Mental health monitoring with multimodal sensing and machine learning: A survey
By Enrique Garcia-Ceja, Michael Alexander Riegler, T. Nordgreen, P. Jakobsen, K. Oedegaard, J. Tørresen. Pervasive and Mobile Computing, 2018
319 citations recorded by the source corpus.
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.
Subfields: Applied Psychology, Experimental and Cognitive Psychology, Social Psychology
View Paper: https://www.semanticscholar.org/paper/fcb112a7a978dfe93572c41c8375ff7d3c18526f
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In Applied Psychology
In Experimental and Cognitive Psychology
- John B. Torous
- Sarah Graham
- Martin P. Paulus
- Ho-Cheol Kim
- M. de Choudhury
- Ellen E. Lee
- John H. Krystal
- Colin A. Depp
In Social Psychology
- John B. Torous
- Martin P. Paulus
- M. de Choudhury
- Glen A. Coppersmith
- Mark Dredze
- Nazanin Andalibi
- Andrea Forte
- Craig Harman
Citations in the corpus
- Artificial Intelligence for Mental Healthcare: Clinical Applications, Barriers, Facilitators, and Artificial Wisdom (2021, background): “…light) enables collection of real-world, continuous data concerning symptoms, treatment response, and behaviors, thoughts, and emotions (62,63). a r Platforms designed to enable multimodal data collection, like mindLAMP (64), AWARE (65), and CrossCheck (66) aid in continuous remote…”