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

机译:运动学和动态传感器模态以及人体运动分割的派生特征的比较

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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:人体运动分割的目的通常是使用单个传感器模式从连续的数据流中提取单个运动重复。但是,由于有众多可用于运动测量的传感器模式,可能很难确定哪种模式最合适。本文研究了分割方式如何影响传感方式的选择。考虑了运动捕获关节位置,运动学,力板地面反作用力,压力中心和关节扭矩特征,并使用基于分类器的分割方法比较了它们的分割精度。发现关节位置,关节角度和地面反作用力在96%处产生相似的精度值。这些结果表明,原始运动捕获和力板传感器数据可以提供与关节角度相当的精度,从而减少了对计算上昂贵的逆运动/动态计算和困难的参数估计的需求。

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