Easy access to precise 3D tracking of movement could benefit many aspects of rehabilitation. A challenge to achieving this goal is that while there are many datasets and pretrained algorithms for able-bodied adults, algorithms trained on these datasets often fail to generalize to clinical populations including people with disabilities, infants, and neonates. Reliable movement analysis of infants and neonates is important as spontaneous movement behavior is an important indicator of neurological function and neurodevelopmental disability, which can help guide early interventions. We explored the application of dynamic Gaussian splatting to sparse markerless motion capture (MMC) data. Our approach leverages semantic segmentation masks to focus on the infant, significantly improving the initialization of the scene. Our results demonstrate the potential of this method in rendering novel views of scenes and tracking infant movements. This work paves the way for advanced movement analysis tools that can be applied to diverse clinical populations, with a particular emphasis on early detection in infants.
精确的3D运动跟踪易于获取，可以使康复的许多方面受益。实现这一目标的挑战在于，尽管有许多面向健全成人的数据集和预训练算法，但在这些数据集上训练的算法常常无法泛化到包括残疾人、婴儿和新生儿在内的临床人群。对婴儿和新生儿的可靠运动分析很重要，因为自发运动行为是神经功能和神经发育障碍的重要指标，有助于指导早期干预。我们探索了将动态高斯溅射应用于稀疏无标记运动捕捉（MMC）数据。我们的方法利用语义分割掩码专注于婴儿，显著改善了场景的初始化。我们的结果展示了这种方法在渲染场景的新视角和跟踪婴儿运动方面的潜力。这项工作为可以应用于不同临床人群的先进运动分析工具铺平了道路，特别强调了对婴儿的早期检测。