Imperial College London
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ProfessorAlisonMcGregor

Faculty of Medicine, Department of Surgery & Cancer

Professor of Musculoskeletal Biodynamics
 
 
 
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Contact

 

+44 (0)20 7594 2972a.mcgregor

 
 
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Location

 

Room 202ASir Michael Uren HubWhite City Campus

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Summary

 

Publications

Citation

BibTex format

@article{Rane:2019:10.1007/s10439-018-02190-0,
author = {Rane, L and Ding, Z and McGregor, AH and Bull, AMJ},
doi = {10.1007/s10439-018-02190-0},
journal = {Annals of Biomedical Engineering},
pages = {778--789},
title = {Deep learning for musculoskeletal force prediction},
url = {http://dx.doi.org/10.1007/s10439-018-02190-0},
volume = {47},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Musculoskeletal models permit the determination of internal forces acting during dynamic movement, which is clinically useful, but traditional methods may suffer from slowness and a need for extensive input data. Recently, there has been interest in the use of supervised learning to build approximate models for computationally demanding processes, with benefits in speed and flexibility. Here, we use a deep neural network to learn the mapping from movement space to muscle space. Trained on a set of kinematic, kinetic and electromyographic measurements from 156 subjects during gait, the network’s predictions of internal force magnitudes show good concordance with those derived by musculoskeletal modelling. In a separate set of experiments, training on data from the most widely known benchmarks of modelling performance, the international Grand Challenge competitions, generates predictions that better those of the winning submissions in four of the six competitions. Computational speedup facilitates incorporation into a lab-based system permitting real-time estimation of forces, and interrogation of the trained neural networks provides novel insights into population-level relationships between kinematic and kinetic factors.
AU  - Rane,L
AU  - Ding,Z
AU  - McGregor,AH
AU  - Bull,AMJ
DO  - 10.1007/s10439-018-02190-0
EP  - 789
PY  - 2019///
SN  - 0090-6964
SP  - 778
TI  - Deep learning for musculoskeletal force prediction
T2  - Annals of Biomedical Engineering
UR  - http://dx.doi.org/10.1007/s10439-018-02190-0
UR  - https://link.springer.com/article/10.1007/s10439-018-02190-0
UR  - http://hdl.handle.net/10044/1/65480
VL  - 47
ER  -
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