Journal
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
Volume 66, Issue 3, Pages 689-694Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TBME.2018.2854632
Keywords
Biomechanics; Supervised learning; Image motion analysis; Pattern analysis
Categories
Funding
- NHMRC [400937]
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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 (rRMSE 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.
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