MRI-Based Skeletal Hand Movement Model
MRI-Based Skeletal Hand Movement Model
复制标题
基于 MRI 的骨骼手部运动模型
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
Patrick van der Smagt
中科院分区：
文献类型：
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作者：
G. Stillfried;U. Hillenbrand;M. Settles;Patrick van der Smagt
在握力分配和力量抓取方面，人类手的运动学是最佳的，减少了通过相反的手指施加力时的组织应变，并优化了接触面。在构建仿生机械手时，量化这种最佳性是至关重要的，但了解人类手指的准确运动也是一项重要的资产，例如在操作过程中跟踪手指的运动。本文提出的方法的目的是通过使用合适的手在各种姿势下的磁共振成像(MRI)图像来确定手指关节旋转轴的精确方向和位置。从图像中分割骨骼，并相对于参考姿势估计它们的姿势。轴的方向和位置被数值拟合，以匹配测量的骨骼运动。针对每个关节研究了8种不同自由度的关节类型，并通过设置转动和平移平均偏差的限制来选择关节类型。该方法对不同精度和复杂程度的手部模型进行了建模，给出了3个自由度从22到33个自由度的算例。关节的活动范围与文献中的数据有一定的一致性，也有一些不一致之处。其中一个模型是作为免费OpenSim模拟环境的实现发布的。将从MRI数据建立的手模型的平均差异与由光学运动捕捉数据建立的手模型进行比较。
The kinematics of the human hand is optimal with respect to force distribution during pinch as well as power grasp, reducing the tissue strain when exerting forces through opposing fingers and optimising contact faces. Quantifying this optimality is of key importance when constructing biomimetic robotic hands, but understanding the exact human finger motion is also an important asset in, e.g. tracking finger movement during manipulation. The goal of the method presented here is to determine the precise orientations and positions of the axes of rotation of the finger joints by using suitable magnetic resonance imaging (MRI) images of a hand in various postures. The bones are segmented from the images, and their poses are estimated with respect to a reference posture. The axis orientations and positions are fitted numerically to match the measured bone motions. Eight joint types with varying degrees of freedom are investigated for each joint, and the joint type is selected by setting a limit on the rotational and translational mean discrepancy. The method results in hand models with differing accuracy and complexity, of which three examples, ranging from 22 to 33 DoF, are presented. The ranges of motion of the joints show some consensus and some disagreement with data from literature. One of the models is published as an implementation for the free OpenSim simulation environment. The mean discrepancies from a hand model built from MRI data are compared against a hand model built from optical motion capture data.