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Comparison of kinematic and dynamic sensor modalities and derived features for human motion segmentation

机译:运动和动态传感器模式的比较和人类运动分割的衍生特征

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Human motion segmentation aims to extract individual motion repetitions from a continuous stream of data, typically using a single sensor modality. However, with the numerous sensor modalities available for motion measurement, it can be difficult to determine which modality is the most suitable. This paper investigates how segmentation accuracy is affected by the choice of sensing modality. Motion capture joint position, kinematic, force plate ground reaction force, centre of pressure, and joint torque features were considered, and their segmentation accuracy compared using classifier-based segmentation. It was found that joint position, joint angle, and ground reaction force produced similar accuracy values at 96%. These results suggest that raw motion capture and force plate sensor data can provide comparable accuracy to joint angles, reducing the need for computationally expensive inverse kinematic/dynamic computation and difficult parameter estimation.
机译:人类运动分割旨在从连续的数据流中提取单独的运动重复,通常使用单个传感器模态。然而,对于可用于运动测量的许多传感器方式,可能难以确定哪种方式最合适。本文调查了分割准确性如何受到感知方式的选择。运动捕获接头位置,运动,力板接地反作用力,压力中心和关节扭矩特征,以及使用基于分类的分类的分割精度。发现接合位置,关节角度和地反作用力产生的精度值为96%。这些结果表明,原始运动捕获和力板传感器数据可以为关节角度提供可比的精度,从而减少对计算昂贵的逆运动/动态计算和困难参数估计的需求。

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