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A Quantitative Assessment Grading Study of Balance Performance Based on Lower Limb Dataset
Balance ability is one of the important factors in measuring human physical fitness and a common index for evaluating sports performance. Its quality directly affects the coordination ability of human movements and plays an important role in human productive activities. In the field of sports, balan...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9824022/ https://www.ncbi.nlm.nih.gov/pubmed/36616632 http://dx.doi.org/10.3390/s23010033 |
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author | Wang, Fei Dong, Anqi Zhang, Kaiyu Qian, Dexing Tian, Yinsheng |
author_facet | Wang, Fei Dong, Anqi Zhang, Kaiyu Qian, Dexing Tian, Yinsheng |
author_sort | Wang, Fei |
collection | PubMed |
description | Balance ability is one of the important factors in measuring human physical fitness and a common index for evaluating sports performance. Its quality directly affects the coordination ability of human movements and plays an important role in human productive activities. In the field of sports, balance ability is an important indicator of athletes’ selection and training. How to objectively analyze balance performance becomes a problem for every non-professional sports enthusiast. Therefore, in this paper, we used a dataset of lower limb collected by inertial sensors to extract the feature parameters, then designed a RUS Boost classifier for unbalanced data whose basic classifier was SVM model to predict three classifications of balance degree, and, finally, evaluated the performance of the new classifier by comparing it with two basic classifiers (KNN, SVM). The result showed that the new classifier could be used to evaluate the balanced ability of lower limb, and performed higher than basic ones (RUS Boost: 72%; KNN: 60%; SVM: 44%). The results meant the established classification model could be used for and quantitative assessment of balance ability in initial screening and targeted training. |
format | Online Article Text |
id | pubmed-9824022 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-98240222023-01-08 A Quantitative Assessment Grading Study of Balance Performance Based on Lower Limb Dataset Wang, Fei Dong, Anqi Zhang, Kaiyu Qian, Dexing Tian, Yinsheng Sensors (Basel) Article Balance ability is one of the important factors in measuring human physical fitness and a common index for evaluating sports performance. Its quality directly affects the coordination ability of human movements and plays an important role in human productive activities. In the field of sports, balance ability is an important indicator of athletes’ selection and training. How to objectively analyze balance performance becomes a problem for every non-professional sports enthusiast. Therefore, in this paper, we used a dataset of lower limb collected by inertial sensors to extract the feature parameters, then designed a RUS Boost classifier for unbalanced data whose basic classifier was SVM model to predict three classifications of balance degree, and, finally, evaluated the performance of the new classifier by comparing it with two basic classifiers (KNN, SVM). The result showed that the new classifier could be used to evaluate the balanced ability of lower limb, and performed higher than basic ones (RUS Boost: 72%; KNN: 60%; SVM: 44%). The results meant the established classification model could be used for and quantitative assessment of balance ability in initial screening and targeted training. MDPI 2022-12-20 /pmc/articles/PMC9824022/ /pubmed/36616632 http://dx.doi.org/10.3390/s23010033 Text en © 2022 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 Wang, Fei Dong, Anqi Zhang, Kaiyu Qian, Dexing Tian, Yinsheng A Quantitative Assessment Grading Study of Balance Performance Based on Lower Limb Dataset |
title | A Quantitative Assessment Grading Study of Balance Performance Based on Lower Limb Dataset |
title_full | A Quantitative Assessment Grading Study of Balance Performance Based on Lower Limb Dataset |
title_fullStr | A Quantitative Assessment Grading Study of Balance Performance Based on Lower Limb Dataset |
title_full_unstemmed | A Quantitative Assessment Grading Study of Balance Performance Based on Lower Limb Dataset |
title_short | A Quantitative Assessment Grading Study of Balance Performance Based on Lower Limb Dataset |
title_sort | quantitative assessment grading study of balance performance based on lower limb dataset |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9824022/ https://www.ncbi.nlm.nih.gov/pubmed/36616632 http://dx.doi.org/10.3390/s23010033 |
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