A1 Journal article (refereed)
Deep learning approach for prediction of impact peak appearance at ground reaction force signal of running activity (2020)

Girka, A., Kulmala, J.-P., & Äyrämö, S. (2020). Deep learning approach for prediction of impact peak appearance at ground reaction force signal of running activity. Computer Methods in Biomechanics and Biomedical Engineering, 23(14), 1052-1059. https://doi.org/10.1080/10255842.2020.1786072

JYU authors or editors

Publication details

All authors or editors: Girka, Anastasiia; Kulmala, Juha-Pekka; Äyrämö, Sami

Journal or series: Computer Methods in Biomechanics and Biomedical Engineering

ISSN: 1025-5842

eISSN: 1476-8259

Publication year: 2020

Volume: 23

Issue number: 14

Pages range: 1052-1059

Publisher: Taylor & Francis

Publication country: United Kingdom

Publication language: English

DOI: https://doi.org/10.1080/10255842.2020.1786072

Publication open access: Openly available

Publication channel open access: Partially open access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/71131


Protruding impact peak is one of the features of vertical ground reaction force (GRF) that is related to injury risk while running. The present research is dedicated to predicting GRF impact peak appearance by setting a binary classification problem. Kinematic data, namely a number of raw signals in the sagittal plane, collected by the Vicon motion capture system (Oxford Metrics Group, UK) were employed as predictors. Therefore, the input data for the predictive model are presented as a multi-channel time series. Deep learning techniques, namely five convolutional neural network (CNN) models were applied to the binary classification analysis, based on a Multi-Layer Perceptron (MLP) classifier, support vector machine (SVM), logistic regression, k-nearest neighbors (kNN), and random forest algorithms. SVM, logistic regression, and random forest classifiers demonstrated performances that do not statistically significantly differ. The best classification accuracy achieved is 81.09% ± 2.58%. Due to good performance of the models, this study serves as groundwork for further application of deep learning approaches to predicting kinetic information based on this kind of input data.

Keywords: biomechanics; motion analysis; running; stress injuries; motion capture; machine learning; neural networks (information technology)

Free keywords: CNN; binary classification; running gait analysis; risk assessment; force platform

Contributing organizations

Ministry reporting: Yes

Reporting Year: 2020

JUFO rating: 1

Last updated on 2021-07-07 at 21:35