Abstract
Markerless motion capture using computer vision and human pose estimation (HPE) has the potential to expand access to precise movement analysis. This could greatly benefit rehabilitation by enabling more accurate tracking of outcomes and providing more sensitive tools for research. There are numerous steps between obtaining videos to extracting accurate biomechanical results and limited research to guide many critical design decisions in these pipelines. In this work, we analyze several of these steps including the algorithm used to detect keypoints and the keypoint set, the approach to reconstructing trajectories for biomechanical inverse kinematics and optimizing the IK process. Several features we find important are: 1) using a recent algorithm trained on many datasets that produces a dense set of biomechanically-motivated keypoints, 2) using an implicit representation to reconstruct smooth, anatomically constrained marker trajectories for IK, 3) iteratively optimizing the biomechanical model to match the dense markers, 4) appropriate regularization of the IK process. Our pipeline makes it easy to obtain accurate biomechanical estimates of movement in a rehabilitation hospital.
Abstract (translated)
使用计算机视觉和人类姿态估计(HPE)进行无标记运动捕捉的潜在能力扩展了精确运动分析的访问。这将有助于康复,通过允许更准确地跟踪结果并提供更敏感的研究工具。在这项工作中,我们分析了几个步骤,包括用于检测关键点和关键点集的算法、用于重建生物医学逆运动学轨迹的方法,以及优化IK过程的方法。我们发现几个重要特征是:1)使用训练了许多数据集的最新算法,以生成一组生物医学动机的关键点的深度表示法;2)使用一种隐含表示来重建IK轨迹的平滑、具有身体解剖学约束的关键点标记法;3)迭代优化生物医学模型,使其与密集标记相匹配;4)适当规范IK过程。我们的管道使在康复医院获得准确的生物医学运动估计变得容易。
URL
https://arxiv.org/abs/2303.10654
