A novel approach to depression detection using POV glasses and machine learning for multimodal analysis

Major depressive disorder (MDD) remains challenging to diagnose due to its reliance on subjective interviews and self-reports. Objective, technology-driven methods are increasingly needed to support clinical decision-making. Wearable point-of-view (POV) glasses, which capture both visual and auditory streams, may offer a novel solution for multimodal behavioral analysis.

This study investigated whether features extracted from POV glasses, analyzed with machine learning, can differentiate individuals with MDD from healthy controls.

We studied 44 MDD patients and 41 age/sex-matched HCs (18-55 years). During semi-structured interviews, POV glasses recorded video and audio data. Visual features included gaze distribution, smiling duration, eye-blink frequency, and head movements. Speech features included response latency, silence ratio, and word count. Recursive feature elimination was applied. Multiple classifiers were evaluated, and the primary model-ExtraTrees-was assessed using leave-one-out cross-validation.

After Bonferroni correction, smiling duration, center gaze and happy face duration showed significant group differences. The multimodal classifier achieved an accuracy of 84.7%, sensitivity of 90.9%, specificity of 78%, and an F1 score of 86%.

POV glasses combined with machine learning successfully captured multimodal behavioral markers distinguishing MDD from controls. This low-burden, wearable approach demonstrates promise as an objective adjunct to psychiatric assessment. Future studies should evaluate its generalizability in larger, more diverse populations and real-world clinical settings.