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Uniqueness of gait kinematics in a cohort study
Gait, the style of human walking, has been studied as a behavioral characteristic of an individual. Several studies have utilized gait to identify individuals with the aid of machine learning and computer vision techniques. However, there is a lack of studies on the nature of gait, such as the ident...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8316524/ https://www.ncbi.nlm.nih.gov/pubmed/34315974 http://dx.doi.org/10.1038/s41598-021-94815-z |
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author | Park, Gunwoo Lee, Kyoung Min Koo, Seungbum |
author_facet | Park, Gunwoo Lee, Kyoung Min Koo, Seungbum |
author_sort | Park, Gunwoo |
collection | PubMed |
description | Gait, the style of human walking, has been studied as a behavioral characteristic of an individual. Several studies have utilized gait to identify individuals with the aid of machine learning and computer vision techniques. However, there is a lack of studies on the nature of gait, such as the identification power or the uniqueness. This study aims to quantify the uniqueness of gait in a cohort. Three-dimensional full-body joint kinematics were obtained during normal walking trials from 488 subjects using a motion capture system. The joint angles of the gait cycle were converted into gait vectors. Four gait vectors were obtained from each subject, and all the gait vectors were pooled together. Two gait vectors were randomly selected from the pool and tested if they could be accurately classified if they were from the same person or not. The gait from the cohort was classified with an accuracy of 99.71% using the support vector machine with a radial basis function kernel as a classifier. Gait of a person is as unique as his/her facial motion and finger impedance, but not as unique as fingerprints. |
format | Online Article Text |
id | pubmed-8316524 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-83165242021-07-29 Uniqueness of gait kinematics in a cohort study Park, Gunwoo Lee, Kyoung Min Koo, Seungbum Sci Rep Article Gait, the style of human walking, has been studied as a behavioral characteristic of an individual. Several studies have utilized gait to identify individuals with the aid of machine learning and computer vision techniques. However, there is a lack of studies on the nature of gait, such as the identification power or the uniqueness. This study aims to quantify the uniqueness of gait in a cohort. Three-dimensional full-body joint kinematics were obtained during normal walking trials from 488 subjects using a motion capture system. The joint angles of the gait cycle were converted into gait vectors. Four gait vectors were obtained from each subject, and all the gait vectors were pooled together. Two gait vectors were randomly selected from the pool and tested if they could be accurately classified if they were from the same person or not. The gait from the cohort was classified with an accuracy of 99.71% using the support vector machine with a radial basis function kernel as a classifier. Gait of a person is as unique as his/her facial motion and finger impedance, but not as unique as fingerprints. Nature Publishing Group UK 2021-07-27 /pmc/articles/PMC8316524/ /pubmed/34315974 http://dx.doi.org/10.1038/s41598-021-94815-z Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Park, Gunwoo Lee, Kyoung Min Koo, Seungbum Uniqueness of gait kinematics in a cohort study |
title | Uniqueness of gait kinematics in a cohort study |
title_full | Uniqueness of gait kinematics in a cohort study |
title_fullStr | Uniqueness of gait kinematics in a cohort study |
title_full_unstemmed | Uniqueness of gait kinematics in a cohort study |
title_short | Uniqueness of gait kinematics in a cohort study |
title_sort | uniqueness of gait kinematics in a cohort study |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8316524/ https://www.ncbi.nlm.nih.gov/pubmed/34315974 http://dx.doi.org/10.1038/s41598-021-94815-z |
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