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.

Keywords: mHealth; machine learning; mental health; pervasive health; sensors; wearables.

Figures

Figure 1

Example of a layered, hierarchical sensemaking framework. Green boxes at the bottom of the figure represent inputs to the sensing platform. Yellow boxes represent features. Blue boxes represent high-level behavioral markers. Abbreviations: GPS, global positioning system; SMS, short message service.