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Gait Trajectory and Gait Phase Prediction Based on an LSTM Network

Lower body segment trajectory and gait phase prediction is crucial for the control of assistance-as-needed robotic devices, such as exoskeletons. In order for a powered exoskeleton with phase-based control to determine and provide proper assistance to the wearer during gait, we propose an approach t...

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Detalles Bibliográficos
Autores principales: Su, Binbin, Gutierrez-Farewik, Elena M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7764336/
https://www.ncbi.nlm.nih.gov/pubmed/33322673
http://dx.doi.org/10.3390/s20247127
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author Su, Binbin
Gutierrez-Farewik, Elena M.
author_facet Su, Binbin
Gutierrez-Farewik, Elena M.
author_sort Su, Binbin
collection PubMed
description Lower body segment trajectory and gait phase prediction is crucial for the control of assistance-as-needed robotic devices, such as exoskeletons. In order for a powered exoskeleton with phase-based control to determine and provide proper assistance to the wearer during gait, we propose an approach to predict segment trajectories up to 200 ms ahead (angular velocity of the thigh, shank and foot segments) and five gait phases (loading response, mid-stance, terminal stance, preswing and swing), based on collected data from inertial measurement units placed on the thighs, shanks, and feet. The approach we propose is a long-short term memory (LSTM)-based network, a modified version of recurrent neural networks, which can learn order dependence in sequence prediction problems. The algorithm proposed has a weighted discount loss function that places more weight in predicting the next three to five time frames but also contributes to an overall prediction performance for up to 10 time frames. The LSTM model was designed to learn lower limb segment trajectories using training samples and was tested for generalization across participants. All predicted trajectories were strongly correlated with the measured trajectories, with correlation coefficients greater than 0.98. The proposed LSTM approach can also accurately predict the five gait phases, particularly swing phase with 95% accuracy in inter-subject implementation. The ability of the LSTM network to predict future gait trajectories and gait phases can be applied in designing exoskeleton controllers that can better compensate for system delays to smooth the transition between gait phases.
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spelling pubmed-77643362020-12-27 Gait Trajectory and Gait Phase Prediction Based on an LSTM Network Su, Binbin Gutierrez-Farewik, Elena M. Sensors (Basel) Article Lower body segment trajectory and gait phase prediction is crucial for the control of assistance-as-needed robotic devices, such as exoskeletons. In order for a powered exoskeleton with phase-based control to determine and provide proper assistance to the wearer during gait, we propose an approach to predict segment trajectories up to 200 ms ahead (angular velocity of the thigh, shank and foot segments) and five gait phases (loading response, mid-stance, terminal stance, preswing and swing), based on collected data from inertial measurement units placed on the thighs, shanks, and feet. The approach we propose is a long-short term memory (LSTM)-based network, a modified version of recurrent neural networks, which can learn order dependence in sequence prediction problems. The algorithm proposed has a weighted discount loss function that places more weight in predicting the next three to five time frames but also contributes to an overall prediction performance for up to 10 time frames. The LSTM model was designed to learn lower limb segment trajectories using training samples and was tested for generalization across participants. All predicted trajectories were strongly correlated with the measured trajectories, with correlation coefficients greater than 0.98. The proposed LSTM approach can also accurately predict the five gait phases, particularly swing phase with 95% accuracy in inter-subject implementation. The ability of the LSTM network to predict future gait trajectories and gait phases can be applied in designing exoskeleton controllers that can better compensate for system delays to smooth the transition between gait phases. MDPI 2020-12-12 /pmc/articles/PMC7764336/ /pubmed/33322673 http://dx.doi.org/10.3390/s20247127 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Su, Binbin
Gutierrez-Farewik, Elena M.
Gait Trajectory and Gait Phase Prediction Based on an LSTM Network
title Gait Trajectory and Gait Phase Prediction Based on an LSTM Network
title_full Gait Trajectory and Gait Phase Prediction Based on an LSTM Network
title_fullStr Gait Trajectory and Gait Phase Prediction Based on an LSTM Network
title_full_unstemmed Gait Trajectory and Gait Phase Prediction Based on an LSTM Network
title_short Gait Trajectory and Gait Phase Prediction Based on an LSTM Network
title_sort gait trajectory and gait phase prediction based on an lstm network
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7764336/
https://www.ncbi.nlm.nih.gov/pubmed/33322673
http://dx.doi.org/10.3390/s20247127
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