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.

翻译：慢性疼痛通过降低功能性能力来损害生活质量，然而，在现实环境中客观测量这种功能性影响仍具有挑战性。虽然光学运动捕捉在评估运动质量改变方面提供了高精度，但其成本高昂且局限于实验室环境。我们旨在开发并验证定量运动测试（QMT），这是一种从标准单目智能手机视频中提取三维运动学生物标志物的计算机视觉流程，在临床可及性与生物力学精度之间取得平衡。我们利用基于深度学习的3D姿态估计技术，以健康对照组（N=13）中的金标准光学运动捕捉为基准验证了QMT流程。在通过留一法受试者校准校正系统偏差后，我们将QMT部署到两个前瞻性临床队列中以评估其实用性：纤维肌痛患者的干预前后试验，以及慢性坐骨神经痛患者与健康对照组的30天纵向居家监测研究。在实验室验证中，QMT提取的临床运动学指标与光学运动捕捉具有高度一致性，表现出强相关性（r > 0.85）和低平均绝对误差。QMT在纤维肌痛患者中显示出高重测信度（r > 0.86），并成功追踪了慢性坐骨神经痛患者的日常运动波动。尽管现实居家环境引入了比实验室环境更高的测量变异，但QMT完全基于远程记录发现了健康对照组与坐骨神经痛患者之间的群体水平差异。单目3D姿态估计为传统评估提供了一种可扩展的替代方案。QMT为临床试验中追踪疾病进展和治疗反应提供了一种客观、可及的生物标志物，但需进一步研究以优化其在居家环境中的可靠性。