Uniqueness of gait kinematics in a cohort study

Abstract 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...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Gunwoo Park, Kyoung Min Lee, Seungbum Koo
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b5025984071b483b8f6f9cf42156f7d1
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:b5025984071b483b8f6f9cf42156f7d1
record_format	dspace
spelling	oai:doaj.org-article:b5025984071b483b8f6f9cf42156f7d12021-12-02T16:24:22ZUniqueness of gait kinematics in a cohort study10.1038/s41598-021-94815-z2045-2322https://doaj.org/article/b5025984071b483b8f6f9cf42156f7d12021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-94815-zhttps://doaj.org/toc/2045-2322Abstract 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.Gunwoo ParkKyoung Min LeeSeungbum KooNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Gunwoo Park Kyoung Min Lee Seungbum Koo Uniqueness of gait kinematics in a cohort study
description	Abstract 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	article
author	Gunwoo Park Kyoung Min Lee Seungbum Koo
author_facet	Gunwoo Park Kyoung Min Lee Seungbum Koo
author_sort	Gunwoo Park
title	Uniqueness of gait kinematics in a cohort study
title_short	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_sort	uniqueness of gait kinematics in a cohort study
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b5025984071b483b8f6f9cf42156f7d1
work_keys_str_mv	AT gunwoopark uniquenessofgaitkinematicsinacohortstudy AT kyoungminlee uniquenessofgaitkinematicsinacohortstudy AT seungbumkoo uniquenessofgaitkinematicsinacohortstudy
_version_	1718384132142137344

Uniqueness of gait kinematics in a cohort study

Ejemplares similares