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对肩膀和上肢的解剖学标志进行三维无标记姿势估计

F Lefebvre¹, I Rogowski², N Long³

¹Univ Lyon, Université Claude Bernard Lyon 1, Laboratoire Interuniversitaire de Biologie de la Motricité, EA 7424, F-69622 Villeurbanne, France; TRINOMA, Villefort, France.

Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology

|September 13, 2025

概括

研究人员开发了一种无标记的3D姿势估计算法,使用神经网络准确识别肩膀和上肢的解剖学地标,显示出临床使用的前景.

科学领域:

生物医学工程生物医学工程
计算机视觉计算机视觉
人类解剖学人类解剖学

背景情况:

准确的3D姿势对解剖学里程碑的估计对于生物力学分析和临床评估至关重要.
基于标记器的运动捕捉系统可能是侵入性的和耗时的.
无标记技术提供了一个非侵入性的替代方案,但需要强大的算法用于解剖学地标识别.

研究的目的:

开发和验证一个3D无标记的姿势估计算法,用于肩膀和上肢的解剖标志.
在健康参与者中评估开发算法的准确性和可靠性.
为了评估算法在各种静态位置和手臂高度的性能.

主要方法:

一个卷积神经网络 (ResNet-50) 通过使用26个健康参与者的2612个无标记图像在21个静态位置进行了微调.
基于标记器 (触摸) 的图像作为训练和验证的基础真理.
该算法估计了20个解剖学地标的3D姿势,使用基于标记器的数据对欧几里德距离进行准确性评估.

主要成果:

该算法在大多数解剖学地标上实现了15mm以下的欧几里德中位距离和30mm以下的第90百分位距离.
第八个胸椎,肩的下角和中部表骨显示出最高的欧几里德距离.
对于大多数地标,预测坐标的丢失率低于6%,在测试的移动和手臂升高方面保持一致的准确性.

结论:

成功开发了一种经过验证的3D无标记姿势估计算法,用于肩膀和上肢解剖学地标.
该算法在生物力学和康复领域的潜在临床应用中显示出有希望的准确性.
对于具有更高错误率的特定地标,可能需要进一步精细化.

关键词:

手臂手臂手臂手臂手臂手臂在DeepLabCut中使用了DeepLabCut.动力学就是运动学.神经网络的神经网络头骨是一个头骨.

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