Autoren:	Horst, Fabian Slijepcevic, Djordje Simak, Marvin Horsak, Brian Schöllhorn, Wolfgang Immanuel Zeppelzauer, Matthias
Titel:	Modeling biological individuality using machine learning : a study on human gait
Online-Publikationsdatum:	25-Jul-2023
Erscheinungsdatum:	2023
Sprache des Dokuments:	Englisch
Zusammenfassung/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.
DDC-Sachgruppe:	610 Medizin 610 Medical sciences 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-9292
Version:	Published version
Publikationstyp:	Zeitschriftenaufsatz
Weitere Angaben zur Dokumentart:	Scientific article
Nutzungsrechte:	CC BY-NC-ND
Informationen zu den Nutzungsrechten:	https://creativecommons.org/licenses/by-nc-nd/4.0/
Zeitschrift:	Computational and Structural Biotechnology Journal 21
Seitenzahl oder Artikelnummer:	3414 3423
Verlag:	Elsevier
Verlagsort:	Amsterdam
Erscheinungsdatum:	2023
DOI der Originalveröffentlichung:	10.1016/j.csbj.2023.06.009
Enthalten in den Sammlungen:	DFG-491381577-G