Please use this identifier to cite or link to this item: http://doi.org/10.25358/openscience-482
Authors: Horst, Fabian
Lapuschkin, Sebastian
Samek, Wojciech
Müller, Klaus-Robert
Schöllhorn, Wolfgang
Title: Explaining the unique nature of individual gait patterns with deep learning
Online publication date: 25-Feb-2019
Language: english
Abstract: Machine learning (ML) techniques such as (deep) artificial neural networks (DNN) are solving very successfully a plethora of tasks and provide new predictive models for complex physical, chemical, biological and social systems. However, in most cases this comes with the disadvantage of acting as a black box, rarely providing information about what made them arrive at a particular prediction. This black box aspect of ML techniques can be problematic especially in medical diagnoses, so far hampering a clinical acceptance. The present paper studies the uniqueness of individual gait patterns in clinical biomechanics using DNNs. By attributing portions of the model predictions back to the input variables (ground reaction forces and full-body joint angles), the Layer-Wise Relevance Propagation (LRP) technique reliably demonstrates which variables at what time windows of the gait cycle are most relevant for the characterisation of gait patterns from a certain individual. By measuring the time-resolved contribution of each input variable to the prediction of ML techniques such as DNNs, our method describes the first general framework that enables to understand and interpret non-linear ML methods in (biomechanical) gait analysis and thereby supplies a powerful tool for analysis, diagnosis and treatment of human gait.
DDC: 000 Allgemeines
000 Generalities
Institution: Johannes Gutenberg-Universität
Department: FB 02 Sozialwiss., Medien u. Sport
Place: Mainz
DOI: http://doi.org/10.25358/openscience-482
Version: Published version
Publication type: Zeitschriftenaufsatz
License: CC-BY
Information on rights of use: https://creativecommons.org/licenses/by/4.0/
Journal: Scientific reports
9
Pages or article number: Art. 2391
Publisher: Macmillan Publishers Limited, part of Springer Nature
Publisher place: London
Issue date: 2019
ISSN: 2045-2322
Publisher's URL: http://dx.doi.org/10.1038/s41598-019-38748-8
Appears in collections:JGU-Publikationen

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