Corazza Stefano, Mündermann Lars, Andriacchi Tom
Stanford University, Stanford, CA, USA.
J Biomech. 2007;40(15):3510-5. doi: 10.1016/j.jbiomech.2007.05.029. Epub 2007 Aug 13.
The objective of the study was to develop a framework for the accurate identification of joint centers to be used for the calculation of human body kinematics and kinetics. The present work introduces a method for the functional identification of joint centers using markerless motion capture (MMC). The MMC system used 8 color VGA cameras. An automatic segmentation-registration algorithm was developed to identify the optimal joint center in a least-square sense. The method was applied to the hip joint center with a validation study conducted in a virtual environment. The results had an accuracy (6mm mean absolute error) below the current MMC system resolution (1cm voxel resolution). Direct experimental comparison with marker-based methods was carried out showing mean absolute deviations over the three anatomical directions of 11.9 and 15.3mm if compared with either a full leg or only thigh markers protocol, respectively. Those experimental results were presented only in terms of deviations between the two systems (marker-based and markerless) as no real gold standard was available. The methods presented in this paper provide an important enabling step towards the biomechanical and clinical applications of markerless motion capture.
该研究的目的是开发一个用于准确识别关节中心的框架，以用于人体运动学和动力学的计算。目前的工作介绍了一种使用无标记运动捕捉（MMC）进行关节中心功能识别的方法。MMC系统使用了8台彩色VGA摄像机。开发了一种自动分割-配准算法，以在最小二乘意义上识别最佳关节中心。该方法应用于髋关节中心，并在虚拟环境中进行了验证研究。结果的精度（平均绝对误差6mm）低于当前MMC系统的分辨率（1cm体素分辨率）。与基于标记的方法进行了直接实验比较，结果表明，与全腿标记方案或仅大腿标记方案相比，在三个解剖方向上的平均绝对偏差分别为11.9mm和15.3mm。由于没有真正的金标准，这些实验结果仅以两种系统（基于标记和无标记）之间的偏差来呈现。本文提出的方法为无标记运动捕捉在生物力学和临床应用方面迈出了重要的一步。
