从参数化人体模型旋转矩阵中直接提取临床关节角度
Direct Clinical Joint Angle Extraction from Parametric Body Model Rotation Matrices
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中文总结 AI 辅助
研究提出直接从参数化人体模型旋转矩阵提取临床关节角度的方法，无需复杂步骤，通过小校准表实现。在OpenCap LabValidation队列等测试中精度良好，拓宽了运动分析应用范围，且无需额外输入，流程简单可移植。
中文摘要 AI 辅助
在常规护理中，由于工具缓慢、成本高或限于实验室，定量关节角度很少可用。我们表明，临床关节角度可以直接从参数化人体模型已经生成的逐段旋转矩阵中读取，无需逆运动学或肌肉骨骼模型拟合步骤。在OpenCap LabValidation队列中，使用GEM-X人体模型估计器处理单智能手机视频，在与OpenCap单目参考集匹配的15个关节角度上，我们的合并平均绝对误差为4.50度，与OpenCap单目在同一队列和参考标准上的4.8度处于相同精度范围，且流程更简单。连接人体模型与临床角度的步骤是一个小校准表而非优化，该程序可原样转移到其他人体模型。该方法除视频本身外无需每次记录输入。这拓宽了运动分析的实际应用范围。
英文摘要
Quantitative joint angles are rarely available in routine care because the tools are slow, costly, or confined to a laboratory. We show that clinical joint angles can be read directly from the per-segment rotation matrices a parametric body model already produces, with no inverse-kinematics or musculoskeletal-model fitting step. On the OpenCap LabValidation cohort, using the GEM-X body-model estimator on single-smartphone video, our pooled mean absolute error is 4.50 degrees over the fifteen joint angles that match the OpenCap Monocular reference set, the same accuracy range as OpenCap Monocular's 4.8 degrees on the same cohort and reference standard, from a much simpler pipeline. The step that connects a body model to clinical angles is a small calibration table rather than an optimisation, so the same procedure transfers unchanged to other body models: repeating it on SAM 3D Body, changing only the table, gives 4.66 degrees, statistically indistinguishable from GEM-X, and runs in real time from a live single-camera stream. The method needs no per-recording inputs beyond the video itself: no participant height, no camera-intrinsics database, no per-subject model scaling. This broadens where movement analysis is practical, from in-clinic and at-home recording to telerehabilitation and large-scale decentralised studies.
发表机构
- Babon Innovations B.V.（巴邦创新有限公司）
- HU University of Applied Sciences Utrecht（乌得勒支应用科学大学）
机构由 AI 辅助整理，请以论文原文为准。
