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Non-invasive estimation of ground and joint kinetics through deep learning
http://digitalathlete.org

Deep learning models driven by wearable sensor accelerometers can replace captive laboratory instrumentation to facilitate biomechanical accuracy and validity anywhere

By employing a new deep learning workbench for spatio-temporal data, we train convolutional neural networks (CNN) with archive biomechanics data to predict accurate multidimensional on-field analytics for complex sports movements. Using test sets from multi data-captures which include ground truth force plate or source modeling, we see strong correspondence between measured versus predicted ground reaction forces and moments, and knee joint moments. Driven by eight markers, study two GRF/M mean r>0.97, study three KJM mean r>0.88, and from five wearable sensor accelerometers, study four GRF mean r>0.87. The overarching hypothesis, whether it is possible to build deep learning models which can mimic the physics behind human movement, specifically to replace force plate derived kinetic output, is supported.

William R Johnson PhD CPEng CSCS (he/him/his)
bill@johnsonwr.com | July 2022
cv.billjohnson.org | videocv.billjohnson.org
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.


Post-doc: Conference presentations, panels, & session chairs

ISBS-2022
ISBS
Neural Networks Session Chair http://www.isbs2022.org/programme.html
NSCA/PBSCCS-2021
NSCA
NSCA Baseball and Sport Science SIG Performance Technology Roundtable
Video https://vimeo.com/627746779/74b81ecfd5
ISBS-2021
ISBS
Data Science and Sports Biomechanics Panel http://www.isbs2021.org/workshops-and-panels
Video https://youtu.be/A_PeEtMN92k


Post-doc: A comparison of three neural network approaches for estimating joint angles and moments from inertial measurement units

KeywordsMachine learning · Wearable sensors · Joint kinematics · Joint kinetics
Sensors https://www.mdpi.com/1424-8220/21/13/4535/pdf [12]


Doctoral thesis: Non-invasive estimation of ground and joint kinetics through deep learning

KeywordsBiomechanics · Data science · Deep learning · Big data · Sports analytics · Computer vision · Motion capture · Wearable sensors
The University
of Western Australia
https://research-repository.uwa.edu.au/en/publications/non-invasive-estimation-of-ground-and-joint-kinetics-through-deep


Study four: Multidimensional ground reaction forces and moments from wearable sensor accelerations via deep learning

KeywordsBiomechanics · Wearable sensors · Simulated accelerations · Workload exposure · Sports analytics · Deep learning
IEEE TBMEhttps://ieeexplore.ieee.org/document/9130158 [11a]
arXivhttps://arxiv.org/abs/1903.07221 [11b]
Deep learning workbench for biomechanics Each study utilized an incremental sequence of data preparation and modeling strategies, which by study four had evolved into the "deep learning workbench for biomechanics." Although the individual data science components had previously existed in the literature, the approach was novel and unique in this configuration and application to sports biomechanics.

ISB-2019
ISB/ASB
Multidimensional ground reaction forces predicted from a single sacrum-mounted accelerometer via deep learning
Abstract http://bit.ly/2M9j3rw [10]
Presentation http://bit.ly/2SHcsYv
EMS HDR Conference 2018 Poster (Conference Award) http://bit.ly/2yXgdgO
WCB-2018 Abstract (Student Bursary Award) http://bit.ly/2GzYnHD [6]
Presentation with commentary http://bit.ly/2tCKHTo
ISBS-2018
ISBS
Artificial intelligence, data analytics and sports biomechanics: A new era or a false dawn?
Abstract https://commons.nmu.edu/cgi/viewcontent.cgi?article=1618&context=isbs [7]
MATLAB figureshttps://github.com/johnsonwr/digitalathlete/tree/master/study4/figures
Caffe modelshttps://github.com/johnsonwr/digitalathlete/tree/master/study4/models (637MB)
cat grftrain_181112104456175_mcrnet.caffemodel_* > grftrain_181112104456175_mcrnet.caffemodel # reconstitute CaffeNet donor seed model
cat grftrain_181123170749181_mcrnet.caffemodel_* > grftrain_181123170749181_mcrnet.caffemodel # reconstitute CaffeNet model
Prototxthttps://github.com/johnsonwr/digitalathlete/tree/master/study4/prototxt


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

KeywordsBiomechanics · Wearable sensors · Computer vision · Motion capture · Sports analytics
Journal of Biomechanicshttps://www.sciencedirect.com/science/article/abs/pii/S0021929019304427 [8a]
arXivhttps://arxiv.org/abs/1809.08016 [8b]
ISB-2019
ISB/ASB
Predicting ground and joint kinetics from wearable sensor accelerations via deep learning
Abstract http://bit.ly/2y7mZ3A [9]
Presentation (panel) http://bit.ly/2rIh2uo
Presentationhttp://bit.ly/2HS7HCv
AnimationTraining set marker trajectories versus corresponding knee joint moments visualization
(supplementary figure) http://bit.ly/2yTaX1f
MATLAB figureshttps://github.com/johnsonwr/digitalathlete/tree/master/study3/figures
Caffe modelshttps://github.com/johnsonwr/digitalathlete/tree/master/study3/models (2.6GB)

cat grftrain_180612214018112_mcrnet.caffemodel_j01_* > grftrain_180612214018112_mcrnet.caffemodel_j01 # reconstitute CaffeNet donor seed model 01
cat grftrain_190215144249130_mcrnet.caffemodel_j01_* > grftrain_190215144249130_mcrnet.caffemodel_j01 # reconstitute CaffeNet model 01
Caffe prototxthttps://github.com/johnsonwr/digitalathlete/tree/master/study3/prototxt


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

KeywordsBiomechanics · Supervised learning · Image motion analysis · Computer simulation · Pattern analysis
IEEE TBME Paper https://ieeexplore.ieee.org/document/8408711 [5]
Cover & Feature https://tbme.embs.org/2019/03/01/predicting-athlete-ground-reaction-forces-and-moments-from-spatio-temporal-driven-cnn-models
Animation Training set marker trajectories versus corresponding ground reaction forces and moments visualization
(supplementary figure) http://bit.ly/2Is3PJx
UWA CSSE Conference 2017 Relative performance of Caffe deep learning models for spatio-temporal sport analytics
Presentation http://bit.ly/2TCWqwM
ISBS-2017
ISBS
Prediction of ground reaction forces and moments via supervised learning is independent of participant sex, height and mass
Abstract (Student Travel Grant) https://commons.nmu.edu/cgi/viewcontent.cgi?&article=1034&context=isbs [3]
Presentation http://bit.ly/2MvqW8c
MATLAB figureshttps://github.com/johnsonwr/digitalathlete/tree/master/study2/figures
Caffe modelshttps://github.com/johnsonwr/digitalathlete/tree/master/study2/models (1.3GB)

cat grftrain_190215204017060_mcrnet.caffemodel_j01_* > grftrain_190215204017060_mcrnet.caffemodel_j01 # reconstitute CaffeNet model 01
Caffe prototxthttps://github.com/johnsonwr/digitalathlete/tree/master/study2/prototxt
CaffeNet referencehttps://github.com/BVLC/caffe/tree/master/models/bvlc_reference_caffenet


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

KeywordsAction recognition · Wearable sensors · Computer simulation
MBEChttps://link.springer.com/article/10.1007/s11517-018-1802-7 [4]
UWA CSSE Conference 2016Presentation with commentary http://bit.ly/2kcgXrw
ISBS-2016
ISBS
The personalised 'Digital Athlete': An evolving vision for the capture, modelling and simulation, of on-field athletic performance
Abstract https://ojs.ub.uni-konstanz.de/cpa/article/download/7099/6390 [2]
MATLAB figureshttps://github.com/johnsonwr/digitalathlete/tree/master/study1/figures
R modelshttps://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 referencehttps://cran.r-project.org/web/packages/spls/index.html


HDR prelim: Validity of a markerless motion capture system for sporting application

The University
of Western Australia
https://bit.ly/3qLDIUB


Master's assignment: Talent identification in elite rugby union - a theoretical update to an existing predictor algorithm

KeywordsAthlete selection · Predictor variables · Algorithm · Weightings
JASC https://www.strengthandconditioning.org/jasc-25-3 [1]


Machine Learning resources

Andrew Ng Machine Learning course
https://www.coursera.org/learn/machine-learning
https://www.coursera.org/learn/ai-for-everyone
http://cs229.stanford.edu
http://www.mlyearning.org
Fei-Fei Li Stanford Convolutional Neural Networks for Visual Recognition
http://cs231n.stanford.edu
http://cs231n.github.io
Ian Goodfellow Deep Learning
http://www.deeplearningbook.org
https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618
Bill Lubanovic Introducing Python
https://www.amazon.com/Introducing-Python-Modern-Computing-Packages/dp/1449359361
Sebastian Raschka Python Machine Learning
https://www.amazon.com/Python-Machine-Learning-scikit-learn-TensorFlow-ebook/dp/B0742K7HYF
Aurélien Géron Hands-On Machine Learning with Scikit-Learn & TensorFlow
https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow-ebook/dp/B06XNKV5TS
Sean Driver Perth Machine Learning Group
https://www.meetup.com/en-AU/Perth-Machine-Learning-Group
https://github.com/pmlg/pmlg.github.io
https://www.fast.ai


Biomechanics resources

The University
of Western Australia
Biomechanics courses
SSEH2250 Biomechanics in Sport and Exercise
SSEH3355 Biomechanical Principles
SSEH4633 Advanced Biomechanical Methods
David Winter Biomechanics and Motor Control of Human Movement
https://www.amazon.com/Biomechanics-Motor-Control-Human-Movement/dp/0470398183
D. Gordon E. Robertson
Graham E. Caldwell
Joseph Hamill
Gary Kamen
Saunders N. Whittlesey
Research Methods in Biomechanics
https://www.amazon.com/Research-Methods-Biomechanics-Gordon-Robertson/dp/0736093400
Roger Bartlett Introduction to Sports Biomechanics
https://www.amazon.com/Introduction-Sports-Biomechanics-Analysing-Movement/dp/0415632439
Joseph Hamill
Kathleen Knutzen
Timothy Derrick
Biomechanical Basis of Human Movement
https://www.amazon.com/Biomechanical-Basis-Movement-Joseph-Hamill/dp/1451177305
Jim Richards Biomechanics in Clinic and Research: An Interactive Teaching and Learning Course
https://www.amazon.com/Comprehensive-Textbook-Clinical-Biomechanics-learning/dp/0702054895
Carl Peyton
Adrian Burden
Biomechanical Evaluation of Movement in Sport and Exercise
https://www.amazon.com/Biomechanical-Evaluation-Movement-Exercise-Science/dp/0415632668
Youlian Hong
Roger Bartlett
Routledge Handbook of Biomechanics and Human Movement Science
https://www.amazon.com/Routledge-Handbook-Biomechanics-International-Handbooks-ebook/dp/B001PC1ZX8
The Biomechanist The Week in Biomechanics
https://www.biomechanist.net/blog
Biomch-L Forum, sponsored by the International Society of Biomechanics (ISB)
https://biomch-l.isbweb.org
3-D Analysis of Human Movement Technical Group of the International Society of Biomechanics (ISB)
http://www.geocities.ws/3d-ahm
Awesome Biomechanics A curated repository of biomechanical resources
https://github.com/modenaxe/awesome-biomechanics


PhD supervisors

Jacqueline A. Alderson School of Human Sciences (Exercise and Sport Science), The University of Western Australia, Perth, Australia
Sports Performance Research Institute New Zealand (SPRINZ), Auckland University of Technology, Auckland, New Zealand
https://research-repository.uwa.edu.au/en/persons/jacqueline-alderson
Ajmal Mian Department of Computer Science and Software Engineering, The University of Western Australia, Perth, Australia
https://research-repository.uwa.edu.au/en/persons/ajmal-mian
Machine Intelligence Group
https://www.youtube.com/channel/UCy-HDqRqdYS3UUiCIqqfFxQ/videos
David G. Lloyd Menzies Health Institute Queensland, and the School of Allied Health Sciences, Griffith University, Gold Coast, Australia
https://experts.griffith.edu.au/academic/david.lloyd


Acknowledgements

This project was partially supported by the ARC Discovery Grant DP190102443 and an Australian Government Research Training Program Scholarship. NVIDIA Corporation is gratefully acknowledged for the GPU provision through its Hardware Grant Program, Eigenvector Research for the PLS_Toolbox licence, and C-Motion Inc. for the Visual3D licence. Portions of data included in this study were funded by NHMRC grant 400937.


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