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PManalyzer: A Software Facilitating the Study of Sensorimotor Control of Whole-Body Movements
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, despi...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6461015/ https://www.ncbi.nlm.nih.gov/pubmed/31024286 http://dx.doi.org/10.3389/fninf.2019.00024 |
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author | Haid, Thomas H. Zago, Matteo Promsri, Arunee Doix, Aude-Clémence M. Federolf, Peter A. |
author_facet | Haid, Thomas H. Zago, Matteo Promsri, Arunee Doix, Aude-Clémence M. Federolf, Peter A. |
author_sort | Haid, Thomas H. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6461015 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-64610152019-04-25 PManalyzer: A Software Facilitating the Study of Sensorimotor Control of Whole-Body Movements Haid, Thomas H. Zago, Matteo Promsri, Arunee Doix, Aude-Clémence M. Federolf, Peter A. Front Neuroinform Neuroscience 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. Frontiers Media S.A. 2019-04-05 /pmc/articles/PMC6461015/ /pubmed/31024286 http://dx.doi.org/10.3389/fninf.2019.00024 Text en Copyright © 2019 Haid, Zago, Promsri, Doix and Federolf. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Haid, Thomas H. Zago, Matteo Promsri, Arunee Doix, Aude-Clémence M. Federolf, Peter A. PManalyzer: A Software Facilitating the Study of Sensorimotor Control of Whole-Body Movements |
title | PManalyzer: A Software Facilitating the Study of Sensorimotor Control of Whole-Body Movements |
title_full | PManalyzer: A Software Facilitating the Study of Sensorimotor Control of Whole-Body Movements |
title_fullStr | PManalyzer: A Software Facilitating the Study of Sensorimotor Control of Whole-Body Movements |
title_full_unstemmed | PManalyzer: A Software Facilitating the Study of Sensorimotor Control of Whole-Body Movements |
title_short | PManalyzer: A Software Facilitating the Study of Sensorimotor Control of Whole-Body Movements |
title_sort | pmanalyzer: a software facilitating the study of sensorimotor control of whole-body movements |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6461015/ https://www.ncbi.nlm.nih.gov/pubmed/31024286 http://dx.doi.org/10.3389/fninf.2019.00024 |
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