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

Abhinav Mehrotra, Robert Hendley, Mirco Musolesi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

36 Citations (Scopus)
266 Downloads (Pure)


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 languageEnglish
Title of host publicationUbiComp '16 Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing:
Subtitle of host publicationAdjunct
PublisherAssociation for Computing Machinery
Number of pages7
ISBN (Print)978-1-4503-4462-3
Publication statusPublished - 12 Sept 2016
EventMental Health: Sensing and Intervention Workshop 2016: at Ubicomp 2016 - Heidelberg, Germany
Duration: 13 Sept 201613 Sept 2016


WorkshopMental Health: Sensing and Intervention Workshop 2016


  • Mobile Sensing
  • Depression
  • Anticipatory Computing
  • Behaviour Change Interventions


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