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The Measure of Motion Similarity for Robotics Application †

A new measure of motion similarity has been proposed. The formulation of this measure is presented and its logical basis is described. Unlike in most of other methods, the measure enables easy determination of the instantaneous synergies of the motion of body parts. To demonstrate how to use the mea...

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Autores principales: Zielinska, Teresa, Coba, Gabriel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9919200/
https://www.ncbi.nlm.nih.gov/pubmed/36772683
http://dx.doi.org/10.3390/s23031643
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author Zielinska, Teresa
Coba, Gabriel
author_facet Zielinska, Teresa
Coba, Gabriel
author_sort Zielinska, Teresa
collection PubMed
description A new measure of motion similarity has been proposed. The formulation of this measure is presented and its logical basis is described. Unlike in most of other methods, the measure enables easy determination of the instantaneous synergies of the motion of body parts. To demonstrate how to use the measure, the data describing human movement is used. The movement is recorded using a professional motion capture system. Two different cases of non-periodic movements are discussed: stepping forward and backward, and returning to a stable posture after an unexpected thrust to the side (hands free or tied). This choice enables the identification of synergies in slow dynamics (stepping) and in fast dynamics (push recovery). The trajectories of motion similarity measures are obtained for point masses of the human body. The interpretation of these trajectories in relation to motion events is discussed. In addition, ordinary motion trajectories and footprints are shown in order to better illustrate the specificity of the discussed examples. The article ends with a discussion and conclusions.
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spelling pubmed-99192002023-02-12 The Measure of Motion Similarity for Robotics Application † Zielinska, Teresa Coba, Gabriel Sensors (Basel) Article A new measure of motion similarity has been proposed. The formulation of this measure is presented and its logical basis is described. Unlike in most of other methods, the measure enables easy determination of the instantaneous synergies of the motion of body parts. To demonstrate how to use the measure, the data describing human movement is used. The movement is recorded using a professional motion capture system. Two different cases of non-periodic movements are discussed: stepping forward and backward, and returning to a stable posture after an unexpected thrust to the side (hands free or tied). This choice enables the identification of synergies in slow dynamics (stepping) and in fast dynamics (push recovery). The trajectories of motion similarity measures are obtained for point masses of the human body. The interpretation of these trajectories in relation to motion events is discussed. In addition, ordinary motion trajectories and footprints are shown in order to better illustrate the specificity of the discussed examples. The article ends with a discussion and conclusions. MDPI 2023-02-02 /pmc/articles/PMC9919200/ /pubmed/36772683 http://dx.doi.org/10.3390/s23031643 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zielinska, Teresa
Coba, Gabriel
The Measure of Motion Similarity for Robotics Application †
title The Measure of Motion Similarity for Robotics Application †
title_full The Measure of Motion Similarity for Robotics Application †
title_fullStr The Measure of Motion Similarity for Robotics Application †
title_full_unstemmed The Measure of Motion Similarity for Robotics Application †
title_short The Measure of Motion Similarity for Robotics Application †
title_sort measure of motion similarity for robotics application †
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9919200/
https://www.ncbi.nlm.nih.gov/pubmed/36772683
http://dx.doi.org/10.3390/s23031643
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