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.

翻译：通过客观、非侵入性方法预测重度抑郁症（MDD）状态是一个活跃的研究领域。然而，如何自动提取客观、可解释的特征以对患者状态进行详细分析，在很大程度上仍未得到充分探索。在MDD的症状中，精神运动迟缓（PMR）是一个核心表现，但其临床评估仍主要依赖主观判断。虽然三维运动捕捉技术提供了客观的替代方案，但其对专业硬件的依赖往往限制了常规临床应用。本文提出一种非侵入性计算框架，可将单目RGB视频转化为具有临床意义的三维步态运动学数据。我们的流程采用重力-视图坐标系，并结合一种新颖的轨迹校正算法——该算法利用我们改进的计时起立行走测试（TUG）协议的闭环拓扑结构来缓解单目深度误差。这一创新流程使得仅通过单摄像头采集即可提取297个明确的步态生物力学生物标志物。为应对临床数据集规模较小的挑战，我们引入了一种基于稳定性的机器学习框架，该框架能够识别稳健的运动特征并防止过拟合。在CALYPSO数据集上的验证结果表明，我们的方法在检测PMR方面达到83.3%的准确率，并能解释总体抑郁严重程度64%的方差（R^2=0.64）。值得注意的是，我们的研究揭示了踝关节推进力减弱与骨盆活动受限与抑郁运动表型之间的密切关联。这些结果表明，躯体运动可作为认知状态的稳健代理指标，为标准临床环境中抑郁的客观监测提供了透明且可扩展的工具。