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Using Deep Learning to Predict Minimum Foot–Ground Clearance Event from Toe-Off Kinematics
Efficient, adaptive, locomotor function is critically important for maintaining our health and independence, but falls-related injuries when walking are a significant risk factor, particularly for more vulnerable populations such as older people and post-stroke individuals. Tripping is the leading c...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9502804/ https://www.ncbi.nlm.nih.gov/pubmed/36146308 http://dx.doi.org/10.3390/s22186960 |
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author | Asogwa, Clement Ogugua Nagano, Hanatsu Wang, Kai Begg, Rezaul |
author_facet | Asogwa, Clement Ogugua Nagano, Hanatsu Wang, Kai Begg, Rezaul |
author_sort | Asogwa, Clement Ogugua |
collection | PubMed |
description | Efficient, adaptive, locomotor function is critically important for maintaining our health and independence, but falls-related injuries when walking are a significant risk factor, particularly for more vulnerable populations such as older people and post-stroke individuals. Tripping is the leading cause of falls, and the swing-phase event Minimum Foot Clearance (MFC) is recognised as the key biomechanical determinant of tripping probability. MFC is defined as the minimum swing foot clearance, which is seen approximately mid-swing, and it is routinely measured in gait biomechanics laboratories using precise, high-speed, camera-based 3D motion capture systems. For practical intervention strategies designed to predict, and possibly assist, swing foot trajectory to prevent tripping, identification of the MFC event is essential; however, no technique is currently available to determine MFC timing in real-life settings outside the laboratory. One strategy has been to use wearable sensors, such as Inertial Measurement Units (IMUs), but these data are limited to primarily providing only tri-axial linear acceleration and angular velocity. The aim of this study was to develop Machine Learning (ML) algorithms to predict MFC timing based on the preceding toe-off gait event. The ML algorithms were trained using 13 young adults’ foot trajectory data recorded from an Optotrak 3D motion capture system. A Deep Learning configuration was developed based on a Recurrent Neural Network with a Long Short-Term Memory (LSTM) architecture and Huber loss-functions to minimise MFC-timing prediction error. We succeeded in predicting MFC timing from toe-off characteristics with a mean absolute error of 0.07 s. Although further algorithm training using population-specific inputs are needed. The ML algorithms designed here can be used for real-time actuation of wearable active devices to increase foot clearance at critical MFC and reduce devastating tripping falls. Further developments in ML-guided actuation for active exoskeletons could prove highly effective in developing technologies to reduce tripping-related falls across a range of gait impaired populations. |
format | Online Article Text |
id | pubmed-9502804 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-95028042022-09-24 Using Deep Learning to Predict Minimum Foot–Ground Clearance Event from Toe-Off Kinematics Asogwa, Clement Ogugua Nagano, Hanatsu Wang, Kai Begg, Rezaul Sensors (Basel) Article Efficient, adaptive, locomotor function is critically important for maintaining our health and independence, but falls-related injuries when walking are a significant risk factor, particularly for more vulnerable populations such as older people and post-stroke individuals. Tripping is the leading cause of falls, and the swing-phase event Minimum Foot Clearance (MFC) is recognised as the key biomechanical determinant of tripping probability. MFC is defined as the minimum swing foot clearance, which is seen approximately mid-swing, and it is routinely measured in gait biomechanics laboratories using precise, high-speed, camera-based 3D motion capture systems. For practical intervention strategies designed to predict, and possibly assist, swing foot trajectory to prevent tripping, identification of the MFC event is essential; however, no technique is currently available to determine MFC timing in real-life settings outside the laboratory. One strategy has been to use wearable sensors, such as Inertial Measurement Units (IMUs), but these data are limited to primarily providing only tri-axial linear acceleration and angular velocity. The aim of this study was to develop Machine Learning (ML) algorithms to predict MFC timing based on the preceding toe-off gait event. The ML algorithms were trained using 13 young adults’ foot trajectory data recorded from an Optotrak 3D motion capture system. A Deep Learning configuration was developed based on a Recurrent Neural Network with a Long Short-Term Memory (LSTM) architecture and Huber loss-functions to minimise MFC-timing prediction error. We succeeded in predicting MFC timing from toe-off characteristics with a mean absolute error of 0.07 s. Although further algorithm training using population-specific inputs are needed. The ML algorithms designed here can be used for real-time actuation of wearable active devices to increase foot clearance at critical MFC and reduce devastating tripping falls. Further developments in ML-guided actuation for active exoskeletons could prove highly effective in developing technologies to reduce tripping-related falls across a range of gait impaired populations. MDPI 2022-09-14 /pmc/articles/PMC9502804/ /pubmed/36146308 http://dx.doi.org/10.3390/s22186960 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Asogwa, Clement Ogugua Nagano, Hanatsu Wang, Kai Begg, Rezaul Using Deep Learning to Predict Minimum Foot–Ground Clearance Event from Toe-Off Kinematics |
title | Using Deep Learning to Predict Minimum Foot–Ground Clearance Event from Toe-Off Kinematics |
title_full | Using Deep Learning to Predict Minimum Foot–Ground Clearance Event from Toe-Off Kinematics |
title_fullStr | Using Deep Learning to Predict Minimum Foot–Ground Clearance Event from Toe-Off Kinematics |
title_full_unstemmed | Using Deep Learning to Predict Minimum Foot–Ground Clearance Event from Toe-Off Kinematics |
title_short | Using Deep Learning to Predict Minimum Foot–Ground Clearance Event from Toe-Off Kinematics |
title_sort | using deep learning to predict minimum foot–ground clearance event from toe-off kinematics |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9502804/ https://www.ncbi.nlm.nih.gov/pubmed/36146308 http://dx.doi.org/10.3390/s22186960 |
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