Prediction of gait trajectories based on the Long Short Term Memory neural networks.

The forecasting of lower limb trajectories can improve the operation of assistive devices and minimise the risk of tripping and balance loss. The aim of this work was to examine four Long Short Term Memory (LSTM) neural network architectures (Vanilla, Stacked, Bidirectional and Autoencoders) in pred...

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Autores principales:	Abdelrahman Zaroug, Alessandro Garofolini, Daniel T H Lai, Kurt Mudie, Rezaul Begg
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a5de0cf3941c41b585df56e04a3f2f87
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id	oai:doaj.org-article:a5de0cf3941c41b585df56e04a3f2f87
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spelling	oai:doaj.org-article:a5de0cf3941c41b585df56e04a3f2f872021-12-02T20:18:38ZPrediction of gait trajectories based on the Long Short Term Memory neural networks.1932-620310.1371/journal.pone.0255597https://doaj.org/article/a5de0cf3941c41b585df56e04a3f2f872021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0255597https://doaj.org/toc/1932-6203The forecasting of lower limb trajectories can improve the operation of assistive devices and minimise the risk of tripping and balance loss. The aim of this work was to examine four Long Short Term Memory (LSTM) neural network architectures (Vanilla, Stacked, Bidirectional and Autoencoders) in predicting the future trajectories of lower limb kinematics, i.e. Angular Velocity (AV) and Linear Acceleration (LA). Kinematics data of foot, shank and thigh (LA and AV) were collected from 13 male and 3 female participants (28 ± 4 years old, 1.72 ± 0.07 m in height, 66 ± 10 kg in mass) who walked for 10 minutes at preferred walking speed (4.34 ± 0.43 km.h-1) and at an imposed speed (5km.h-1, 15.4% ± 7.6% faster) on a 0% gradient treadmill. The sliding window technique was adopted for training and testing the LSTM models with total kinematics time-series data of 10,500 strides. Results based on leave-one-out cross validation, suggested that the LSTM autoencoders is the top predictor of the lower limb kinematics trajectories (i.e. up to 0.1s). The normalised mean squared error was evaluated on trajectory predictions at each time-step and it obtained 2.82-5.31% for the LSTM autoencoders. The ability to predict future lower limb motions may have a wide range of applications including the design and control of bionics allowing improved human-machine interface and mitigating the risk of falls and balance loss.Abdelrahman ZarougAlessandro GarofoliniDaniel T H LaiKurt MudieRezaul BeggPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 8, p e0255597 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Abdelrahman Zaroug Alessandro Garofolini Daniel T H Lai Kurt Mudie Rezaul Begg Prediction of gait trajectories based on the Long Short Term Memory neural networks.
description	The forecasting of lower limb trajectories can improve the operation of assistive devices and minimise the risk of tripping and balance loss. The aim of this work was to examine four Long Short Term Memory (LSTM) neural network architectures (Vanilla, Stacked, Bidirectional and Autoencoders) in predicting the future trajectories of lower limb kinematics, i.e. Angular Velocity (AV) and Linear Acceleration (LA). Kinematics data of foot, shank and thigh (LA and AV) were collected from 13 male and 3 female participants (28 ± 4 years old, 1.72 ± 0.07 m in height, 66 ± 10 kg in mass) who walked for 10 minutes at preferred walking speed (4.34 ± 0.43 km.h-1) and at an imposed speed (5km.h-1, 15.4% ± 7.6% faster) on a 0% gradient treadmill. The sliding window technique was adopted for training and testing the LSTM models with total kinematics time-series data of 10,500 strides. Results based on leave-one-out cross validation, suggested that the LSTM autoencoders is the top predictor of the lower limb kinematics trajectories (i.e. up to 0.1s). The normalised mean squared error was evaluated on trajectory predictions at each time-step and it obtained 2.82-5.31% for the LSTM autoencoders. The ability to predict future lower limb motions may have a wide range of applications including the design and control of bionics allowing improved human-machine interface and mitigating the risk of falls and balance loss.
format	article
author	Abdelrahman Zaroug Alessandro Garofolini Daniel T H Lai Kurt Mudie Rezaul Begg
author_facet	Abdelrahman Zaroug Alessandro Garofolini Daniel T H Lai Kurt Mudie Rezaul Begg
author_sort	Abdelrahman Zaroug
title	Prediction of gait trajectories based on the Long Short Term Memory neural networks.
title_short	Prediction of gait trajectories based on the Long Short Term Memory neural networks.
title_full	Prediction of gait trajectories based on the Long Short Term Memory neural networks.
title_fullStr	Prediction of gait trajectories based on the Long Short Term Memory neural networks.
title_full_unstemmed	Prediction of gait trajectories based on the Long Short Term Memory neural networks.
title_sort	prediction of gait trajectories based on the long short term memory neural networks.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/a5de0cf3941c41b585df56e04a3f2f87
work_keys_str_mv	AT abdelrahmanzaroug predictionofgaittrajectoriesbasedonthelongshorttermmemoryneuralnetworks AT alessandrogarofolini predictionofgaittrajectoriesbasedonthelongshorttermmemoryneuralnetworks AT danielthlai predictionofgaittrajectoriesbasedonthelongshorttermmemoryneuralnetworks AT kurtmudie predictionofgaittrajectoriesbasedonthelongshorttermmemoryneuralnetworks AT rezaulbegg predictionofgaittrajectoriesbasedonthelongshorttermmemoryneuralnetworks
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Prediction of gait trajectories based on the Long Short Term Memory neural networks.

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