The DOI matches its Crossref record; 185 of the 193 references it cites were found in a registry or an index.
Mental health monitoring with multimodal sensing and machine learning: A survey
Open accessAbstract
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.
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“Close interrelationships between everyday behavior, physiology, and mental well-being makes digital phenotyping with wearables especially attractive for detection of mental disorders and discovering respective digital biomarkers [8-11].”
From Digital Biomarkers for Depression Screening With Wearable Devices: Cross-sectional Study With Machine Learning Modeling (2021) · cites this paper as [11]
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Lee, E., Torous, J. B., Choudhury, M. D., Depp, C. A., Graham, S. A., Kim, H., Paulus, M. P., Krystal, J. H., & Jeste, D. V. (2021) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2021.02.001 · Cited by 523
OECD (2021) · OECD Health Policy Studies · doi:10.1787/4ed890f6-en · Cited by 407
Machine Learning for Healthcare Wearable Devices: The Big Picture
Sabry, F., Eltaras, T., Labda, W., Alzoubi, K., & Malluhi, Q. (2022) · Journal of Healthcare Engineering · doi:10.1155/2022/4653923 · Cited by 321
Vistorte, A. O. R., Deroncele-Acosta, Á., Ayala, J. L. M., Barrasa, Á., López-Granero, C., & Martí-González, M. (2024) · Frontiers in Psychology · doi:10.3389/fpsyg.2024.1387089 · Cited by 412
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Metadata
- Authors
- Garcia-Ceja, E., Riegler, M., Nordgreen, T., Jakobsen, P., Oedegaard, K. J., & Tørresen, J.
- Affiliations
- University of Oslo (NO)
- Haukeland University Hospital (NO)
- Corresponding author
- Garcia-Ceja, Enrique
- ORCID
- Venue
- Pervasive and Mobile Computing
- Publisher
- Elsevier BV
- Published
- 2018-12
- Volume
- 51
- Pages
- 1-26
- ISSN
- 1574-1192 (ISSN-L)
- OpenAlex
- W2890929258
- Cited by
- 356
- References
- 193
- Citations by year
- 2026: 23 · 2025: 53 · 2024: 62 · 2023: 60 · 2022: 70 · 2021: 47 · 2020: 27 · 2019: 14
- FWCI
- 28.82
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Pervasive and Mobile Computing (publishedVersion, cc-by-nc-nd)
- Funders
- Norwegian Research Council (259293/o70)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Keywords
- mental health monitoring, ubiquitous sensing, smartphones, multimodal sensing, depression detection, anxiety detection, stress detection, bipolar disorder, continuous monitoring, classification taxonomy
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