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Identifying underlying individuality across running, walking, and handwriting patterns with conditional cycle–consistent generative adversarial networks

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,...

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Autores principales: Burdack, Johannes, Giesselbach, Sven, Simak, Marvin L., Ndiaye, Mamadou L., Marquardt, Christian, Schöllhorn, Wolfgang I.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10436554/
https://www.ncbi.nlm.nih.gov/pubmed/37600317
http://dx.doi.org/10.3389/fbioe.2023.1204115
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author Burdack, Johannes
Giesselbach, Sven
Simak, Marvin L.
Ndiaye, Mamadou L.
Marquardt, Christian
Schöllhorn, Wolfgang I.
author_facet Burdack, Johannes
Giesselbach, Sven
Simak, Marvin L.
Ndiaye, Mamadou L.
Marquardt, Christian
Schöllhorn, Wolfgang I.
author_sort Burdack, Johannes
collection PubMed
description 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.
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spelling pubmed-104365542023-08-19 Identifying underlying individuality across running, walking, and handwriting patterns with conditional cycle–consistent generative adversarial networks Burdack, Johannes Giesselbach, Sven Simak, Marvin L. Ndiaye, Mamadou L. Marquardt, Christian Schöllhorn, Wolfgang I. Front Bioeng Biotechnol Bioengineering and Biotechnology 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. Frontiers Media S.A. 2023-08-04 /pmc/articles/PMC10436554/ /pubmed/37600317 http://dx.doi.org/10.3389/fbioe.2023.1204115 Text en Copyright © 2023 Burdack, Giesselbach, Simak, Ndiaye, Marquardt and Schöllhorn. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Bioengineering and Biotechnology
Burdack, Johannes
Giesselbach, Sven
Simak, Marvin L.
Ndiaye, Mamadou L.
Marquardt, Christian
Schöllhorn, Wolfgang I.
Identifying underlying individuality across running, walking, and handwriting patterns with conditional cycle–consistent generative adversarial networks
title Identifying underlying individuality across running, walking, and handwriting patterns with conditional cycle–consistent generative adversarial networks
title_full Identifying underlying individuality across running, walking, and handwriting patterns with conditional cycle–consistent generative adversarial networks
title_fullStr Identifying underlying individuality across running, walking, and handwriting patterns with conditional cycle–consistent generative adversarial networks
title_full_unstemmed Identifying underlying individuality across running, walking, and handwriting patterns with conditional cycle–consistent generative adversarial networks
title_short Identifying underlying individuality across running, walking, and handwriting patterns with conditional cycle–consistent generative adversarial networks
title_sort identifying underlying individuality across running, walking, and handwriting patterns with conditional cycle–consistent generative adversarial networks
topic Bioengineering and Biotechnology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10436554/
https://www.ncbi.nlm.nih.gov/pubmed/37600317
http://dx.doi.org/10.3389/fbioe.2023.1204115
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