Explainable Depression Detection via Head Motion Patterns

TL;DR

This paper explores the use of fundamental head-motion units called kinemes as biomarkers for depression detection, demonstrating their effectiveness through machine learning on two datasets and achieving high classification scores.

Contribution

It introduces the novel concept of kinemes for depression detection and compares two approaches for utilizing head motion data in machine learning models.

Findings

Head motion patterns are effective depression biomarkers.

Explanatory kineme patterns align with prior research.

Achieved peak F1 scores of 0.79 and 0.82 on two datasets.

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 \emph{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 \emph{BlackDog} and \emph{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 \emph{thin-slices}, and a peak F1 of 0.72 over videos for AVEC2013.