Accurate lower-limb pose estimation is aprerequisite of skeleton based pathological gait analysis. To achieve this goal in free-living environments for long-term monitoring, single depth sensor has been proposed in research. However, the depth map acquired from a single viewpoint encodes only partial geometric information of the lower limbs and exhibits large variations across different viewpoints. Existing off-the-shelf 3D pose tracking algorithms and public datasets for depth based human pose estimation are mainly targeted at activity recognition applications. They are relatively insensitive to skeleton estimation accuracy, especially at the foot segments. Furthermore, acquiring ground truth skeleton data for detailed biomechanics analysis also requires considerable efforts. To address these issues, we propose a novel cross-domain self-supervised complete geometric representation learning framework, with knowledge transfer from the unlabelled synthetic point clouds of full lower-limb surfaces. The proposed method can significantly reduce the number of ground truth skeletons (with only 1%) in the training phase, meanwhile ensuring accurate and precise pose estimation and capturing discriminative features across different pathological gait patterns compared to other methods.

中文翻译：

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基于真实扫描点云的病理步态分析的跨域自监督完整几何表示学习


准确的下肢姿势估计是基于骨骼的病理步态分析的先决条件。为了在自由生活环境中实现长期监测的目标，研究中提出了单一深度传感器。然而，从单个视点获取的深度图仅编码下肢的部分几何信息，并且在不同视点之间表现出较大的变化。现有的现成 3D 姿态跟踪算法和用于基于深度的人体姿态估计的公共数据集主要针对活动识别应用。它们对骨骼估计精度相对不敏感，尤其是在足部部分。此外，获取用于详细生物力学分析的地面真实骨架数据也需要相当大的努力。为了解决这些问题，我们提出了一种新颖的跨域自监督完整几何表示学习框架，其知识来自完整下肢表面的未标记合成点云。与其他方法相比，所提出的方法可以在训练阶段显着减少地面真实骨架的数量（仅1%），同时确保准确和精确的姿态估计并捕获不同病理步态模式的判别特征。