The Gaitprint : Identifying Individuals by Their Running Style

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WEICH, Christian, Manfred M. VIETEN, 2020. The Gaitprint : Identifying Individuals by Their Running Style. In: Sensors. MDPI. 20(14), 3810. eISSN 1424-8220. Available under: doi: 10.3390/s20143810

@article{Weich2020Gaitp-50186, title={The Gaitprint : Identifying Individuals by Their Running Style}, year={2020}, doi={10.3390/s20143810}, number={14}, volume={20}, journal={Sensors}, author={Weich, Christian and Vieten, Manfred M.}, note={Article Number: 3810} }

<rdf:RDF xmlns:dcterms="" xmlns:dc="" xmlns:rdf="" xmlns:bibo="" xmlns:dspace="" xmlns:foaf="" xmlns:void="" xmlns:xsd="" > <rdf:Description rdf:about=""> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <dspace:isPartOfCollection rdf:resource=""/> <dc:creator>Vieten, Manfred M.</dc:creator> <dcterms:rights rdf:resource=""/> <dcterms:isPartOf rdf:resource=""/> <dc:rights>terms-of-use</dc:rights> <dc:contributor>Vieten, Manfred M.</dc:contributor> <dcterms:abstract xml:lang="eng">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 di erentiations 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.</dcterms:abstract> <dcterms:issued>2020</dcterms:issued> <dcterms:hasPart rdf:resource=""/> <bibo:uri rdf:resource=""/> <dc:creator>Weich, Christian</dc:creator> <dcterms:title>The Gaitprint : Identifying Individuals by Their Running Style</dcterms:title> <dspace:hasBitstream rdf:resource=""/> <dc:contributor>Weich, Christian</dc:contributor> <dc:language>eng</dc:language> <dc:date rdf:datatype="">2020-07-08T12:59:25Z</dc:date> <dcterms:available rdf:datatype="">2020-07-08T12:59:25Z</dcterms:available> <foaf:homepage rdf:resource="http://localhost:8080/jspui"/> </rdf:Description> </rdf:RDF>

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