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Open-source Python software for analysis of 3D kinematics from quadrupedal animals

机译:开源Python软件,用于分析四足动物的3D运动学

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Locomotion is key to survival, yet it can be disturbed by injuries, diseases, and aging. Therefore, it is important for researchers in biology, neuroscience, biomechanics, and further afield. Locomotion is frequently quantified using kinematic data, and quadrupeds including rodents are medical model animals used worldwide. Kinematics typically result from the tracking of some landmarks, often joint centers. Although commercially available systems and software exist for the analysis of these kinematic data, they are expensive, often restricted to use with a given apparatus, and may be inextensible. Therefore, there is a need for an open source tool to analyze kinematic data. We present a Python software to address this need. It uses 2D coordinates from four cameras and DLT coefficients from the calibrated volume to generates 3D coordinates [1]. A method is presented to modify the knee and elbow joint positions in 3D. Then, kinematic features are extracted, and they are sorted in a time series format to plot a summary of a study. In addition, we generate videos from the tracked points, 3D reconstruction of the points, showing joint angles for eight joints, the location of animal on the belt, and the animal's speed on the belt. The software has been evaluated by eight trials to show the importance of the work. The 3D reconstruction error, having an average of 7.36 pixels, was calculated for the markers. The presented program can be used in different fields. It will encourage the researchers to design the studies based on their needs because they can change the setup in any required conditions while they can extract the kinematic data. (C) 2019 Elsevier Ltd. All rights reserved.
机译:运动是生存的关键,但可能会因受伤,疾病和衰老而受到干扰。因此,对于生物学,神经科学,生物力学以及更远的领域的研究人员来说很重要。运动经常通过运动学数据进行量化,包括啮齿类在内的四足动物是全世界使用的医学模型动物。运动学通常是由某些地标(通常是联合中心)的跟踪产生的。尽管存在用于分析这些运动学数据的可商购的系统和软件,但是它们很昂贵,通常受限于与给定设备一起使用,并且可能不可扩展。因此,需要一种开源工具来分析运动学数据。我们提供一种Python软件来满足这一需求。它使用来自四个摄像机的2D坐标和来自校准体积的DLT系数来生成3D坐标[1]。提出了一种在3D模式下修改膝盖和肘关节位置的方法。然后,提取运动学特征,并以时间序列格式对它们进行分类,以绘制研究摘要。此外,我们从跟踪的点生成视频,对这些点进行3D重建,显示八个关节的关节角度,皮带上动物的位置以及皮带上动物的速度。该软件已经过八次试用评估,以显示这项工作的重要性。计算出标记的3D重建误差平均为7.36像素。所提供的程序可以在不同领域中使用。这将鼓励研究人员根据需要设计研究,因为他们可以在任何需要的条件下更改设置,同时可以提取运动学数据。 (C)2019 Elsevier Ltd.保留所有权利。

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