Cargando…
The Gaitprint: Identifying Individuals by Their Running Style
Recognizing the characteristics of a well-developed running style is a central issue in athletic sub-disciplines. The development of portable micro-electro-mechanical-system (MEMS) sensors within the last decades has made it possible to accurately quantify movements. This paper introduces an analysi...
Autores principales: | , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7412195/ https://www.ncbi.nlm.nih.gov/pubmed/32650424 http://dx.doi.org/10.3390/s20143810 |
_version_ | 1783568551821443072 |
---|---|
author | Weich, Christian M. Vieten, Manfred |
author_facet | Weich, Christian M. Vieten, Manfred |
author_sort | Weich, Christian |
collection | PubMed |
description | Recognizing the characteristics of a well-developed running style is a central issue in athletic sub-disciplines. The development of portable micro-electro-mechanical-system (MEMS) sensors within the last decades has made it possible to accurately quantify movements. This paper introduces an analysis method, based on limit-cycle attractors, to identify subjects by their specific running style. The movement data of 30 athletes were collected over 20 min. in three running sessions to create an individual gaitprint. A recognition algorithm was applied to identify each single individual as compared to other participants. The analyses resulted in a detection rate of 99% with a false identification probability of 0.28%, which demonstrates a very sensitive method for the recognition of athletes based solely on their running style. Further, it can be seen that these differentiations can be described as individual modifications of a general running pattern inherent in all participants. These findings open new perspectives for the assessment of running style, motion in general, and a person’s identification, in, for example, the growing e-sports movement. |
format | Online Article Text |
id | pubmed-7412195 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-74121952020-08-17 The Gaitprint: Identifying Individuals by Their Running Style Weich, Christian M. Vieten, Manfred Sensors (Basel) Article Recognizing the characteristics of a well-developed running style is a central issue in athletic sub-disciplines. The development of portable micro-electro-mechanical-system (MEMS) sensors within the last decades has made it possible to accurately quantify movements. This paper introduces an analysis method, based on limit-cycle attractors, to identify subjects by their specific running style. The movement data of 30 athletes were collected over 20 min. in three running sessions to create an individual gaitprint. A recognition algorithm was applied to identify each single individual as compared to other participants. The analyses resulted in a detection rate of 99% with a false identification probability of 0.28%, which demonstrates a very sensitive method for the recognition of athletes based solely on their running style. Further, it can be seen that these differentiations can be described as individual modifications of a general running pattern inherent in all participants. These findings open new perspectives for the assessment of running style, motion in general, and a person’s identification, in, for example, the growing e-sports movement. MDPI 2020-07-08 /pmc/articles/PMC7412195/ /pubmed/32650424 http://dx.doi.org/10.3390/s20143810 Text en © 2020 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Weich, Christian M. Vieten, Manfred The Gaitprint: Identifying Individuals by Their Running Style |
title | The Gaitprint: Identifying Individuals by Their Running Style |
title_full | The Gaitprint: Identifying Individuals by Their Running Style |
title_fullStr | The Gaitprint: Identifying Individuals by Their Running Style |
title_full_unstemmed | The Gaitprint: Identifying Individuals by Their Running Style |
title_short | The Gaitprint: Identifying Individuals by Their Running Style |
title_sort | gaitprint: identifying individuals by their running style |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7412195/ https://www.ncbi.nlm.nih.gov/pubmed/32650424 http://dx.doi.org/10.3390/s20143810 |
work_keys_str_mv | AT weichchristian thegaitprintidentifyingindividualsbytheirrunningstyle AT mvietenmanfred thegaitprintidentifyingindividualsbytheirrunningstyle AT weichchristian gaitprintidentifyingindividualsbytheirrunningstyle AT mvietenmanfred gaitprintidentifyingindividualsbytheirrunningstyle |