Gutenberg Open Science: Identifying underlying individuality across running, walking, and handwriting patterns with conditional cycle-consistent generative adversarial networks

Please use this identifier to cite or link to this item: http://doi.org/10.25358/openscience-9542

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DC Field	Value	Language
dc.contributor.author	Burdack, Johannes	-
dc.contributor.author	Giesselbach, Sven	-
dc.contributor.author	Simak, Marvin L.	-
dc.contributor.author	Ndiaye, Mamadou L.	-
dc.contributor.author	Marquardt, Christian	-
dc.contributor.author	Schöllhorn, Wolfgang I.	-
dc.date.accessioned	2023-09-07T13:00:58Z	-
dc.date.available	2023-09-07T13:00:58Z	-
dc.date.issued	2023	-
dc.identifier.uri	https://openscience.ub.uni-mainz.de/handle/20.500.12030/9560	-
dc.description.abstract	In recent years, the analysis of movement patterns has increasingly focused on the individuality of movements. After long speculations about weak individuality, strong individuality is now accepted, and the first situation–dependent fine structures within it are already identified. Methodologically, however, only signals of the same movements have been compared so far. The goal of this work is to detect cross-movement commonalities of individual walking, running, and handwriting patterns using data augmentation. A total of 17 healthy adults (35.8 ± 11.1 years, eight women and nine men) each performed 627.9 ± 129.0 walking strides, 962.9 ± 182.0 running strides, and 59.25 ± 1.8 handwritings. Using the conditional cycle-consistent generative adversarial network (CycleGAN), conditioned on the participant’s class, a pairwise transformation between the vertical ground reaction force during walking and running and the vertical pen pressure during handwriting was learned in the first step. In the second step, the original data of the respective movements were used to artificially generate the other movement data. In the third step, whether the artificially generated data could be correctly assigned to a person via classification using a support vector machine trained with original data of the movement was tested. The classification F1–score ranged from 46.8% for handwriting data generated from walking data to 98.9% for walking data generated from running data. Thus, cross–movement individual patterns could be identified. Therefore, the methodology presented in this study may help to enable cross–movement analysis and the artificial generation of larger amounts of data.	en_GB
dc.description.sponsorship	Deutsche Forschungsgemeinschaft (DFG)\|491381577\|Open-Access-Publikationskosten 2022–2024 Universität Mainz - Universitätsmedizin	-
dc.language.iso	eng	de
dc.rights	CC BY	*
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/	*
dc.subject.ddc	796 Sport	de_DE
dc.subject.ddc	796 Athletic and outdoor sports and games	en_GB
dc.title	Identifying underlying individuality across running, walking, and handwriting patterns with conditional cycle-consistent generative adversarial networks	en_GB
dc.type	Zeitschriftenaufsatz	de
dc.identifier.doi	http://doi.org/10.25358/openscience-9542	-
jgu.type.contenttype	Scientific article	de
jgu.type.dinitype	article	en_GB
jgu.type.version	Published version	de
jgu.type.resource	Text	de
jgu.organisation.department	FB 02 Sozialwiss., Medien u. Sport	de
jgu.organisation.number	7910	-
jgu.organisation.name	Johannes Gutenberg-Universität Mainz	-
jgu.rights.accessrights	openAccess	-
jgu.journal.title	Frontiers in Bioengineering and Biotechnology	de
jgu.journal.volume	11	de
jgu.pages.alternative	1204115	de
jgu.publisher.year	2023	-
jgu.publisher.name	Frontiers	de
jgu.publisher.place	Lausanne	de
jgu.publisher.issn	2296-4185	de
jgu.organisation.place	Mainz	-
jgu.subject.ddccode	796	de
jgu.publisher.doi	10.3389/fbioe.2023.1204115	de
jgu.organisation.ror	https://ror.org/023b0x485	-
jgu.subject.dfg	Geistes- und Sozialwissenschaften	de
Appears in collections:	DFG-491381577-G

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