# Explainable Depression Detection via Head Motion Patterns

arXiv: https://arxiv.org/abs/2307.12241 · HTML: https://mlsanity.com/paper/2307.12241/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 47 references that are in the map.
- First posted on arXiv 23 Jul 2023.

## Builds on (most cited first, 4 of 4)

- [AVEC 2019 Workshop and Challenge: State-of-Mind, Detecting Depression with AI, and Cross-Cultural Affect Recognition](https://mlsanity.com/paper/1907.11510/) (2019)
- [MFCC-based Recurrent Neural Network for Automatic Clinical Depression Recognition and Assessment from Speech](https://mlsanity.com/paper/1909.07208/) (2019)
- [Head Matters: Explainable Human-centered Trait Prediction from Head Motion Dynamics](https://mlsanity.com/paper/2112.08068/) (2021)
- [AVEID: Automatic Video System for Measuring Engagement In Dementia](https://mlsanity.com/paper/1712.08084/) (2017)

## Built on by (most cited first, 5 of 5)

- [Automatic Depression Assessment using Machine Learning: A Comprehensive Survey](https://mlsanity.com/paper/2506.18915/) (2025)
- [Measuring Anxiety Levels with Head Motion Patterns in Severe Depression Population](https://mlsanity.com/paper/2502.08813/) (2025)
- [On the Validity of Head Motion Patterns as Generalisable Depression Biomarkers](https://mlsanity.com/paper/2505.23427/) (2025)
- [Cognitive State Inference from VR Motion via Motion Foundation Model](https://mlsanity.com/paper/2509.24255/) (2025)
- [Depression Markers in Speech: An Approach based on Tract Variables Dynamics](https://mlsanity.com/paper/2607.25888/) (2026)

## Abstract (arXiv)

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.