Multimodal assessment of depression from behavioral signals

Jeffrey Cohn, Nicholas Cummins, Julien Epps, Roland Goecke, Jyoti Dhall, Stefan Scherer

Research output: A Conference proceeding or a Chapter in BookChapterpeer-review


Reliable, valid, and efficient assessment of depression is critical to identify individuals in need of treatment and to gauge treatment response. Current methods of assessment are limited to subjective measures of patient self-report and clinical interview. They fail to take into account observable measures of behavior that could better inform detection of the occurrence and severity of depression. Recent advances in computer vision, signal processing, and machine learning have potential to meet the need for improved depression screening, diagnosis, and ascertainment of severity (i.e., assessment). This chapter reviews these advances. We describe multimodal measures of behavior and physiology, how these measures can be processed to extract features sensitive to depression, and how classification or prediction may be used to provide automatic assessment of depression occurrence and severity.
Original languageEnglish
Title of host publicationThe Handbook of Multimodal-Multisensor Interfaces
Subtitle of host publicationSignal Processing, Architectures, and Detection of Emotion and Cognition
EditorsSharon Oviatt, Bjoern Schuller, Philip R. Cohen, Daniel Sonntag, Gerasimos Potamianos, Antonio Krueger
Place of PublicationUnited States
PublisherAssociation for Computing Machinery (ACM)
Number of pages43
ISBN (Print)9781970001716
Publication statusPublished - Oct 2018


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