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Filtering Biomechanical Signals in Movement Analysis

Biomechanical analysis of human movement is based on dynamic measurements of reference points on the subject’s body and orientation measurements of body segments. Collected data include positions’ measurement, in a three-dimensional space. Signal enhancement by proper filtering is often recommended....

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Detalles Bibliográficos
Autores principales: Crenna, Francesco, Rossi, Giovanni Battista, Berardengo, Marta
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8271607/
https://www.ncbi.nlm.nih.gov/pubmed/34283131
http://dx.doi.org/10.3390/s21134580
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author Crenna, Francesco
Rossi, Giovanni Battista
Berardengo, Marta
author_facet Crenna, Francesco
Rossi, Giovanni Battista
Berardengo, Marta
author_sort Crenna, Francesco
collection PubMed
description Biomechanical analysis of human movement is based on dynamic measurements of reference points on the subject’s body and orientation measurements of body segments. Collected data include positions’ measurement, in a three-dimensional space. Signal enhancement by proper filtering is often recommended. Velocity and acceleration signal must be obtained from position/angular measurement records, needing numerical processing effort. In this paper, we propose a comparative filtering method study procedure, based on measurement uncertainty related parameters’ set, based upon simulated and experimental signals. The final aim is to propose guidelines to optimize dynamic biomechanical measurement, considering the measurement uncertainty contribution due to the processing method. Performance of the considered methods are examined and compared with an analytical signal, considering both stationary and transient conditions. Finally, four experimental test cases are evaluated at best filtering conditions for measurement uncertainty contributions.
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spelling pubmed-82716072021-07-11 Filtering Biomechanical Signals in Movement Analysis Crenna, Francesco Rossi, Giovanni Battista Berardengo, Marta Sensors (Basel) Article Biomechanical analysis of human movement is based on dynamic measurements of reference points on the subject’s body and orientation measurements of body segments. Collected data include positions’ measurement, in a three-dimensional space. Signal enhancement by proper filtering is often recommended. Velocity and acceleration signal must be obtained from position/angular measurement records, needing numerical processing effort. In this paper, we propose a comparative filtering method study procedure, based on measurement uncertainty related parameters’ set, based upon simulated and experimental signals. The final aim is to propose guidelines to optimize dynamic biomechanical measurement, considering the measurement uncertainty contribution due to the processing method. Performance of the considered methods are examined and compared with an analytical signal, considering both stationary and transient conditions. Finally, four experimental test cases are evaluated at best filtering conditions for measurement uncertainty contributions. MDPI 2021-07-04 /pmc/articles/PMC8271607/ /pubmed/34283131 http://dx.doi.org/10.3390/s21134580 Text en © 2021 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
Crenna, Francesco
Rossi, Giovanni Battista
Berardengo, Marta
Filtering Biomechanical Signals in Movement Analysis
title Filtering Biomechanical Signals in Movement Analysis
title_full Filtering Biomechanical Signals in Movement Analysis
title_fullStr Filtering Biomechanical Signals in Movement Analysis
title_full_unstemmed Filtering Biomechanical Signals in Movement Analysis
title_short Filtering Biomechanical Signals in Movement Analysis
title_sort filtering biomechanical signals in movement analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8271607/
https://www.ncbi.nlm.nih.gov/pubmed/34283131
http://dx.doi.org/10.3390/s21134580
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