research-article

Public Access

Large-scale Automatic Depression Screening Using Meta-data from WiFi Infrastructure

Authors:

Reynaldo Morillo,

Athanasios Bamis,

Alexander Russell,

Bing WangAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 2, Issue 4

Article No.: 195, Pages 1 - 27

https://doi.org/10.1145/3287073

Published: 27 December 2018 Publication History

Abstract

Depression is a serious public health problem. Current diagnosis techniques rely on physician-administered or patient self-administered interview tools, which are burdensome and suffer from recall bias. Recent studies have proposed new approaches that use sensing data collected on smartphones to serve as "human sensors" for automatic depression screening. These approaches, however, require running an app on the phones for continuous data collection. We explore a novel approach that uses data collected from WiFi infrastructure for large-scale automatic depression screening. Specifically, when smartphones connect to a WiFi network, their locations (and hence the locations of the users) can be determined by the access points that they associate with; the location information over time provides important insights into the behavior of the users, which can be used for depression screening. To investigate the feasibility of this approach, we have analyzed two datasets, each collected over several months, involving tens of participants recruited from a university. Our results demonstrate that WiFi meta-data is effective for passive depression screening: the F1 scores are as high as 0.85 for predicting depression, comparable to those obtained by using sensing data collected directly from smartphones.

References

[1]

Centers for Disease Control and Prevention. National Center for Injury Prevention and Control., 2010. http:www.cdc.gov/ncipc/wisqars.

[2]

C. Aguilar-Melchor and P. Gaborit. A lattice-based computationally-efficient private information retrieval protocol. In Proc. of Western European Workshop on Research in Cryptology (WEWoRC), July 2007.

[3]

A. Balasubramanian, R. Mahajan, and A. Venkataramani. Augmenting mobile 3g using wifi. In Proceedings of the 8th international conference on Mobile systems, applications, and services, pages 209--222. ACM, 2010.

Digital Library

[4]

N. Balasubramanian, A. Balasubramanian, and A. Venkataramani. Energy consumption in mobile phones: a measurement study and implications for network applications. In Proceedings of the 9th ACM SIGCOMM Conference on Internet Measurement, pages 280--293. ACM, 2009.

Digital Library

[5]

D. Ben-Zeev, E. A. Scherer, R. Wang, H. Xie, and A. T. Campbell. Next-generation psychiatric assessment: Using smartphone sensors to monitor behavior and mental health. Psychiatric Rehabilitation Journal, 38(3):218--226, 2015.

[6]

J. T. Cacioppo, L. C. Hawkley, and R. A. Thisted. Perceived social isolation makes me sad: 5-year cross-lagged analyses of loneliness and depressive symptomatology in the Chicago health, aging, and social relations study. Psychology and aging, 25(2):453, 2010.

[7]

L. Canzian and M. Musolesi. Trajectories of depression: Unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis. In Proc. of ACM UbiComp, pages 1293--1304, 2015.

Digital Library

[8]

C.-C. Chang and C.-J. Lin. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2:27:1--27:27, 2011. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.

Digital Library

[9]

B. Chor, O. Goldreich, E. Kushilevitz, and M. Sudan. Private information retrieval. In Proc. of the 36th Annual Symposium on the Foundations of Computer Science (FOCS), pages 41--50, October 1995.

Digital Library

[10]

B. Chor, E. Kushilevitz, O. Goldreich, and M. Sudan. Private information retrieval. J. ACM, 45(6):965--981, 1998.

Digital Library

[11]

I. P. Chow, K. Fua, Y. Huang, W. Bonelli, H. Xiong, E. L. Barnes, and A. B. Teachman. Using mobile sensing to test clinical models of depression, social anxiety, state affect, and social isolation among college students. J Med Internet Res, 19(3):e62, Mar 2017.

[12]

P. Cuijpers and F. Smit. Excess mortality in depression: a meta-analysis of community studies. J Affect Disord, 72(3):227--236, December 2002.

[13]

H. Falaki and S. Keshav. Trace-based analysis of wifi scanning strategies. ACM SIGMOBILE Mobile Computing and Communications Review, 13(1):73--76, 2009.

Digital Library

[14]

R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin. LIBLINEAR: A library for large linear classification. The Journal of Machine Learning Research, 9:1871--1874, 2008.

Digital Library

[15]

A. A. Farhan, J. Lu, J. Bi, A. Russell, B. Wang, and A. Bamis. Multi-view bi-clustering to identify smartphone sensing features indicative of depression. In Proc. IEEE CHASE, June 2016.

[16]

A. A. Farhan, C. Yue, R. Morillo, S. Ware, J. Lu, J. Bi, J. Kamath, A. Russell, A. Bamis, and B. Wang. Behavior vs. introspection: Refining prediction of clinical depression via smartphone sensing data. In Proc. of Wireless Health, 2016.

[17]

M. Frost, A. Doryab, M. Faurholt-Jepsen, L. V. Kessing, and J. E. Bardram. Supporting disease insight through data analysis: refinements of the monarca self-assessment system. In Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing, pages 133--142. ACM, 2013.

Digital Library

[18]

C. Gentry and Z. Ramzan. Single-database private information retrieval with constant communication rate. In Proc. of The 32nd International Colloquium on Automata, Languages and Programming (ICALP), volume 3580, pages 803--815, 2005.

Digital Library

[19]

A. Gruenerbl, V. Osmani, G. Bahle, J. C. Carrasco, S. Oehler, O. Mayora, C. Haring, and P. Lukowicz. Using smart phone mobility traces for the diagnosis of depressive and manic episodes in bipolar patients. In Proceedings of the 5th Augmented Human International Conference, page 38. ACM, 2014.

Digital Library

[20]

A. Grünerbl, P. Oleksy, G. Bahle, C. Haring, J. Weppner, and P. Lukowicz. Towards smart phone based monitoring of bipolar disorder. In Proceedings of the Second ACM Workshop on Mobile Systems, Applications, and Services for HealthCare, page 3. ACM, 2012.

Digital Library

[21]

I. Guyon, J. Weston, S. Barnhill, and V. Vapnik. Gene selection for cancer classification using support vector machines. Machine learning, 46(1-3):389--422, 2002.

Digital Library

[22]

H. Hong, C. Luo, and M. C. Chan. Socialprobe: understanding social interaction through passive wifi monitoring. In Proceedings of the 13th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, pages 94--103. ACM, 2016.

Digital Library

[23]

W. Hsu, D. Dutta, and A. Helmy. Extended Abstract: Mining Behavioral Groups in Large Wireless LANs. In Proc. of ACM MobiCom, September 2007.

Digital Library

[24]

W. Hsu, D. Dutta, and A. Helmy. CSI: A Paradigm for Behavior-oriented Profile-cast Services in Mobile Networks. Ad Hoc Networks Journal, 10(8), November 2012.

Digital Library

[25]

S. S. Kanhere. Participatory sensing: Crowdsourcing data from mobile smartphones in urban spaces. In Mobile Data Management (MDM), 2011 12th IEEE International Conference on, volume 2, pages 3--6. IEEE, 2011.

Digital Library

[26]

W. Katon and P. Ciechanowski. Impact of major depression on chronic medical illness. J Psychosom Res, 53(4):859--863, October 2002.

[27]

K.-H. Kim, A. W. Min, D. Gupta, P. Mohapatra, and J. P. Singh. Improving energy efficiency of wifi sensing on smartphones. In INFOCOM, 2011 Proceedings IEEE, pages 2930--2938. IEEE, 2011.

[28]

K. Kroenke, R. L. Spitzer, and J. B. Williams. The PHQ-9. Journal of General Internal Medicine, 16(9):606--613, 2001.

[29]

J. Lu, C. Shang, C. Yue, R. Morillo, S. Ware, J. Kamath, A. Bamis, A. Russell, B. Wang, and J. Bi. Joint modeling of heterogeneous sensing data for depression assessment via multi-task learning. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2(1):21, 2018.

Digital Library

[30]

A. Mehrotra, R. Hendley, and M. Musolesi. Towards multi-modal anticipatory monitoring of depressive states through the analysis of human-smartphone interaction. In Proc. of UbiComp, 2016.

Digital Library

[31]

N. Palmius, A. Tsanas, K. E. A. Saunders, A. C. Bilderbeck, J. R. Geddes, G. M. Goodwin, and M. D. Vos. Detecting bipolar depression from geographic location data. IEEE Transactions on Biomedical Engineering, 64(8):1761--1771, 2017.

[32]

W. H. Press, S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery. Numerical recipes 3rd edition: The art of scientific computing. Cambridge university press, 2007.

Digital Library

[33]

M.-R. Ra, J. Paek, A. B. Sharma, R. Govindan, M. H. Krieger, and M. J. Neely. Energy-delay tradeoffs in smartphone applications. In Proceedings of the 8th international conference on Mobile systems, applications, and services, pages 255--270. ACM, 2010.

Digital Library

[34]

A. Rakotomamonjy. Variable selection using svm-based criteria. Journal of machine learning research, 3(Mar):1357--1370, 2003.

Digital Library

[35]

A.J. Rush, M. H. Trivedi, H. M. Ibrahim, T. J. Carmody, B. Arnow, D. N. Klein, J. C. Markowitz, P. T. Ninan, S. Kornstein, R. Manber, et al. The 16-item quick inventory of depressive symptomatology (QIDS), clinician rating (qids-c), and self-report (qids-sr): a psychometric evaluation in patients with chronic major depression. Biological psychiatry, 54(5):573--583, 2003.

[36]

S. Saeb, M. Zhang, C. J. Karr, S. M. Schueller, M. E. Corden, K. P. Kording, and D. C. Mohr. Mobile phone sensor correlates of depressive symptom severity in daily-life behavior: An exploratory study. Journal of Medical Internet Research, 17(7), 2015.

[37]

C. E. Sanders, T. M. Field, D. Miguel, and M. Kaplan. The relationship of Internet use to depression and social isolation among adolescents. Adolescence, 35(138):237, 2000.

[38]

G. Simon. Social and economic burden of mood disorders. Biol Psychiatry, 54(3):208--215, August 2003.

[39]

K. M. Smith, P. F. Renshaw, and J. Bilello. The diagnosis of depression: current and emerging methods. Comprehensive Psychiatry, 54(1):1--6, January 2013.

[40]

Y. Suhara, Y. Xu, and A. Pentland. Deepmood: Forecasting depressed mood based on self-reported histories via recurrent neural networks. In Proc.of WWW, 2017.

Digital Library

[41]

R. J. Turner and W. R. Avison. Status variations in stress exposure: Implications for the interpretation of research on race, socioeconomic status, and gender. Journal of Health and Social Behavior, pages 488--505, 2003.

[42]

T. Vos, A. D. Flaxman, M. Naghavi, R. Lozano, C. Michaud, M. Ezzati, K. Shibuya, J. A. Salomon, S. Abdalla, V. Aboyans, et al. Years lived with disability (ylds) for 1160 sequelae of 289 diseases and injuries 1990--2010: a systematic analysis for the global burden of disease study 2010. The lancet, 380(9859):2163--2196, 2012.

[43]

R. Wadhwa, A. Chugh, A. Kumar, M. Singh, K. Yadav, S. Eswaran, and T. Mukherjee. Sensex: Design and deployment of a pervasive wellness monitoring platform for workplaces. In International Conference on Service-Oriented Computing, pages 427--443. Springer, 2015.

[44]

R. Wang, M. S. H. Aung, S. Abdullah, R. Brian, A. T. Campbell, T. Choudhuryy, M. Hauserz, J. Kanez, M. Merrilly, E. A. Scherer, V. W. S. Tsengy, and D. Ben-Zeev. Crosscheck: Toward passive sensing and detection of mental health changes in people with schizophrenia. In Proc. of UbiComp, 2016.

Digital Library

[45]

R. Wang, F. Chen, Z. Chen, T. Li, G. Harari, S. Tignor, X. Zhou, D. Ben-Zeev, and A. T. Campbell. StudentLife: Assessing mental health, academic performance and behavioral trends of college students using smartphones. In Proc. of ACM Ubicomp, pages 3--14, 2014.

Digital Library

[46]

K. Yan and D. Zhang. Feature selection and analysis on correlated gas sensor data with recursive feature elimination. Sensors and Actuators B: Chemical, 212:353--363, 2015.

[47]

C. Yue, S. Ware, R. Morillo, J. Lu, C. Shang, J. Bi, A. Russell, A. Bamis, and B. Wang. Fusing location data for depression prediction. In Proc. IEEE Ubiquitous Intelligence and Computing, August 2017.

[48]

H. Zhang, Z. Yan, J. Yang, E. M. Tapia, and D. J. Crandall. Mfingerprint: Privacy-preserving user modeling with multimodal mobile device footprints. In International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction, pages 195--203. Springer, 2014.

[49]

D. Zhou, J. Luo, V. M. B. Silenzio, Y. Zhou, J. Hu, G. Currier, and H. A. Kautz. Tackling mental health by integrating unobtrusive multimodal sensing. In Proc. of AAAI, 2015.

Digital Library

Cited By

Ahmed MHasan TIslam SAhmed N(2024)Investigating Rhythmicity in App Usage to Predict Depressive Symptoms: Protocol for Personalized Framework Development and Validation Through a Countrywide StudyJMIR Research Protocols10.2196/5154013(e51540)Online publication date: 24-Apr-2024
https://doi.org/10.2196/51540
Mullick TShaaban SRadovic ADoryab A(2024)Framework for Ranking Machine Learning Predictions of Limited, Multimodal, and Longitudinal Behavioral Passive Sensing Data: Combining User-Agnostic and Personalized ModelingJMIR AI10.2196/478053(e47805)Online publication date: 20-May-2024
https://doi.org/10.2196/47805
Tlachac MHeinz MReisch MOgden S(2024)Symptom Detection with Text Message Log Distributions for Holistic Depression and Anxiety ScreeningProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435548:1(1-28)Online publication date: 6-Mar-2024
https://dl.acm.org/doi/10.1145/3643554
Show More Cited By

Index Terms

Large-scale Automatic Depression Screening Using Meta-data from WiFi Infrastructure
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
2. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile computing systems and tools

Recommendations

Joint Modeling of Heterogeneous Sensing Data for Depression Assessment via Multi-task Learning

Depression is a common mood disorder that causes severe medical problems and interferes negatively with daily life. Identifying human behavior patterns that are predictive or indicative of depressive disorder is important. Clinical diagnosis of ...
Forecasting Mood Using Smartphone and SNS Data
HotMobile '19: Proceedings of the 20th International Workshop on Mobile Computing Systems and Applications

This poster demonstrates a mood forecasting system that forecasts tomorrow's mood based on today's data collected from SNS and smartphone sensors. Forecasting user's mood plays an important role in diverse topics such as recommendation systems. We ...
Emotion detection for supporting depression screening
Abstract
Depression is the most prevalent mental disorder in the world. One of the most adopted tools for depression screening is the Beck Depression Inventory-II (BDI-II) questionnaire. Patients may minimize or exaggerate their answers. Thus, to further ...

Comments

Information & Contributors

Information

Published In

cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 2, Issue 4

December 2018

1169 pages

EISSN:2474-9567

DOI:10.1145/3301777

Issue’s Table of Contents

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 December 2018

Accepted: 01 October 2018

Revised: 01 August 2018

Received: 01 February 2018

Published in IMWUT Volume 2, Issue 4

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Funding Sources

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

27
Total Citations
View Citations
1,083
Total Downloads

Downloads (Last 12 months)123
Downloads (Last 6 weeks)19

Reflects downloads up to 13 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

Ahmed MHasan TIslam SAhmed N(2024)Investigating Rhythmicity in App Usage to Predict Depressive Symptoms: Protocol for Personalized Framework Development and Validation Through a Countrywide StudyJMIR Research Protocols10.2196/5154013(e51540)Online publication date: 24-Apr-2024
https://doi.org/10.2196/51540
Mullick TShaaban SRadovic ADoryab A(2024)Framework for Ranking Machine Learning Predictions of Limited, Multimodal, and Longitudinal Behavioral Passive Sensing Data: Combining User-Agnostic and Personalized ModelingJMIR AI10.2196/478053(e47805)Online publication date: 20-May-2024
https://doi.org/10.2196/47805
Tlachac MHeinz MReisch MOgden S(2024)Symptom Detection with Text Message Log Distributions for Holistic Depression and Anxiety ScreeningProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435548:1(1-28)Online publication date: 6-Mar-2024
https://dl.acm.org/doi/10.1145/3643554
Atrey AZakaria CBalan RShenoy P(2024)W4-Groups: Modeling the Who, What, When and Where of Group Behavior via Mobility SensingProceedings of the ACM on Human-Computer Interaction10.1145/36374278:CSCW1(1-29)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3637427
Bryan AHeinz MSalzhauer APrice GTlachac MJacobson N(2024)Behind the Screen: A Narrative Review on the Translational Capacity of Passive Sensing for Mental Health AssessmentBiomedical Materials & Devices10.1007/s44174-023-00150-42:2(778-810)Online publication date: 22-Feb-2024
https://doi.org/10.1007/s44174-023-00150-4
Liang KZhou AZhang ZZhou HMa HWu C(2023)mmStressProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36109267:3(1-36)Online publication date: 27-Sep-2023
https://dl.acm.org/doi/10.1145/3610926
Dai RKannampallil TKim SThornton VBierut LLu C(2023)Detecting Mental Disorders with Wearables: A Large Cohort StudyProceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation10.1145/3576842.3582389(39-51)Online publication date: 9-May-2023
https://dl.acm.org/doi/10.1145/3576842.3582389
Das Swain VKwon HSargolzaei SSaket BBin Morshed MTran KPatel DTian YPhilipose JCui YPlötz TDe Choudhury MAbowd G(2023)Leveraging WiFi network logs to infer student collocation and its relationship with academic performanceEPJ Data Science10.1140/epjds/s13688-023-00398-212:1Online publication date: 7-Jul-2023
https://doi.org/10.1140/epjds/s13688-023-00398-2
Tlachac MReisch MHeinz M(2023)Mobile Communication Log Time Series to Detect Depressive Symptoms2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)10.1109/EMBC40787.2023.10341154(1-4)Online publication date: 24-Jul-2023
https://doi.org/10.1109/EMBC40787.2023.10341154
Kamath JBarriera RJain NKeisari EWang B(2022)Digital phenotyping in depression diagnostics: Integrating psychiatric and engineering perspectivesWorld Journal of Psychiatry10.5498/wjp.v12.i3.39312:3(393-409)Online publication date: 19-Mar-2022
https://doi.org/10.5498/wjp.v12.i3.393
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

Media

Figures

Other

Tables

View Issue’s Table of Contents