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

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


Colleges, School and Institutes

External organisations

  • University College London


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
Publication statusPublished - 12 Sep 2016
EventMental Health: Sensing and Intervention Workshop 2016: at Ubicomp 2016 - Heidelberg, Germany
Duration: 13 Sep 201613 Sep 2016


WorkshopMental Health: Sensing and Intervention Workshop 2016


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