  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.
