为辅助技术建立人体膝关节模型。
Modeling the human knee for assistive technologies.
机构信息
Institute of Biomedical Engineering, National Research Council, 35127 Padova, Italy.
出版信息
IEEE Trans Biomed Eng. 2012 Sep;59(9):2642-9. doi: 10.1109/TBME.2012.2208746.
Abstract
In this paper, we use motion capture technology together with an EMG-driven musculoskeletal model of the knee joint to predict muscle behavior during human dynamic movements. We propose a muscle model based on infinitely stiff tendons and show this allows speeding up 250 times the computation of muscle force and the resulting joint moment calculation with no loss of accuracy with respect to the previously developed elastic-tendon model. We then integrate our previously developed method for the estimation of 3-D musculotendon kinematics in the proposed EMG-driven model. This new code enabled the creation of a standalone EMG-driven model that was implemented and run on an embedded system for applications in assistive technologies such as myoelectrically controlled prostheses and orthoses.
摘要
在本文中，我们使用运动捕捉技术和膝关节的肌电驱动骨骼肌肉模型来预测人体动态运动中的肌肉行为。我们提出了一种基于无限刚性肌腱的肌肉模型，并表明这可以将肌肉力计算和关节力矩计算的计算速度提高 250 倍，而不会损失与之前开发的弹性肌腱模型相比的准确性。然后，我们将之前开发的用于估计三维肌肉肌腱运动学的方法集成到所提出的肌电驱动模型中。这个新的代码使创建一个独立的肌电驱动模型成为可能，该模型已经在嵌入式系统上实现和运行，可用于辅助技术，如肌电控制假肢和矫形器。
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