In this study we compare the performance of available generic- and infant-pose estimators for a video-based automated general movement assessment (GMA), and the choice of viewing angle for optimal recordings, i.e., conventional diagonal view used in GMA vs. top-down view. We used 4500 annotated video-frames from 75 recordings of infant spontaneous motor functions from 4 to 26 weeks. To determine which pose estimation method and camera angle yield the best pose estimation accuracy on infants in a GMA related setting, the distance to human annotations and the percentage of correct key-points (PCK) were computed and compared. The results show that the best performing generic model trained on adults, ViTPose, also performs best on infants. We see no improvement from using infant-pose estimators over the generic pose estimators on our infant dataset. However, when retraining a generic model on our data, there is a significant improvement in pose estimation accuracy. The pose estimation accuracy obtained from the top-down view is significantly better than that obtained from the diagonal view, especially for the detection of the hip key-points. The results also indicate limited generalization capabilities of infant-pose estimators to other infant datasets, which hints that one should be careful when choosing infant pose estimators and using them on infant datasets which they were not trained on. While the standard GMA method uses a diagonal view for assessment, pose estimation accuracy significantly improves using a top-down view. This suggests that a top-down view should be included in recording setups for automated GMA research.

翻译：本研究比较了现有通用姿态估计器与婴儿专用姿态估计器在基于视频的自动化全身运动评估（GMA）中的性能，并探讨了最优拍摄视角的选择，即GMA中常规使用的对角线视角与俯视视角。我们使用了来自75次婴儿自发运动功能记录的4500帧标注视频帧，这些记录覆盖了婴儿4至26周龄阶段。为确定在GMA相关场景下何种姿态估计方法和摄像头角度能获得最佳的婴儿姿态估计精度，我们计算并比较了与人工标注的距离以及关键点正确率（PCK）。结果表明，在成人数据上训练的最佳通用模型ViTPose在婴儿数据上同样表现最优。在我们的婴儿数据集上，使用婴儿专用姿态估计器相比通用姿态估计器未见性能提升。然而，当使用我们的数据对通用模型进行重新训练时，姿态估计精度得到显著改善。俯视视角获得的姿态估计精度显著优于对角线视角，尤其在髋部关键点检测方面。结果还表明婴儿专用姿态估计器对其他婴儿数据集的泛化能力有限，这提示在选择婴儿姿态估计器并将其应用于未经训练过的婴儿数据集时需谨慎。虽然标准GMA方法采用对角线视角进行评估，但使用俯视视角可显著提升姿态估计精度。这表明在自动化GMA研究的记录设置中应考虑纳入俯视视角。