Annual Review of Clinical Psychology· 2017Q1· Review
Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning
- 826citations
- Q1SCImago
- 2017year
Short summary
Personal sensing uses data from everyday devices like smartphones and wearables to identify behaviors, thoughts, and feelings related to mental health.
AI-generated from the title and abstract; the full text is not read.
Key points
- Personal sensing collects and analyzes data from everyday device sensors to understand mental health.
- Research primarily focuses on smartphones but also includes wearables, social media, and computers.
- A hierarchical model is proposed to translate raw sensor data into mental health markers.
- Key challenges include privacy concerns and data dimensionality.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source. Annual Review of Clinical Psychology, 2017 · DOI ↗
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Field: Applied Psychology
Applied PsychologyPsychology