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Deep learning with an attention mechanism for continuous biomechanical motion estimation across varied activities

Reliable estimation of desired motion trajectories plays a crucial part in the continuous control of lower extremity assistance devices such as prostheses and orthoses. Moreover, reliable estimation methods are also required to predict hard-to-measure biomechanical quantities (e.g., joint contact mo...

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Autores principales: Ding, Guanlin, Plummer, Andrew, Georgilas, Ioannis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9618651/
https://www.ncbi.nlm.nih.gov/pubmed/36324889
http://dx.doi.org/10.3389/fbioe.2022.1021505
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author Ding, Guanlin
Plummer, Andrew
Georgilas, Ioannis
author_facet Ding, Guanlin
Plummer, Andrew
Georgilas, Ioannis
author_sort Ding, Guanlin
collection PubMed
description Reliable estimation of desired motion trajectories plays a crucial part in the continuous control of lower extremity assistance devices such as prostheses and orthoses. Moreover, reliable estimation methods are also required to predict hard-to-measure biomechanical quantities (e.g., joint contact moment/force) for use in sports injury science. Recognising that human locomotion is an inherently time-sequential and limb-synergetic behaviour, this study investigates models and learning algorithms for predicting the motion of a subject’s leg from the motion of complementary limbs. The novel deep learning model architectures proposed are based on the Long Short-Term Memory approach with the addition of an attention mechanism. A dataset comprising Inertial Measurement Unit signals from 21 subjects traversing varied terrains was used, including stair ascent/descent, ramp ascent/descent, stopped, level-ground walking and the transitions between these conditions. Fourier Analysis is deployed to evaluate the model robustness, in addition to assessing time-based prediction errors. The experiment on three unseen test participants suggests that the branched neural network structure is preferred to tackle the multioutput problem, and the inclusion of an attention mechanism demonstrates improved performance in terms of accuracy, robustness and network size. An experimental comparison found that 57% of the model parameters were not needed after adding attention layers meanwhile the prediction error is lower than the LSTM model without attention mechanism. The attention model has errors of 9.06% and 7.64% (normalised root mean square error) for ankle and hip acceleration prediction respectively. Also, less high-frequency noise is present in the attention model predictions. We conclude that the internal structure of the proposed deep learning model is justified, principally the benefit of using an attention mechanism. Experimental results for biomechanical motion estimation are obtained, showing greater accuracy than only with LSTM. The trained attention model can be used throughout despite transitioning between terrain types. Such a model will be useful in, for example, the control of lower-limb prostheses, instead of the need to identify and switch between different trajectory generators for different walking modes.
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spelling pubmed-96186512022-11-01 Deep learning with an attention mechanism for continuous biomechanical motion estimation across varied activities Ding, Guanlin Plummer, Andrew Georgilas, Ioannis Front Bioeng Biotechnol Bioengineering and Biotechnology Reliable estimation of desired motion trajectories plays a crucial part in the continuous control of lower extremity assistance devices such as prostheses and orthoses. Moreover, reliable estimation methods are also required to predict hard-to-measure biomechanical quantities (e.g., joint contact moment/force) for use in sports injury science. Recognising that human locomotion is an inherently time-sequential and limb-synergetic behaviour, this study investigates models and learning algorithms for predicting the motion of a subject’s leg from the motion of complementary limbs. The novel deep learning model architectures proposed are based on the Long Short-Term Memory approach with the addition of an attention mechanism. A dataset comprising Inertial Measurement Unit signals from 21 subjects traversing varied terrains was used, including stair ascent/descent, ramp ascent/descent, stopped, level-ground walking and the transitions between these conditions. Fourier Analysis is deployed to evaluate the model robustness, in addition to assessing time-based prediction errors. The experiment on three unseen test participants suggests that the branched neural network structure is preferred to tackle the multioutput problem, and the inclusion of an attention mechanism demonstrates improved performance in terms of accuracy, robustness and network size. An experimental comparison found that 57% of the model parameters were not needed after adding attention layers meanwhile the prediction error is lower than the LSTM model without attention mechanism. The attention model has errors of 9.06% and 7.64% (normalised root mean square error) for ankle and hip acceleration prediction respectively. Also, less high-frequency noise is present in the attention model predictions. We conclude that the internal structure of the proposed deep learning model is justified, principally the benefit of using an attention mechanism. Experimental results for biomechanical motion estimation are obtained, showing greater accuracy than only with LSTM. The trained attention model can be used throughout despite transitioning between terrain types. Such a model will be useful in, for example, the control of lower-limb prostheses, instead of the need to identify and switch between different trajectory generators for different walking modes. Frontiers Media S.A. 2022-10-17 /pmc/articles/PMC9618651/ /pubmed/36324889 http://dx.doi.org/10.3389/fbioe.2022.1021505 Text en Copyright © 2022 Ding, Plummer and Georgilas. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Bioengineering and Biotechnology
Ding, Guanlin
Plummer, Andrew
Georgilas, Ioannis
Deep learning with an attention mechanism for continuous biomechanical motion estimation across varied activities
title Deep learning with an attention mechanism for continuous biomechanical motion estimation across varied activities
title_full Deep learning with an attention mechanism for continuous biomechanical motion estimation across varied activities
title_fullStr Deep learning with an attention mechanism for continuous biomechanical motion estimation across varied activities
title_full_unstemmed Deep learning with an attention mechanism for continuous biomechanical motion estimation across varied activities
title_short Deep learning with an attention mechanism for continuous biomechanical motion estimation across varied activities
title_sort deep learning with an attention mechanism for continuous biomechanical motion estimation across varied activities
topic Bioengineering and Biotechnology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9618651/
https://www.ncbi.nlm.nih.gov/pubmed/36324889
http://dx.doi.org/10.3389/fbioe.2022.1021505
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AT georgilasioannis deeplearningwithanattentionmechanismforcontinuousbiomechanicalmotionestimationacrossvariedactivities