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

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 propos...

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Bibliografske podrobnosti
izdano v:	Proc SIGCHI Conf Hum Factor Comput Syst
Main Authors:	Sarker, Hillol, Tyburski, Matthew, Rahman, Md. Mahbubur, Hovsepian, Karen, Sharmin, Moushumi, Epstein, David H., Preston, Kenzie L., Furr-Holden, C. Debra, Milam, Adam, Nahum-Shani, Inbal, al’Absi, Mustafa, Kumar, Santosh
Format:	Artigo
Jezik:	Inglês
Izdano:	2016
Teme:	Article
Online dostop:	https://ncbi.nlm.nih.gov/pmc/articles/PMC5207658/ https://ncbi.nlm.nih.gov/pubmed/28058409 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1145/2858036.2858218
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Finding Significant Stress Episodes in a Discontinuous Time Series of Rapidly Varying Mobile Sensor Data

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