Takeda Misaki, Nambu Isao, Wada Yasuhiro
Graduate School of Engineering, Nagaoka University of Technology, Nagaoka, Niigata 940-2188, Japan.
Brain Sci. 2021 Jan 23;11(2):149. doi: 10.3390/brainsci11020149.
A computational trajectory formation model based on the optimization principle, which introduces the forward inverse relaxation model (FIRM) as the hardware and algorithm, represents the features of human arm movements well. However, in this model, the movement duration was defined as a given value and not as a planned value. According to considerable empirical facts, movement duration changes depending on task factors, such as required accuracy and movement distance thus, it is considered that there are some criteria that optimize the cost function. Therefore, we propose a FIRM that incorporates a movement duration optimization module. The movement duration optimization module minimizes the weighted sum of the commanded torque change term as the trajectory cost, and the tolerance term as the cost of time. We conducted a behavioral experiment to examine how well the movement duration obtained by the model reproduces the true movement. The results suggested that the model movement duration was close to the true movement. In addition, the trajectory generated by inputting the obtained movement duration to the FIRM reproduced the features of the actual trajectory well. These findings verify the use of this computational model in measuring human arm movements.
一种基于优化原理的计算轨迹形成模型，该模型引入前向逆松弛模型（FIRM）作为硬件和算法，能够很好地呈现人类手臂运动的特征。然而，在该模型中，运动持续时间被定义为一个给定值，而非计划值。根据大量经验事实，运动持续时间会根据任务因素而变化，如所需精度和运动距离，因此，可以认为存在一些优化成本函数的标准。所以，我们提出了一种包含运动持续时间优化模块的FIRM。运动持续时间优化模块将作为轨迹成本的指令扭矩变化项的加权和以及作为时间成本的容差项最小化。我们进行了一项行为实验，以检验模型得到的运动持续时间能在多大程度上再现真实运动。结果表明，模型运动持续时间接近真实运动。此外，将获得的运动持续时间输入到FIRM中生成的轨迹很好地再现了实际轨迹的特征。这些发现验证了该计算模型在测量人类手臂运动方面的应用。
