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A new method to evaluate order and accuracy of inaccurately and incompletely reproduced movement sequences

机译:一种评估不正确和不完全复制的运动序列的顺序和准确性的新方法

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摘要

Studying imitation learning of long sequences requires the evaluation of inaccurately and incompletely reproduced movement sequences. In order to evaluate the movement reproduction, it has to be assigned to the original stimulus. We developed an assignment algorithm that considers the Spatial Neighborhood and Order of reproduction (SNOA). To evaluate the features of this analysis it was applied to human performance during learning of long pointing sequences under two conditions: stimulus-guided reproduction with high spatial accuracy and imitation learning with low spatial accuracy. The results were compared with a simple assignment considering Spatial Neighborhood only (SNA) and with a Manual Assignment (MA). In the stimulus-guided reproduction the error measures did not differ between the algorithms. In contrast, with imitation learning, SNOA and MA generated higher estimates of order and omission errors than SNA. The results show that SNOA can be used to automatically quantify the similarity of both movement structure and metric information between long target sequences and inaccurate and incomplete movement reproductions.
机译:研究长序列的模仿学习需要评估不准确和不完全再现的运动序列。为了评估运动再现,必须将其分配给原始刺激。我们开发了一种分配算法,该算法考虑了空间邻域和再现顺序(SNOA)。为了评估此分析的功能,将其应用于在以下两种条件下学习长指向序列的过程中的人类表现:具有高空间精度的刺激引导复制和具有低空间精度的模仿学习。将结果与仅考虑空间邻域(SNA)的简单分配和手动分配(MA)进行比较。在刺激引导的复制中,误差度量在算法之间没有差异。相比之下,通过模仿学习,SNOA和MA生成的订单和遗漏误差估计值比SNA高。结果表明,SNOA可用于自动量化长目标序列以及不准确和不完整的运动复制之间的运动结构和度量信息的相似性。

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