Terms

Finding Significant Stress Episodes in a Discontinuous Time Series of Rapidly Varying Mobile Sensor Data
1. Human-centered computing
  1. Human computer interaction (HCI)

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CHI '16: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems

May 2016

6108 pages

ISBN:9781450333627

DOI:10.1145/2858036

General Chairs:
Jofish Kaye
Yahoo
,
Allison Druin
University of Maryland / National Park Service
,
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Cliff Lampe
University of Michigan
,
Dan Morris
Microsoft
,
Juan Pablo Hourcade
University of Iowa

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