Corazza S, Mündermann L, Chaudhari A M, Demattio T, Cobelli C, Andriacchi T P
Department of Mechanical Engineering, Stanford University, 496 Lomita Mall, Durand B. 201, CA 94305-4038, USA.
Ann Biomed Eng. 2006 Jun;34(6):1019-29. doi: 10.1007/s10439-006-9122-8. Epub 2006 May 5.
Human motion capture is frequently used to study musculoskeletal biomechanics and clinical problems, as well as to provide realistic animation for the entertainment industry. The most popular technique for human motion capture uses markers placed on the skin, despite some important drawbacks including the impediment to the motion by the presence of skin markers and relative movement between the skin where the markers are placed and the underlying bone. The latter makes it difficult to estimate the motion of the underlying bone, which is the variable of interest for biomechanical and clinical applications. A model-based markerless motion capture system is presented in this study, which does not require the placement of any markers on the subject's body. The described method is based on visual hull reconstruction and an a priori model of the subject. A custom version of adapted fast simulated annealing has been developed to match the model to the visual hull. The tracking capability and a quantitative validation of the method were evaluated in a virtual environment for a complete gait cycle. The obtained mean errors, for an entire gait cycle, for knee and hip flexion are respectively 1.5 degrees (+/-3.9 degrees ) and 2.0 degrees (+/-3.0 degrees ), while for knee and hip adduction they are respectively 2.0 degrees (+/-2.3 degrees ) and 1.1 degrees (+/-1.7 degrees ). Results for the ankle and shoulder joints are also presented. Experimental results captured in a gait laboratory with a real subject are also shown to demonstrate the effectiveness and potential of the presented method in a clinical environment.
人体运动捕捉常用于研究肌肉骨骼生物力学和临床问题,以及为娱乐产业提供逼真的动画。尽管存在一些重要缺点,如皮肤标记物会阻碍运动以及标记物所在皮肤与深层骨骼之间的相对运动,但最流行的人体运动捕捉技术仍使用放置在皮肤上的标记物。后者使得难以估计深层骨骼的运动,而深层骨骼的运动是生物力学和临床应用中感兴趣的变量。本研究提出了一种基于模型的无标记运动捕捉系统,该系统无需在受试者身体上放置任何标记物。所描述的方法基于视觉外壳重建和受试者的先验模型。已开发出一种定制版的自适应快速模拟退火算法,以使模型与视觉外壳匹配。在虚拟环境中对一个完整步态周期评估了该方法的跟踪能力和定量验证。对于整个步态周期,膝关节和髋关节屈曲的平均误差分别为1.5度(±3.9度)和2.0度(±3.0度),而膝关节和髋关节内收的平均误差分别为2.0度(±2.3度)和1.1度(±1.7度)。还给出了踝关节和肩关节的结果。在步态实验室对真实受试者进行的实验结果也表明了所提出方法在临床环境中的有效性和潜力。