Unobtrusive monitoring of behavior and movement patterns to detect clinical depression severity level via smartphone

Msud, M, Mamun, M, Thappa, K, Lee, DH, Griffiths, M ORCID: https://orcid.org/0000-0001-8880-6524 and Yang, S-H, 2020. Unobtrusive monitoring of behavior and movement patterns to detect clinical depression severity level via smartphone. Journal of Biomedical Informatics, 103: 103371. ISSN 1532-0464

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Abstract

The number of individuals with mental disorders is increasing and they are commonly found among individuals who avoid social interaction and like to live alone. Amongst such mental health disorders is depression which is both common and serious. The present paper introduces a method to assess the depression level of an individual using a smartphone by monitoring their daily activities. The time domain characteristics from a smartphone acceleration sensor were used alongside a vector machine algorithm to classify physical activities. Additionally, the geographical location information was clustered using a smartphone GPS sensor to simplify movement patterns. A total of 12 features were extracted from individuals’ physical activity and movement patterns and were analyzed alongside their weekly depression scores using the nine-item Patient Health Questionnaire. Using a wrapper feature selection method, a subset of features was selected and applied to a linear regression model to estimate the depression score. The support vector machine algorithm was then used to classify the depression severity level among individuals (absence, moderate, severe) and had an accuracy of 87.2% in severe depression cases which outperformed other classification models including the k-nearest neighbor and artificial neural network. This method of identifying depression is a cost-effective solution for long-term use and can monitor individuals for depression without invading their personal space or creating other day-to-day disturbances.

Item Type:	Journal article
Publication Title:	Journal of Biomedical Informatics
Creators:	Msud, M., Mamun, M., Thappa, K., Lee, D.H., Griffiths, M. and Yang, S.-H.
Publisher:	Elsevier
Date:	March 2020
Volume:	103
ISSN:	1532-0464
Identifiers:	Number Type 10.1016/j.jbi.2019.103371 DOI S1532046419302916 Publisher Item Identifier 1274846 Other
Divisions:	Schools > School of Social Sciences
Record created by:	Linda Sullivan
Date Added:	22 Jan 2020 09:09
Last Modified:	31 May 2021 15:08
URI:	https://irep.ntu.ac.uk/id/eprint/39039

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