Isolating the Unique and Generic Movement Characteristics of Highly Trained Runners

Human movement patterns were shown to be as unique to individuals as their fingerprints. However, some movement characteristics are more important than other characteristics for machine learning algorithms to distinguish between individuals. Here, we explored the idea that movement patterns contain...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Fabian Hoitz, Laura Fraeulin, Vinzenz von Tscharner, Daniela Ohlendorf, Benno M. Nigg, Christian Maurer-Grubinger
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	running triathlon movement pattern human recognition artificial neural network layer-wise relevance propagation Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/8974b7b857e94e829772cd3fe03661d7
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:8974b7b857e94e829772cd3fe03661d7
record_format	dspace
spelling	oai:doaj.org-article:8974b7b857e94e829772cd3fe03661d72021-11-11T19:08:29ZIsolating the Unique and Generic Movement Characteristics of Highly Trained Runners10.3390/s212171451424-8220https://doaj.org/article/8974b7b857e94e829772cd3fe03661d72021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7145https://doaj.org/toc/1424-8220Human movement patterns were shown to be as unique to individuals as their fingerprints. However, some movement characteristics are more important than other characteristics for machine learning algorithms to distinguish between individuals. Here, we explored the idea that movement patterns contain unique characteristics that differentiate between individuals and generic characteristics that do not differentiate between individuals. Layer-wise relevance propagation was applied to an artificial neural network that was trained to recognize 20 male triathletes based on their respective movement patterns to derive characteristics of high/low importance for human recognition. The similarity between movement patterns that were defined exclusively through characteristics of high/low importance was then evaluated for all participants in a pairwise fashion. We found that movement patterns of triathletes overlapped minimally when they were defined by variables that were very important for a neural network to distinguish between individuals. The movement patterns overlapped substantially when defined through less important characteristics. We concluded that the unique movement characteristics of elite runners were predominantly sagittal plane movements of the spine and lower extremities during mid-stance and mid-swing, while the generic movement characteristics were sagittal plane movements of the spine during early and late stance.Fabian HoitzLaura FraeulinVinzenz von TscharnerDaniela OhlendorfBenno M. NiggChristian Maurer-GrubingerMDPI AGarticlerunningtriathlonmovement patternhuman recognitionartificial neural networklayer-wise relevance propagationChemical technologyTP1-1185ENSensors, Vol 21, Iss 7145, p 7145 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	running triathlon movement pattern human recognition artificial neural network layer-wise relevance propagation Chemical technology TP1-1185
spellingShingle	running triathlon movement pattern human recognition artificial neural network layer-wise relevance propagation Chemical technology TP1-1185 Fabian Hoitz Laura Fraeulin Vinzenz von Tscharner Daniela Ohlendorf Benno M. Nigg Christian Maurer-Grubinger Isolating the Unique and Generic Movement Characteristics of Highly Trained Runners
description	Human movement patterns were shown to be as unique to individuals as their fingerprints. However, some movement characteristics are more important than other characteristics for machine learning algorithms to distinguish between individuals. Here, we explored the idea that movement patterns contain unique characteristics that differentiate between individuals and generic characteristics that do not differentiate between individuals. Layer-wise relevance propagation was applied to an artificial neural network that was trained to recognize 20 male triathletes based on their respective movement patterns to derive characteristics of high/low importance for human recognition. The similarity between movement patterns that were defined exclusively through characteristics of high/low importance was then evaluated for all participants in a pairwise fashion. We found that movement patterns of triathletes overlapped minimally when they were defined by variables that were very important for a neural network to distinguish between individuals. The movement patterns overlapped substantially when defined through less important characteristics. We concluded that the unique movement characteristics of elite runners were predominantly sagittal plane movements of the spine and lower extremities during mid-stance and mid-swing, while the generic movement characteristics were sagittal plane movements of the spine during early and late stance.
format	article
author	Fabian Hoitz Laura Fraeulin Vinzenz von Tscharner Daniela Ohlendorf Benno M. Nigg Christian Maurer-Grubinger
author_facet	Fabian Hoitz Laura Fraeulin Vinzenz von Tscharner Daniela Ohlendorf Benno M. Nigg Christian Maurer-Grubinger
author_sort	Fabian Hoitz
title	Isolating the Unique and Generic Movement Characteristics of Highly Trained Runners
title_short	Isolating the Unique and Generic Movement Characteristics of Highly Trained Runners
title_full	Isolating the Unique and Generic Movement Characteristics of Highly Trained Runners
title_fullStr	Isolating the Unique and Generic Movement Characteristics of Highly Trained Runners
title_full_unstemmed	Isolating the Unique and Generic Movement Characteristics of Highly Trained Runners
title_sort	isolating the unique and generic movement characteristics of highly trained runners
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/8974b7b857e94e829772cd3fe03661d7
work_keys_str_mv	AT fabianhoitz isolatingtheuniqueandgenericmovementcharacteristicsofhighlytrainedrunners AT laurafraeulin isolatingtheuniqueandgenericmovementcharacteristicsofhighlytrainedrunners AT vinzenzvontscharner isolatingtheuniqueandgenericmovementcharacteristicsofhighlytrainedrunners AT danielaohlendorf isolatingtheuniqueandgenericmovementcharacteristicsofhighlytrainedrunners AT bennomnigg isolatingtheuniqueandgenericmovementcharacteristicsofhighlytrainedrunners AT christianmaurergrubinger isolatingtheuniqueandgenericmovementcharacteristicsofhighlytrainedrunners
_version_	1718431596484231168

Isolating the Unique and Generic Movement Characteristics of Highly Trained Runners

Ejemplares similares