Explainable Depression Detection via Head Motion Patterns

Gahalawat, M., Fernandez Rojas, R., Guha, T. , Subramanian, R. and Goecke, R. (2023) Explainable Depression Detection via Head Motion Patterns. In: 25th ACM International Conference on Multimodal Interaction (ICMI 2023), Paris, France, 9-13 October 2023, pp. 261-270. ISBN 9798400700552 (doi: 10.1145/3577190.3614130)

Abstract

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.

• Item Type: Conference Proceedings
• Additional Information: This research is partially funded by the Australian Government through the Australian Research Council’s Discovery Projects funding scheme (project DP190101294). Monika Gahalawat is supported by a scholarship from the Faculty of Science & Technology (UC).
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Guha, Dr Tanaya and Subramanian, Dr Ramanathan
• Authors: Gahalawat, M., Fernandez Rojas, R., Guha, T., Subramanian, R., and Goecke, R.
• College/School: College of Science and Engineering > School of Computing Science
• ISBN: 9798400700552
• Copyright Holders: Copyright © 2023 The Authors
• First Published: First published in 25th ACM International Conference on Multimodal Interaction (ICMI 2023): 261-270
• Publisher Policy: Reproduced under a Creative Commons licence