A Non-Invasive 3D Gait Analysis Framework for Quantifying Psychomotor Retardation in Major Depressive Disorder

2026-01-27

AI Summary

This study addresses the lack of objective, non-invasive methods for quantifying psychomotor retardation (PMR) in major depressive disorder (MDD), a condition often assessed subjectively in clinical settings or via costly 3D motion capture systems. To overcome these limitations, the authors propose a monocular RGB video–based approach for 3D gait reconstruction, integrating a gravity-aware viewpoint coordinate system and a closed-loop trajectory correction mechanism. Coupled with an adapted Timed Up-and-Go (TUG) protocol, the method extracts 297 gait-related biomechanical markers from ordinary video footage. Validated on the CALYPSO dataset, this framework achieves 83.3% accuracy in PMR detection and explains 64% of the variance in depression severity (R² = 0.64). It further identifies reduced ankle propulsion and restricted pelvic mobility as key biomechanical correlates, supporting bodily movement as a reliable proxy for cognitive state in depression.

Technology Category

• Computer Vision: Motion & Tracking
• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Intelligent Robots: State Estimation

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Predicting the status of Major Depressive Disorder (MDD) from objective, non-invasive methods is an active research field. Yet, extracting automatically objective, interpretable features for a detailed analysis of the patient state remains largely unexplored. Among MDD's symptoms, Psychomotor retardation (PMR) is a core item, yet its clinical assessment remains largely subjective. While 3D motion capture offers an objective alternative, its reliance on specialized hardware often precludes routine clinical use. In this paper, we propose a non-invasive computational framework that transforms monocular RGB video into clinically relevant 3D gait kinematics. Our pipeline uses Gravity-View Coordinates along with a novel trajectory-correction algorithm that leverages the closed-loop topology of our adapted Timed Up and Go (TUG) protocol to mitigate monocular depth errors. This novel pipeline enables the extraction of 297 explicit gait biomechanical biomarkers from a single camera capture. To address the challenges of small clinical datasets, we introduce a stability-based machine learning framework that identifies robust motor signatures while preventing overfitting. Validated on the CALYPSO dataset, our method achieves an 83.3% accuracy in detecting PMR and explains 64% of the variance in overall depression severity (R^2=0.64). Notably, our study reveals a strong link between reduced ankle propulsion and restricted pelvic mobility to the depressive motor phenotype. These results demonstrate that physical movement serves as a robust proxy for the cognitive state, offering a transparent and scalable tool for the objective monitoring of depression in standard clinical environments.

Problem

• Major Depressive Disorder
• Psychomotor Retardation
• 3D Gait Analysis
• Non-invasive Assessment
• Objective Biomarkers

Innovation

• monocular 3D gait analysis
• psychomotor retardation
• trajectory-correction algorithm
• stability-based machine learning
• biomechanical biomarkers