Explainable Depression Detection via Head Motion Patterns

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While depression has been studied via multimodal non-verbal behavioural cues, head motion behaviour has not received much attention as a biomarker. This study demonstrates the utility of fundamental head-motion units, termed kinemes, for depression detection by adopting two distinct approaches, and employing distinctive features: (a) discovering kinemes from head motion data corresponding to both depressed patients and healthy controls, and (b) learning kineme patterns only from healthy controls, and computing statistics derived from reconstruction errors for both the patient and control classes. Employing machine learning methods, we evaluate depression classification performance on the BlackDog and AVEC2013 datasets. Our findings indicate that: (1) head motion patterns are effective biomarkers for detecting depressive symptoms, and (2) explanatory kineme patterns consistent with prior findings can be observed for the two classes. Overall, we achieve peak F1 scores of 0.79 and 0.82, respectively, over BlackDog and AVEC2013 for binary classification over episodic thin-slices, and a peak F1 of 0.72 over videos for AVEC2013.
Original languageEnglish
Title of host publicationICMI 2023 - Proceedings of the 25th International Conference on Multimodal Interaction
EditorsElisabeth Andre, Mohamed Chetouani, Dominique Vaufreydaz, Gale Lucas, Tanja Schultz, Louis-Philippe Morency, Alessandro Vinciarelli
Place of PublicationUnited States
PublisherAssociation for Computing Machinery (ACM)
Number of pages10
ISBN (Electronic)9798400700552
ISBN (Print)9798400700552
Publication statusPublished - 9 Oct 2023
Event25th International Conference on Multimodal Interaction - Sorbonne University, Campus Pierre & Marie Curie, Paris, France
Duration: 9 Oct 202313 Oct 2023

Publication series

NameACM International Conference Proceeding Series


Conference25th International Conference on Multimodal Interaction
Abbreviated titleICMI '23
Internet address


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