Gutenberg Open Science: Modeling biological individuality using machine learning : a study on human gait

Please use this identifier to cite or link to this item: http://doi.org/10.25358/openscience-9292

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DC Field	Value	Language
dc.contributor.author	Horst, Fabian	-
dc.contributor.author	Slijepcevic, Djordje	-
dc.contributor.author	Simak, Marvin	-
dc.contributor.author	Horsak, Brian	-
dc.contributor.author	Schöllhorn, Wolfgang Immanuel	-
dc.contributor.author	Zeppelzauer, Matthias	-
dc.date.accessioned	2023-07-25T13:25:34Z	-
dc.date.available	2023-07-25T13:25:34Z	-
dc.date.issued	2023	-
dc.identifier.uri	https://openscience.ub.uni-mainz.de/handle/20.500.12030/9310	-
dc.description.abstract	Human gait is a complex and unique biological process that can offer valuable insights into an individual’s health and well-being. In this work, we leverage a machine learning-based approach to model individual gait signatures and identify factors contributing to inter-individual variability in gait patterns. We provide a comprehensive analysis of gait individuality by (1) demonstrating the uniqueness of gait signatures in a large-scale dataset and (2) highlighting the gait characteristics that are most distinctive to each individual. We utilized the data from three publicly available datasets comprising 5368 bilateral ground reaction force recordings during level overground walking from 671 distinct healthy individuals. Our results show that individuals can be identified with a prediction accuracy of 99.3% by using the bilateral signals of all three ground reaction force components, with only 10 out of 1342 recordings in our test data being misclassified. This indicates that the combination of bilateral ground reaction force signals with all three components provides a more comprehensive and accurate representation of an individual’s gait signature. The highest accuracy was achieved by (linear) Support Vector Machines (99.3%), followed by Random Forests (98.7%), Convolutional Neural Networks (95.8%), and Decision Trees (82.8%). The proposed approach provides a powerful tool to better understand biological individuality and has potential applications in personalized healthcare, clinical diagnosis, and therapeutic interventions.	en_GB
dc.language.iso	eng	de
dc.rights	CC BY-NC-ND	*
dc.rights.uri	https://creativecommons.org/licenses/by-nc-nd/4.0/	*
dc.subject.ddc	610 Medizin	de_DE
dc.subject.ddc	610 Medical sciences	en_GB
dc.subject.ddc	796 Sport	de_DE
dc.subject.ddc	796 Athletic and outdoor sports and games	en_GB
dc.title	Modeling biological individuality using machine learning : a study on human gait	en_GB
dc.type	Zeitschriftenaufsatz	de
dc.identifier.doi	http://doi.org/10.25358/openscience-9292	-
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	Computational and Structural Biotechnology Journal	de
jgu.journal.volume	21	de
jgu.pages.start	3414	de
jgu.pages.end	3423	de
jgu.publisher.year	2023	-
jgu.publisher.name	Elsevier	de
jgu.publisher.place	Amsterdam	de
jgu.organisation.place	Mainz	-
jgu.subject.ddccode	610	de
jgu.subject.ddccode	796	de
jgu.publisher.doi	10.1016/j.csbj.2023.06.009	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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