论文标题
在高维空间中建模人类运动学习动力学
Towards Modeling Human Motor Learning Dynamics in High-Dimensional Spaces
论文作者
论文摘要
为上肢设计有效的康复策略，尤其是手和手指，值得需要人类运动学习的计算模型。这些系统中可用的很大程度的自由度（DOF）的存在使得在学习全部灵活性和完成操纵目标之间的权衡很难平衡。运动学习文献认为，人类使用运动协同作用来降低控制空间的维度。使用这些协同作用跨越的低维空间，我们基于内部模型控制理论开发了一个计算模型。我们根据其收敛性能分析了提出的模型，并将其拟合到从人类实验中收集的数据。我们将拟合模型的性能与实验数据进行比较，并表明它可以很好地捕获人类运动学习行为。
Designing effective rehabilitation strategies for upper extremities, particularly hands and fingers, warrants the need for a computational model of human motor learning. The presence of large degrees of freedom (DoFs) available in these systems makes it difficult to balance the trade-off between learning the full dexterity and accomplishing manipulation goals. The motor learning literature argues that humans use motor synergies to reduce the dimension of control space. Using the low-dimensional space spanned by these synergies, we develop a computational model based on the internal model theory of motor control. We analyze the proposed model in terms of its convergence properties and fit it to the data collected from human experiments. We compare the performance of the fitted model to the experimental data and show that it captures human motor learning behavior well.
