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Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning

Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning Sensors in everyday devices, such as our phones, wearables, and computers, leave a stream of digital traces. Personal sensing refers to collecting and analyzing data from sensors embedded in the context of daily life with the aim of identifying human behaviors, thoughts, feelings, and traits. This article provides a critical review of personal sensing research related to mental health, focused principally on smartphones, but also including studies of wearables, social media, and computers. We provide a layered, hierarchical model for translating raw sensor data into markers of behaviors and states related to mental health. Also discussed are research methods as well as challenges, including privacy and problems of dimensionality. Although personal sensing is still in its infancy, it holds great promise as a method for conducting mental health research and as a clinical tool for monitoring at-risk populations and providing the foundation for the next generation of mobile health (or mHealth) interventions. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annual Review of Clinical Psychology Annual Reviews

Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning

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Publisher
Annual Reviews
Copyright
Copyright © 2017 by Annual Reviews. All rights reserved
ISSN
1548-5943
eISSN
1548-5951
DOI
10.1146/annurev-clinpsy-032816-044949
pmid
28375728
Publisher site
See Article on Publisher Site

Abstract

Sensors in everyday devices, such as our phones, wearables, and computers, leave a stream of digital traces. Personal sensing refers to collecting and analyzing data from sensors embedded in the context of daily life with the aim of identifying human behaviors, thoughts, feelings, and traits. This article provides a critical review of personal sensing research related to mental health, focused principally on smartphones, but also including studies of wearables, social media, and computers. We provide a layered, hierarchical model for translating raw sensor data into markers of behaviors and states related to mental health. Also discussed are research methods as well as challenges, including privacy and problems of dimensionality. Although personal sensing is still in its infancy, it holds great promise as a method for conducting mental health research and as a clinical tool for monitoring at-risk populations and providing the foundation for the next generation of mobile health (or mHealth) interventions.

Journal

Annual Review of Clinical PsychologyAnnual Reviews

Published: May 8, 2017

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