Deep learning approach for prediction of impact peak appearance at ground reaction force signal of running activity

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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 , vol. 23 , no. 14 , pp. 1052-1059 . https://doi.org/10.1080/10255842.2020.1786072

Title: Deep learning approach for prediction of impact peak appearance at ground reaction force signal of running activity
Author: Girka, Anastasiia; Kulmala, Juha-Pekka; Äyrämö, Sami
Other contributor: University of Helsinki, HUS Children and Adolescents



Date: 2020-10-25
Language: eng
Number of pages: 8
Belongs to series: Computer Methods in Biomechanics and Biomedical Engineering
ISSN: 1025-5842
DOI: https://doi.org/10.1080/10255842.2020.1786072
URI: http://hdl.handle.net/10138/325881
Abstract: 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.
Subject: CNN
binary classification
running gait analysis
risk assessment
force platform
INJURIES
RUNNERS
113 Computer and information sciences
315 Sport and fitness sciences
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