用手机视频分析慢性疼痛患者动作，实现实验室级精准测量。
Quantitative Movement Testing: Measuring Chronic Pain Patient Movements from a Single Smartphone Video
- 基于深度学习的单目摄像头3D姿态估计，从普通手机视频提取运动数据。
- 与金标准设备相比，相关性超0.85，误差低，测试重测信度>0.86。
- 适合临床试验中远程追踪疼痛患者治疗效果，尤其适用于居家场景。
慢性疼痛会降低生活质量，但其功能影响在真实世界中难以客观评估。虽然光学运动捕捉可高精度测量运动质量，但成本高且仅限于实验室。我们开发并验证了定量运动检测（QMT）——一种利用深度学习3D姿态估计从普通单目手机视频中提取三维运动生物标志物的计算机视觉流程，兼顾临床可及性与生物力学准确性。在健康对照组（N=13）中，通过留一被试者校准后，QMT与金标准光学运动捕捉表现出高度一致性，相关系数r > 0.85，平均绝对误差低。将QMT应用于两个前瞻性临床队列：纤维肌痛患者的干预前后试验，以及慢性坐骨神经痛患者和健康对照的30天居家纵向监测。实验室验证中，QMT提取的临床运动指标与金标准高度一致；在纤维肌痛患者中显示高重测信度（r > 0.86），并成功捕捉慢性坐骨神经痛患者的日间运动波动。尽管居家环境引入更高测量方差，但仅凭远程记录即能区分健康人与坐骨神经痛患者的群体差异。单目3D姿态估计为传统评估提供可扩展替代方案。QMT为临床试验中疾病进展与治疗反应提供了客观、可及的生物标志物，但需进一步研究以优化居家环境下的可靠性。
原文摘要 · Abstract (English)
Chronic pain diminishes quality of life by decreasing functional ability, yet objectively measuring this functional impact remains challenging in real-world settings. While optical motion capture provides high precision for assessing altered movement quality, it is costly and restricted to laboratory environments. We aimed to develop and validate Quantitative Movement Testing (QMT), a computer vision pipeline extracting 3D kinematic biomarkers from standard monocular smartphone video, balancing clinical accessibility with biomechanical accuracy. We validated the QMT pipeline, utilising deep learning-based 3D pose-estimation, against gold-standard optical motion capture in healthy controls (N=13). Following leave-one-subject-out calibration to correct systematic bias, we deployed QMT in two prospective clinical cohorts to assess real-world utility: a pre- and post-intervention trial for fibromyalgia patients, and a 30-day longitudinal at-home monitoring study of chronic sciatica patients and healthy controls. In laboratory validation, QMT extracted clinical kinematic metrics with high agreement to optical motion capture, yielding strong correlations (r > 0.85) and low mean absolute errors. QMT demonstrated high test-retest reliability (r > 0.86) in fibromyalgia patients and successfully tracked day-to-day movement fluctuations in chronic sciatica. While real-world home settings introduced higher measurement variance than lab settings, QMT found group-level differences between healthy controls and sciatica patients based entirely on remote recordings. Monocular 3D pose estimation offers a scalable alternative to traditional assessments. QMT provides an objective, accessible biomarker for tracking disease progression and treatment response in clinical trials, though further research is needed to optimise reliability in home environments.
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