基于体积运动捕捉的运动学运动分析

Ying Zhu, Cameron Detig, Steven Kane, Gary Lourie
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摘要

运动学运动分析广泛应用于卫生保健、运动医学、机器人、生物力学、运动科学等领域。动作捕捉系统对于动作分析是必不可少的。有三种类型的动作捕捉系统:基于标记的捕捉,基于视觉的捕捉和体积捕捉。基于标记的动作捕捉系统可以获得相当精确的结果,但将标记附加到身体上既不方便又耗时。基于视觉的无标记运动捕捉系统由于其非侵入性和灵活性而更受欢迎。体积捕捉是一种更新、更先进的无标记动作捕捉系统,可以重建现实的、全身的、动画的3D角色模型。但是,由于体积捕获技术提出了新的挑战,因此很少用于运动分析。我们提出了一种利用体积捕获数据进行运动学运动分析的新方法。该方法由三级管道组成。首先,通过体积捕获系统捕获运动。然后使用迭代最近点(ICP)算法处理体积捕获数据,以生成跟踪运动的虚拟标记。第三,将运动跟踪数据导入生物力学分析工具OpenSim进行运动学运动分析。我们的运动分析方法使用户能够将数值运动分析应用于OpenSim中的骨骼模型,同时还可以从不同角度研究全身动画3D模型。它有潜力为医疗保健、运动科学和生物力学等领域提供更详细和深入的运动分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Kinematic Motion Analysis with Volumetric Motion Capture
Kinematic motion analysis is widely used in health-care, sports medicine, robotics, biomechanics, sports science, etc. Motion capture systems are essential for motion analysis. There are three types of motion capture systems: marker-based capture, vision-based capture, and volumetric capture. Marker-based motion capture systems can achieve fairly accurate results but attaching markers to a body is inconvenient and time-consuming. Vision-based, marker-less motion capture systems are more desirable because of their non-intrusiveness and flexibility. Volumetric capture is a newer and more advanced marker-less motion capture system that can reconstruct realistic, full-body, animated 3D character models. But volumetric capture has rarely been used for motion analysis because volumetric motion data presents new challenges. We propose a new method for conducting kinematic motion analysis using volumetric capture data. This method consists of a three-stage pipeline. First, the motion is captured by a volumetric capture system. Then the volumetric capture data is processed using the Iterative Closest Points (ICP) algorithm to generate virtual markers that track the motion. Third, the motion tracking data is imported into the biomechanical analysis tool OpenSim for kinematic motion analysis. Our motion analysis method enables users to apply numerical motion analysis to the skeleton model in OpenSim while also studying the full-body, animated 3D model from different angles. It has the potential to provide more detailed and in-depth motion analysis for areas such as healthcare, sports science, and biomechanics.
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