Deep learning for musculoskeletal force prediction

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Title: Deep learning for musculoskeletal force prediction
Authors: Rane, L
Ding, Z
McGregor, AH
Bull, AMJ
Item Type: Journal Article
Abstract: 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.
Issue Date: 31-Dec-2018
Date of Acceptance: 13-Dec-2018
ISSN: 0090-6964
Publisher: Springer Nature
Journal / Book Title: Annals of Biomedical Engineering
Copyright Statement: © 2018 The Author(s). This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Keywords: 11 Medical And Health Sciences
09 Engineering
Biomedical Engineering
Publication Status: Published online
Online Publication Date: 2018-12-31
Appears in Collections:Faculty of Engineering

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