Predicting Athlete Ground Reaction Forces and Moments From Spatio-Temporal Driven CNN Models

The accurate prediction of three-dimensional (3-D) ground reaction forces and moments (GRF/Ms) outside the laboratory setting would represent a watershed for on-field biomechanical analysis. To extricate the biomechanist's reliance on ground embedded force plates, this study sought to improve o...

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Bibliographic Details
Published in:IEEE transactions on biomedical engineering 2019-03, Vol.66 (3), p.689-694
Main Authors: Johnson, William Robert, Alderson, Jacqueline, Lloyd, David, Mian, Ajmal
Format: Article
Language:English
Subjects:
Artificial neural networks
Athletes
Australia
Biological system modeling
Biomechanical engineering
Biomechanical Phenomena - physiology
Biomechanics
Female
Force
Force plates
Ground truth
Humans
Image motion analysis
Image Processing, Computer-Assisted - methods
Machine learning
Male
Mathematical models
Motion capture
Neural networks
Neural Networks, Computer
Pattern analysis
Pattern Recognition, Automated
Predictions
Regression analysis
Sports - physiology
Supervised learning
Supervised Machine Learning
Three dimensional models
Time compression
Training
Trajectory
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Summary:The accurate prediction of three-dimensional (3-D) ground reaction forces and moments (GRF/Ms) outside the laboratory setting would represent a watershed for on-field biomechanical analysis. To extricate the biomechanist's reliance on ground embedded force plates, this study sought to improve on an earlier partial least squares (PLS) approach by using deep learning to predict 3-D GRF/Ms from legacy marker based motion capture sidestepping trials, ranking multivariate regression of GRF/Ms from five convolutional neural network (CNN) models. In a possible first for biomechanics, tactical feature engineering techniques were used to compress space-time and facilitate fine-tuning from three pretrained CNNs, from which a model derivative of ImageNet called "CaffeNet" achieved the strongest average correlation to ground truth GRF/Ms r (F mean ) 0.9881 and r (M mean ) 0.9715 ( r RMSE 4.31 and 7.04%). These results demonstrate the power of CNN models to facilitate real-world multivariate regression with practical application for spatio-temporal sports analytics.
ISSN:0018-9294
1558-2531
DOI:10.1109/TBME.2018.2854632