【2h】

Hammering Does Not Fit Fitts Law

机译:锤击不符合菲茨定律

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摘要

While movement is essential to human wellbeing, we are still unable to reproduce the deftness and robustness of human movement in automatons or completely restore function to individuals with many types of motor impairment. To better understand how the human nervous system plans and controls movements, neuromechanists employ simple tasks such as upper extremity reaches and isometric force tasks. However, these simple tasks rarely consider impacts and may not capture aspects of motor control that arise from real-world complexity. Here we compared existing models of motor control with the results of a periodic targeted impact task extended from Bernstein's seminal work: hammering a nail into wood. We recorded impact forces and kinematics from 10 subjects hammering at different frequencies and with hammers with different physical properties (mass and face area). We found few statistical differences in most measures between different types of hammer, demonstrating human robustness to minor changes in dynamics. Because human motor control is thought to obey optimality principles, we also developed a feedforward optimal simulation with a neuromechanically inspired cost function that reproduces the experimental data. However, Fitts' Law, which relates movement time to distance traveled and target size, did not match our experimental data. We therefore propose a new model in which the distance moved is a logarithmic function of the time to move that yields better results (R2 ≥ 0.99 compared to R2 ≥ 0.88). These results support the argument that humans control movement in an optimal way, but suggest that Fitts' Law may not generalize to periodic impact tasks.
机译:尽管运动对于人类的福祉至关重要,但我们仍然无法在自动机上再现人类运动的敏捷性和健壮性,也无法完全恢复患有多种运动障碍的人的功能。为了更好地了解人的神经系统如何计划和控制运动,神经机械师采用简单的任务,例如上肢伸直和等距力任务。但是,这些简单的任务很少考虑影响,并且可能无法捕获现实世界中复杂性引起的电机控制方面。在这里,我们将现有的电机控制模型与从伯恩斯坦的开创性工作(即钉子钉入木头)中扩展出来的定期有针对性的冲击任务的结果进行了比较。我们记录了来自10位受试者的冲击力和运动学信息,这些受试者以不同的频率锤击并且使用具有不同物理特性(质量和表面积)的锤子。我们发现,在不同类型的锤子之间的大多数测量中,统计上的差异很小,这表明人的鲁棒性对动力的微小变化。由于认为人类运动控制遵循最优性原则,因此我们还开发了前馈最优模拟,该模拟具有神经力学启发的成本函数,可再现实验数据。但是,将运动时间与行进距离和目标大小相关联的菲茨定律与我们的实验数据不符。因此,我们提出了一个新模型,其中移动距离是移动时间的对数函数,可产生更好的结果(R 2 ≥0.99,而R 2 ≥0.88) 。这些结果支持以下论点:人类以最佳方式控制运动,但表明菲茨定律可能无法推广到周期性影响任务。

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