title: Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning.
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.
authors.affiliation: Center for Behavioral Intervention Technologies and Department of Preventive Medicine, Northwestern University, Chicago, Illinois 60611; email: d-mohr@northwestern.edu , schueller@northwestern.edu.
authors.forename: David C
authors.initials: DC
authors.lastname: Mohr
authors.affiliation: Department of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan 48824; email: mizhang@egr.msu.edu.
authors.forename: Mi
authors.initials: M
authors.lastname: Zhang
authors.affiliation: Center for Behavioral Intervention Technologies and Department of Preventive Medicine, Northwestern University, Chicago, Illinois 60611; email: d-mohr@northwestern.edu , schueller@northwestern.edu.
authors.forename: Stephen M
authors.initials: SM
authors.lastname: Schueller
mesh_terms: D006801:Humans; D000069550:Machine Learning; D001523:Mental Disorders / Q000175:diagnosis*; D064926:Neurophysiological Monitoring; D017216:Telemedicine
keywords: mHealth; machine learning; mental health; pervasive health; sensors; wearables