Deep learning workbench for biomechanics
http://digitalathlete.org

Machine learning driven by wearable sensors can replace laboratory instrumentation and bring biomechanical accuracy and validity to the sports field. By taking a new approach and training convolutional neural networks (CNNs) with archived biomechanics data, we are creating deep learning models to facilitate accurate multidimensional on-field predictive analytics. Using test sets from multi data-captures which include ground truth force plate and/or source modelling, we see strong correspondence between measured versus predicted ground reaction forces and moments, and knee joint moments, from markers (GRF/M r>0.97, KJM r>0.88, both 8 markers, 33% stance, sidestepping). Currently, we are investigating novel methods to build models for predicting GRF/M from wearable sensors (r>0.88, 4 accelerometers, 100% stance, sidestepping). William Johnson BEngHons, MExerSc, CPEng, CSCS [email protected] September 2018

Caution, model files are large, you may not wish to pull the complete repository. GitHub limits file sizes to 100MB, files larger than this have been broken up using split. Instructions to reconstitute files are given inline.

Study four: Multidimensional knee joint moments from wearable sensor accelerations via deep learning

Keywords	Biomechanics · Wearable sensors · Synthetic accelerations · Motion capture · Sports analytics
Paper	In preparation
WCB 2018 conference	Multidimensional ground reaction forces and moments from wearable sensor accelerations via deep learning Abstract http://bit.ly/2GzYnHD Presentation with commentary http://bit.ly/2tCKHTo

Study three: On-field player workload exposure and knee injury risk monitoring via deep learning

Keywords	Biomechanics · Wearable sensors · Computer vision · Motion capture · Sports analytics
arXiv paper	https://arxiv.org/abs/1809.08016
Presentation	http://bit.ly/2HS7HCv
Animation	Training set marker trajectories versus corresponding knee joint moments visualization (supplementary figure) http://bit.ly/2yTaX1f
MATLAB figures	https://github.com/johnsonwr/digitalathlete/tree/master/study3/figures
Caffe models	https://github.com/johnsonwr/digitalathlete/tree/master/study3/models (1.3GB) cat grftrain_180613080625130_mcrnet.caffemodel_j01_* > grftrain_180613080625130_mcrnet.caffemodel_j01 # reconstitute Caffe model 1
Prototxt	https://github.com/johnsonwr/digitalathlete/tree/master/study3/prototxt

Study two: Predicting athlete ground reaction forces and moments from spatio-temporal driven CNN models

Keywords	Biomechanics · Supervised learning · Image motion analysis · Computer simulation
IEEE TBME paper	https://ieeexplore.ieee.org/document/8408711
Animation	Training set marker trajectories versus corresponding ground reaction forces and moments visualization (supplementary figure) http://bit.ly/2Is3PJx
MATLAB figures	https://github.com/johnsonwr/digitalathlete/tree/master/study2/figures
Caffe models	https://github.com/johnsonwr/digitalathlete/tree/master/study2/models (1.3GB) cat grftrain_180505092408060_mcrnet.caffemodel_j01_* > grftrain_180505092408060_mcrnet.caffemodel_j01 # reconstitute Caffe model 1
Prototxt	https://github.com/johnsonwr/digitalathlete/tree/master/study2/prototxt
CaffeNet reference	https://github.com/BVLC/caffe/tree/master/models/bvlc_reference_caffenet
ISBS 2017 conference	Prediction of ground reaction forces and moments via supervised learning is independent of participant sex, height and mass Abstract https://commons.nmu.edu/cgi/viewcontent.cgi?&article=1034&context=isbs Presentation http://bit.ly/2MvqW8c

Study one: Predicting athlete ground reaction forces and moments from motion capture

Keywords	Action recognition · Wearable sensors · Computer simulation
MBEC paper	https://link.springer.com/article/10.1007/s11517-018-1802-7
Presentation with commentary	http://bit.ly/2kcgXrw
MATLAB figures	https://github.com/johnsonwr/digitalathlete/tree/master/study1/figures
R models	https://github.com/johnsonwr/digitalathlete/tree/master/study1/models (1.9GB) cat grftrain_171214215406095_R_predict_model_* > grftrain_171214215406095_R_predict_model.Rda # reconstitute R model
R SPLS reference	https://cran.r-project.org/web/packages/spls/index.html