Towards Multi-modal Anticipatory Monitoring of Depressive States through the Analysis of Human-Smartphone Interaction

Abhinav Mehrotra; Robert Hendley; Mirco Musolesi

doi:10.1145/2968219.2968299

Towards Multi-modal Anticipatory Monitoring of Depressive States through the Analysis of Human-Smartphone Interaction

Abhinav Mehrotra, Robert Hendley, Mirco Musolesi

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

36 Citations (Scopus)

265 Downloads (Pure)

Abstract

Remarkable advances in smartphone technology, especially in terms of passive sensing, have enabled researchers to passively monitor user behavior in real-time and at a granularity that was not possible just a few years ago. Recently,
different approaches have been proposed to investigate the use of different sensing and phone interaction features, including location, call, SMS and overall application usage logs, to infer the depressive state of users. In this paper,
we propose an approach for monitoring of depressive states using multi-modal sensing via smartphones. Through a brief literature review we show the sensing modalities that have been exploited in the past studies for monitoring depression. We then present the initial results of an ongoing study to demonstrate the association of depressive states with the smartphone interaction features. Finally, we discuss the challenges in predicting depression through multimodal mobile sensing.

Original language	English
Title of host publication	UbiComp '16 Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing:
Subtitle of host publication	Adjunct
Publisher	Association for Computing Machinery
Pages	1132-1138
Number of pages	7
ISBN (Print)	978-1-4503-4462-3
DOIs	https://doi.org/10.1145/2968219.2968299
Publication status	Published - 12 Sept 2016
Event	Mental Health: Sensing and Intervention Workshop 2016: at Ubicomp 2016 - Heidelberg, Germany Duration: 13 Sept 2016 → 13 Sept 2016

Workshop

Workshop	Mental Health: Sensing and Intervention Workshop 2016
Country/Territory	Germany
City	Heidelberg
Period	13/09/16 → 13/09/16

Keywords

Mobile Sensing
Depression
Anticipatory Computing
Behaviour Change Interventions

Access to Document

10.1145/2968219.2968299Licence: None: All rights reserved

Mehrotra_et_al_Towards_Multi-modal_anticipatory monitoring_MHSI_2016
© ACM, 2016. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of 1st Mental Health: Sensing and Intervention Workshop 2016
Accepted author manuscript, 124 KBLicence: None: All rights reserved

http://dl.acm.org/citation.cfm?doid=2968219.2968299Licence: None: All rights reserved

Trajectories of Depression: Investigating the Correlation between Human Mobility Patterns and Mental Health Problems by means of Smartphones
Musolesi, M.
Engineering & Physical Science Research Council
31/03/14 → 30/05/15
Project: Research Councils

Cite this

Mehrotra, A., Hendley, R., & Musolesi, M. (2016). Towards Multi-modal Anticipatory Monitoring of Depressive States through the Analysis of Human-Smartphone Interaction. In UbiComp '16 Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct (pp. 1132-1138). Association for Computing Machinery . https://doi.org/10.1145/2968219.2968299

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title = "Towards Multi-modal Anticipatory Monitoring of Depressive States through the Analysis of Human-Smartphone Interaction",

abstract = "Remarkable advances in smartphone technology, especially in terms of passive sensing, have enabled researchers to passively monitor user behavior in real-time and at a granularity that was not possible just a few years ago. Recently,different approaches have been proposed to investigate the use of different sensing and phone interaction features, including location, call, SMS and overall application usage logs, to infer the depressive state of users. In this paper,we propose an approach for monitoring of depressive states using multi-modal sensing via smartphones. Through a brief literature review we show the sensing modalities that have been exploited in the past studies for monitoring depression. We then present the initial results of an ongoing study to demonstrate the association of depressive states with the smartphone interaction features. Finally, we discuss the challenges in predicting depression through multimodal mobile sensing.",

keywords = "Mobile Sensing , Depression , Anticipatory Computing , Behaviour Change Interventions",

author = "Abhinav Mehrotra and Robert Hendley and Mirco Musolesi",

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doi = "10.1145/2968219.2968299",

language = "English",

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pages = "1132--1138",

booktitle = "UbiComp '16 Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing:",

publisher = "Association for Computing Machinery ",

note = "Mental Health: Sensing and Intervention Workshop 2016 : at Ubicomp 2016 ; Conference date: 13-09-2016 Through 13-09-2016",

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Mehrotra, A, Hendley, R & Musolesi, M 2016, Towards Multi-modal Anticipatory Monitoring of Depressive States through the Analysis of Human-Smartphone Interaction. in UbiComp '16 Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct. Association for Computing Machinery , pp. 1132-1138, Mental Health: Sensing and Intervention Workshop 2016, Heidelberg, Germany, 13/09/16. https://doi.org/10.1145/2968219.2968299

Towards Multi-modal Anticipatory Monitoring of Depressive States through the Analysis of Human-Smartphone Interaction. / Mehrotra, Abhinav; Hendley, Robert; Musolesi, Mirco.
UbiComp '16 Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct. Association for Computing Machinery , 2016. p. 1132-1138.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Towards Multi-modal Anticipatory Monitoring of Depressive States through the Analysis of Human-Smartphone Interaction

AU - Mehrotra, Abhinav

AU - Hendley, Robert

AU - Musolesi, Mirco

PY - 2016/9/12

Y1 - 2016/9/12

N2 - Remarkable advances in smartphone technology, especially in terms of passive sensing, have enabled researchers to passively monitor user behavior in real-time and at a granularity that was not possible just a few years ago. Recently,different approaches have been proposed to investigate the use of different sensing and phone interaction features, including location, call, SMS and overall application usage logs, to infer the depressive state of users. In this paper,we propose an approach for monitoring of depressive states using multi-modal sensing via smartphones. Through a brief literature review we show the sensing modalities that have been exploited in the past studies for monitoring depression. We then present the initial results of an ongoing study to demonstrate the association of depressive states with the smartphone interaction features. Finally, we discuss the challenges in predicting depression through multimodal mobile sensing.

AB - Remarkable advances in smartphone technology, especially in terms of passive sensing, have enabled researchers to passively monitor user behavior in real-time and at a granularity that was not possible just a few years ago. Recently,different approaches have been proposed to investigate the use of different sensing and phone interaction features, including location, call, SMS and overall application usage logs, to infer the depressive state of users. In this paper,we propose an approach for monitoring of depressive states using multi-modal sensing via smartphones. Through a brief literature review we show the sensing modalities that have been exploited in the past studies for monitoring depression. We then present the initial results of an ongoing study to demonstrate the association of depressive states with the smartphone interaction features. Finally, we discuss the challenges in predicting depression through multimodal mobile sensing.

KW - Mobile Sensing

KW - Depression

KW - Anticipatory Computing

KW - Behaviour Change Interventions

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DO - 10.1145/2968219.2968299

M3 - Conference contribution

SN - 978-1-4503-4462-3

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BT - UbiComp '16 Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing:

PB - Association for Computing Machinery

T2 - Mental Health: Sensing and Intervention Workshop 2016

Y2 - 13 September 2016 through 13 September 2016

ER -

Towards Multi-modal Anticipatory Monitoring of Depressive States through the Analysis of Human-Smartphone Interaction

Abstract

Workshop

Keywords

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Projects

Trajectories of Depression: Investigating the Correlation between Human Mobility Patterns and Mental Health Problems by means of Smartphones

Cite this