Towards Modeling Human Motor Learning Dynamics in High-Dimensional Spaces
Towards Modeling Human Motor Learning Dynamics in High-Dimensional Spaces
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DOI：
10.23919/acc53348.2022.9867377
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发表时间：
2022-02
期刊：
影响因子：
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通讯作者：
Ankur Kamboj;R. Ranganathan;Xiaobo Tan;Vaibhav Srivastava
中科院分区：
文献类型：
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作者：
Ankur Kamboj;R. Ranganathan;Xiaobo Tan;Vaibhav Srivastava
设计有效的上肢，特别是手和手指的康复策略，需要一个计算模型的人类运动学习。在这些系统中可用的大自由度（DoF）的存在使得难以平衡学习完全灵活性和实现操纵目标之间的权衡。运动学习文献认为，人类使用运动协同作用来减少控制空间的维度。使用这些协同作用所跨越的低维空间，我们开发了一个计算模型的基础上的内部模型理论的电机控制。我们分析了所提出的模型的收敛特性，并适合从人体实验收集的数据。我们将拟合模型的性能与实验数据进行了比较，结果表明它很好地捕捉了人类的运动学习行为。
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.