为人体运动捕捉提供个体化置信区间，提升临床应用可靠性
Biomechanical Reconstruction with Confidence Intervals from Multiview Markerless Motion Capture
- 用可微分生物力学模型学习动作后验分布，生成个体化置信区间
- 虚拟标记空间误差在10-15毫米内，关节角度误差仅几度，远端关节略宽
- 能识别高不确定性的时刻和试验，适合临床与大规模研究使用
多视角无标记运动捕捉（MMMC）技术有望在临床与科研中实现高质量运动分析。尽管以往验证研究表明其平均性能良好，但临床与大规模应用仍缺乏针对特定个体、特定相机配置下各运动学参数的置信区间。本文基于前序工作，采用端到端优化的隐式轨迹表示，通过可微分生物力学模型学习给定检测关键点后的姿态后验分布。该后验分布通过变分近似学习，可对每个时间点的单个关节估计置信区间：虚拟标记的空间误差通常在10-15毫米内，关节角度误差一般仅几度，远端关节稍大。后验分布还建模了关节角度间的相关性（如髋与骨盆角的相关性）。该方法可识别运动不确定性较高的时刻与试验，为临床决策提供量化可信度。
原文摘要 · Abstract (English)
Advances in multiview markerless motion capture (MMMC) promise high-quality movement analysis for clinical practice and research. While prior validation studies show MMMC performs well on average, they do not provide what is needed in clinical practice or for large-scale utilization of MMMC -- confidence intervals over specific kinematic estimates from a specific individual analyzed using a possibly unique camera configuration. We extend our previous work using an implicit representation of trajectories optimized end-to-end through a differentiable biomechanical model to learn the posterior probability distribution over pose given all the detected keypoints. This posterior probability is learned through a variational approximation and estimates confidence intervals for individual joints at each moment in a trial, showing confidence intervals generally within 10-15 mm of spatial error for virtual marker locations, consistent with our prior validation studies. Confidence intervals over joint angles are typically only a few degrees and widen for more distal joints. The posterior also models the correlation structure over joint angles, such as correlations between hip and pelvis angles. The confidence intervals estimated through this method allow us to identify times and trials where kinematic uncertainty is high.
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