University of Oulu

Kennedy Opoku Asare, Aku Visuri, and Denzil S. T. Ferreira. 2019. Towards early detection of depression through smartphone sensing. In Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers (UbiComp/ISWC '19 Adjunct). ACM, New York, NY, USA, 1158-1161. DOI: https://doi.org/10.1145/3341162.3347075

Towards early detection of depression through smartphone sensing

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Author: Opoku Asare, Kennedy1; Visuri, Aku1; Ferreira, Denzil S.T.1
Organizations: 1University of Oulu, Oulu, Oulu
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.6 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2019091628436
Language: English
Published: Association for Computing Machinery, 2019
Publish Date: 2019-09-16
Description:

Abstract

Major depressive disorder is a complex and common mental health disorder that is heterogeneous and varies between individuals. Predictive measures have previously been used to predict depression in individuals. Given the complexity, heterogeneity of major depressive disorder in individuals, and the scarcity of labelled objective depressive behavioural data, predictive measures have shown limited applicability in detecting the early onset of depression. We present a developed system that collects similar smartphone sensor data like in previous predictive analysis studies. We discuss that anomaly detection and entropy analysis methods are best suited for developing new metrics for the early detection of the onset and progression of major depressive disorder.

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ISBN: 978-1-4503-6869-8
Pages: 1158 - 1161
DOI: 10.1145/3341162.3347075
OADOI: https://oadoi.org/10.1145/3341162.3347075
Host publication: Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2019 International Symposium on Wearable Computers (Ubi- Comp/ISWC ’19 Adjunct), September 9–13, 2019, London, United Kingdom
Conference: ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2019 International Symposium on Wearable Computers
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
Subjects:
Funding: This work is partially funded by the Academy of Finland (Grants 313224-STOP, 316253,320089-SENSATE and 318927-6Genesis Flagship) and Infotech Oulu.
Academy of Finland Grant Number: 313224
316253
320089
318927
Detailed Information: 313224 (Academy of Finland Funding decision)
316253 (Academy of Finland Funding decision)
320089 (Academy of Finland Funding decision)
318927 (Academy of Finland Funding decision)
Copyright information: © 2019 Association for Computing Machinery. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers (UbiComp/ISWC '19 Adjunct), https://doi.org/10.1145/3341162.3347075.