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Authors: Horst, Fabian
Slijepcevic, Djordje
Simak, Marvin
Horsak, Brian
Schöllhorn, Wolfgang Immanuel
Zeppelzauer, Matthias
Title: Modeling biological individuality using machine learning : a study on human gait
Online publication date: 25-Jul-2023
Year of first publication: 2023
Language: english
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: 610 Medizin
610 Medical sciences
796 Sport
796 Athletic and outdoor sports and games
Institution: Johannes Gutenberg-Universität Mainz
Department: FB 02 Sozialwiss., Medien u. Sport
Place: Mainz
Version: Published version
Publication type: Zeitschriftenaufsatz
Document type specification: Scientific article
License: CC BY-NC-ND
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Journal: Computational and Structural Biotechnology Journal
Pages or article number: 3414
Publisher: Elsevier
Publisher place: Amsterdam
Issue date: 2023
Publisher DOI: 10.1016/j.csbj.2023.06.009
Appears in collections:DFG-491381577-G

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