| Autor: |
Sarker H; University of Memphis., Tyburski M; NIDA Intramural Research Program., Rahman MM; University of Memphis., Hovsepian K; Troy University., Sharmin M; Western Washington University., Epstein DH; NIDA Intramural Research Program., Preston KL; NIDA Intramural Research Program., Furr-Holden CD; Johns Hopkins Bloomberg School of Public Health., Milam A; Johns Hopkins Bloomberg School of Public Health., Nahum-Shani I; University of Michigan., al'Absi M; University of Minnesota Medical School., Kumar S; University of Memphis. |
| Jazyk: |
angličtina |
| Zdroj: |
Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference [Proc SIGCHI Conf Hum Factor Comput Syst] 2016 May; Vol. 2016, pp. 4489-4501. |
| DOI: |
10.1145/2858036.2858218 |
| Abstrakt: |
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. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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