Quantitative model of transport-aperture coordination during reach-to-grasp movements.
Quantitative model of transport-aperture coordination during reach-to-grasp movements.
复制标题
抓握运动过程中传输-孔径协调的定量模型。
DOI：
10.1007/s00221-008-1361-5
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发表时间：
2008
影响因子：
2
通讯作者：
Stelmach,GeorgeE
中科院分区：
文献类型：
--
作者：
Rand,MiyaK;Shimansky,YP;Hossain,AbulBMI;Stelmach,GeorgeE
我们之前的研究发现，当手到目标的距离超过阈值时，在伸手抓握运动期间就会启动孔径关闭，该阈值是峰值孔径幅度、手速度和手加速度的函数。因此，在孔径关闭开始时观察到这四个运动参数之间的稳定关系。基于运动最优控制的概念（Naslin 1969）及其在抓取运动调节中的应用（Hoff 和 Arbib 1993），假设通过将孔径速度和加速度添加到上述四个运动参数，表达该关系的数学方程可以推广到描述整个抓取运动期间手部传输和手指孔径之间的协调。本研究检验了这一假设是否得到实验中获得的数据的支持，在这些实验中，年轻人在两种伸展幅度条件和四种运动速度条件的八种组合中进行了伸手抓握动作。研究发现，数学模型的线性近似描述了整个孔径闭合阶段的六个运动参数之间的关系，对于每个条件都具有非常高的精度，从而支持了该阶段的假设。测试一个数学模型是否可以在所有实验条件下逼近数据表明，仅通过在模型中包含两个额外的条件编码参数并使用基于非线性人工神经网络的逼近器（具有分别包含三个和两个神经元的两个隐藏层）就可以实现相同高水平的数据拟合精度。该结果表明，传输孔径协调作为手传输参数和手指孔径之间的特定关系，很大程度上取决于条件编码变量。光圈打开阶段的数据也符合线性模型，其系数与光圈关闭阶段确定的系数有很大不同。这一结果支持了上述关于光圈打开阶段的假设，因此也支持了整个抓取运动的假设。然而，拟合精度远低于孔径关闭阶段的拟合精度，表明孔径打开阶段传输孔径协调的试验间差异显着。讨论了理解中枢神经系统用于控制抓握运动的神经机制以及利用传输孔径协调的数学模型进行数据分析的意义。
It has been found in our previous studies that the initiation of aperture closure during reach-to-grasp movements occurs when the hand distance to target crosses a threshold that is a function of peak aperture amplitude, hand velocity, and hand acceleration. Thus, a stable relationship between those four movement parameters is observed at the moment of aperture closure initiation. Based on the concept of optimal control of movements (Naslin 1969) and its application for reach-to-grasp movement regulation (Hoff and Arbib 1993), it was hypothesized that the mathematical equation expressing that relationship can be generalized to describe coordination between hand transport and finger aperture during the entire reach-to-grasp movement by adding aperture velocity and acceleration to the above four movement parameters. The present study examines whether this hypothesis is supported by the data obtained in experiments in which young adults performed reach-to-grasp movements in eight combinations of two reach-amplitude conditions and four movement-speed conditions. It was found that linear approximation of the mathematical model described the relationship among the six movement parameters for the entire aperture-closure phase with very high precision for each condition, thus supporting the hypothesis for that phase. Testing whether one mathematical model could approximate the data across all the experimental conditions revealed that it was possible to achieve the same high level of data-fitting precision only by including in the model two additional, condition-encoding parameters and using a nonlinear, artificial neural network-based approximator with two hidden layers comprising three and two neurons, respectively. This result indicates that transport-aperture coordination, as a specific relationship between the parameters of hand transport and finger aperture, significantly depends on the condition-encoding variables. The data from the aperture-opening phase also fit a linear model, whose coefficients were substantially different from those identified for the aperture-closure phase. This result supports the above hypothesis for the aperture-opening phase, and consequently, for the entire reach-to-grasp movement. However, the fitting precision was considerably lower than that for the aperture-closure phase, indicating significant trial-to-trial variability of transport-aperture coordination during the aperture-opening phase. Implications for understanding the neural mechanisms employed by the CNS for controlling reach-to-grasp movements and utilization of the mathematical model of transport-aperture coordination for data analysis are discussed.