Predicting Three-Dimensional Ground Reaction Forces in Running by Using Artificial Neural Networks and Lower Body Kinematics

Abstract

This study explored the use of artificial neural networks in the estimation of runners' kinetics from lower body kinematics. Three supervised feed-forward artificial neural networks with one hidden layer each were modelled and assigned individually with the mapping of a single force component. Number of training epochs, batch size and dropout rate were treated as modelling hyper-parameters and their values were optimised with a grid search. A public data set of twenty-eight professional athletes containing running trails of different speeds (2.5 m/sec, 3.5 m/sec and 4.5 m/sec) was employed to train and validate the networks. Movements of the lower limbs were captured with twelve motion capture cameras and an instrumented dual-belt treadmill. The acceleration of the shanks was fed to the artificial neural networks and the estimated forces were compared to the kinetic recordings of the instrumented treadmill. Root mean square error was used to evaluate the performance of the models. Predictions were accompanied with low errors: 0.134 BW for the vertical, 0.041 BW for the anteroposterior and 0.042 BW for the mediolateral component of the force. Vertical and anteroposterior estimates were independent of running speed (p=0.233 and p=.058, respectively), while mediolateral results were significantly more accurate for low running speeds (p=0.010). The maximum force mean error between measured and estimated values was found during the vertical active peak (0.114 ± 0.088 BW). Findings indicate that artificial neural networks in conjunction with accelerometry may be used to compute three-dimensional ground reaction forces in running.

Publication DOI: https://doi.org/10.1109/ACCESS.2019.2949699
Divisions: College of Engineering & Physical Sciences > School of Engineering and Technology > Mechanical, Biomedical & Design
College of Engineering & Physical Sciences > School of Engineering and Technology
Funding Information: This work was supported in part by the Enterprise Ireland and Setanta College Ltd., under Agreement IP 2017 0606, and in part by the European Regional Development Fund (ERDF) through the Ireland's European Structural and Investment Funds Programmes 2014-2
Additional Information: © 2019 IEEE. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/. Funding Information: This work was supported in part by the Enterprise Ireland and Setanta College Ltd., under Agreement IP 2017 0606, and in part by the European Regional Development Fund (ERDF) through the Ireland’s European Structural and Investment Funds Programmes 2014-2020. Aspects of this publication have emanated from research conducted with the financial support of Science Foundation Ireland under Grant number 12/RC/2289-P2 which is co-funded under the European Regional Development Fund.
Uncontrolled Keywords: Accelerometry,artificial neural networks,human biomechanics,kinematics,motion analysis,sports performance,Computer Science(all),Materials Science(all),Engineering(all)
Publication ISSN: 2169-3536
Last Modified: 23 May 2024 07:19
Date Deposited: 08 Feb 2023 15:56
Full Text Link:
Related URLs: https://ieeexpl ... ocument/8883167 (Publisher URL)
PURE Output Type: Article
Published Date: 2019-10-25
Published Online Date: 2019-10-25
Accepted Date: 2019-10-18
Authors: Komaris, Sokratis (ORCID Profile 0000-0003-4623-9060)
Pérez-Valero, Eduardo
Jordan, Luke
Barton, John
Hennessy, Liam
O'Flynn, Brendan
Tedesco, Salvatore

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