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PManalyzer:一款助力全身运动感觉运动控制研究的软件。

PManalyzer: A Software Facilitating the Study of Sensorimotor Control of Whole-Body Movements.

作者信息

Haid Thomas H, Zago Matteo, Promsri Arunee, Doix Aude-Clémence M, Federolf Peter A

机构信息

Department of Sport Science, University of Innsbruck, Innsbruck, Austria.

Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.

出版信息

Front Neuroinform. 2019 Apr 5;13:24. doi: 10.3389/fninf.2019.00024. eCollection 2019.

Abstract

Motion analysis is used to study the functionality or dysfunctionality of the neuromuscular system, as human movements are the direct outcome of neuromuscular control. However, motion analysis often relies on measures that quantify simplified aspects of a motion, such as specific joint angles, despite the well-known complexity of segment interactions. In contrast, analyzing whole-body movement patterns may offer a new understanding of movement coordination and movement performance. Clinical research and sports technique evaluations suggest that principal component analysis (PCA) provides novel and valuable insights into control aspects of the neuromuscular system and how they relate to coordinative patterns. However, the implementation of PCA computations are time consuming, and require mathematical knowledge and programming skills, drastically limiting its application in current research. Therefore, the aim of this study is to present the Matlab software tool "PManalyzer" to facilitate and encourage the application of state-of-the-art PCA concepts in human movement science. The generalized PCA concepts implemented in the PManalyzer allow users to apply a variety of marker set independent PCA-variables on any kinematic data and to visualize the results with customizable plots. In addition, the extracted movement patterns can be explored with video options that may help testing hypotheses related to the interplay of segments. Furthermore, the software can be easily modified and adapted to any specific application.

摘要

运动分析用于研究神经肌肉系统的功能或功能障碍,因为人体运动是神经肌肉控制的直接结果。然而,运动分析通常依赖于量化运动简化方面的测量方法,例如特定的关节角度,尽管已知节段间相互作用非常复杂。相比之下,分析全身运动模式可能会为运动协调和运动表现提供新的理解。临床研究和运动技术评估表明,主成分分析(PCA)能为神经肌肉系统的控制方面及其与协调模式的关系提供新颖且有价值的见解。然而,PCA计算的实施耗时,且需要数学知识和编程技能,这极大地限制了其在当前研究中的应用。因此,本研究的目的是展示Matlab软件工具“PManalyzer”,以促进和鼓励在人体运动科学中应用最先进的PCA概念。PManalyzer中实现的广义PCA概念允许用户在任何运动学数据上应用各种与标记集无关的PCA变量,并通过可定制的绘图来可视化结果。此外,可以通过视频选项探索提取的运动模式,这可能有助于检验与节段相互作用相关的假设。此外,该软件可以很容易地修改并适用于任何特定应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c5b5/6461015/cd80c5286482/fninf-13-00024-g0001.jpg

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