Explainable Depression Detection using Multimodal Behavioural Cues

Abstract

Depression is a severe mental illness that not only affects the patient but also has major social and economical implications. Recent studies have employed artificial intelligence using multimodal behavioural cues to objectively investigate depression and alleviate the subjectivity involved in current depression diagnostic process. However, head motion has received a fairly limited attention as a behavioural marker for detecting depression and the lack of explainability of the "black box" approaches have restricted their widespread adoption. Consequently, the objective of this research is to examine the utility of fundamental head-motion units termed kinemes and explore the explainability of multimodal behavioural cues for depression detection. To this end, the research to date evaluated depression classification performance on the BlackDog and AVEC2013 datasets using multiple machine learning methods. Our findings indicate that: (a) head motion patterns are effective cues for depression assessment, and (b) explanatory kineme patterns can be observed for the two classes, consistent with prior research.