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タイトル: | 3D Kinematic Analysis for the Functional Evaluation in the Rat Model of Sciatic Nerve Crush Injury |
著者: | Wang, Tianshu Ito, Akira ![]() ![]() ![]() Tajino, Junichi Kuroki, Hiroshi ![]() Aoyama, Tomoki ![]() ![]() ![]() |
著者名の別形: | 王, 天舒 伊藤, 明良 太治野, 純一 黒木, 裕士 青山, 朋樹 |
キーワード: | Neuroscience Issue 156 rat sciatic nerve injury three-dimensional kinematic analysis functional evaluation center of gravity |
発行日: | Feb-2020 |
出版者: | MyJove Corporation |
誌名: | Journal of Visualized Experiments : JoVE |
巻: | 156 |
論文番号: | e60267 |
抄録: | Compared to the Sciatic Functional Index (SFI), kinematic analysis is a more reliable and sensitive method for performing functional evaluations of sciatic nerve injury rodent models. In this protocol, we describe a novel kinematic analysis method that uses a three-dimensional (3D) motion capture apparatus for functional evaluations using a rat sciatic nerve crush injury model. First, the rat is familiarized with treadmill walking. Markers are then attached to the designated bone landmarks and the rat is made to walk on the treadmill at the desired speed. Meanwhile, the posterior limb movements of the rat are recorded using four cameras. Depending on the software used, marker tracings are created using both automatic and manual modes and the desired data are produced after subtle adjustments. This method of kinematic analysis, which uses a 3D motion capture apparatus, offers numerous advantages, including superior precision and accuracy. Many more parameters can be investigated during the comprehensive functional evaluations. This method has several shortcomings that require consideration: The system is expensive, can be complicated to operate, and may produce data deviations due to skin shifting. Nevertheless, kinematic analysis using a 3D motion capture apparatus is useful for performing functional anterior and posterior limb evaluations. In the future, this method may become increasingly useful for generating accurate assessments of various traumas and diseases. |
著作権等: | Copyright © 2020 Journal of Visualized Experiments |
URI: | http://hdl.handle.net/2433/252815 |
DOI(出版社版): | 10.3791/60267 |
PubMed ID: | 32116292 |
出現コレクション: | 学術雑誌掲載論文等 |

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