AuthorsE. Garcia-Ceja, M. Riegler, T. Nordgreen, P. Jakobsen, K. J. Oedegaard and J. Tørresen
TitleMental Health Monitoring with Multimodal Sensing and Machine Learning: A Survey
AfilliationCommunication Systems, Machine Learning
StatusPublished
Publication TypeJournal Article
Year of Publication2018
JournalPervasive and Mobile Computing
Volume51
Pagination1-26
Date Published12/2018
PublisherElsevier
Abstract

Personal and ubiquitous sensing technologies such as smartphones have allowed the continuous collection of data in an unobtrusive manner. Machine learning methods have been applied to continuous sensor data to predict user contextual information such as location, mood, physical activity, etc. Recently, there has been a growing interest in leveraging ubiquitous sensing technologies for mental health care applications, thus, allowing the automatic continuous monitoring of different mental conditions such as depression, anxiety, stress, and so on. This paper surveys recent research works in mental health monitoring systems (MHMS) using sensor data and machine learning. We focused on research works about mental disorders/conditions such as: depression, anxiety, bipolar disorder, stress, etc. We propose a classification taxonomy to guide the review of related works and present the overall phases of MHMS. Moreover, research challenges in the field and future opportunities are also discussed.

DOI10.1016/j.pmcj.2018.09.003
Citation Key26167