Autoren:	Burdack, Johannes Giesselbach, Sven Simak, Marvin L. Ndiaye, Mamadou L. Marquardt, Christian Schöllhorn, Wolfgang I.
Titel:	Identifying underlying individuality across running, walking, and handwriting patterns with conditional cycle-consistent generative adversarial networks
Online-Publikationsdatum:	7-Sep-2023
Erscheinungsdatum:	2023
Sprache des Dokuments:	Englisch
Zusammenfassung/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.
DDC-Sachgruppe:	796 Sport 796 Athletic and outdoor sports and games
Veröffentlichende Institution:	Johannes Gutenberg-Universität Mainz
Organisationseinheit:	FB 02 Sozialwiss., Medien u. Sport
Veröffentlichungsort:	Mainz
ROR:	https://ror.org/023b0x485
DOI:	http://doi.org/10.25358/openscience-9542
Version:	Published version
Publikationstyp:	Zeitschriftenaufsatz
Weitere Angaben zur Dokumentart:	Scientific article
Nutzungsrechte:	CC BY
Informationen zu den Nutzungsrechten:	https://creativecommons.org/licenses/by/4.0/
Zeitschrift:	Frontiers in Bioengineering and Biotechnology 11
Seitenzahl oder Artikelnummer:	1204115
Verlag:	Frontiers
Verlagsort:	Lausanne
Erscheinungsdatum:	2023
ISSN:	2296-4185
DOI der Originalveröffentlichung:	10.3389/fbioe.2023.1204115
Enthalten in den Sammlungen:	DFG-491381577-G