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Deep Learning in Gait Parameter Prediction for OA and TKA Patients Wearing IMU Sensors

Quantitative assessments of patient movement quality in osteoarthritis (OA), specifically spatiotemporal gait parameters (STGPs), can provide in-depth insight into gait patterns, activity types, and changes in mobility after total knee arthroplasty (TKA). A study was conducted to benchmark the abili...

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Autores principales: Sharifi Renani, Mohsen, Myers, Casey A., Zandie, Rohola, Mahoor, Mohammad H., Davidson, Bradley S., Clary, Chadd W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7582246/
https://www.ncbi.nlm.nih.gov/pubmed/32998329
http://dx.doi.org/10.3390/s20195553
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author Sharifi Renani, Mohsen
Myers, Casey A.
Zandie, Rohola
Mahoor, Mohammad H.
Davidson, Bradley S.
Clary, Chadd W.
author_facet Sharifi Renani, Mohsen
Myers, Casey A.
Zandie, Rohola
Mahoor, Mohammad H.
Davidson, Bradley S.
Clary, Chadd W.
author_sort Sharifi Renani, Mohsen
collection PubMed
description Quantitative assessments of patient movement quality in osteoarthritis (OA), specifically spatiotemporal gait parameters (STGPs), can provide in-depth insight into gait patterns, activity types, and changes in mobility after total knee arthroplasty (TKA). A study was conducted to benchmark the ability of multiple deep neural network (DNN) architectures to predict 12 STGPs from inertial measurement unit (IMU) data and to identify an optimal sensor combination, which has yet to be studied for OA and TKA subjects. DNNs were trained using movement data from 29 subjects, walking at slow, normal, and fast paces and evaluated with cross-fold validation over the subjects. Optimal sensor locations were determined by comparing prediction accuracy with 15 IMU configurations (pelvis, thigh, shank, and feet). Percent error across the 12 STGPs ranged from 2.1% (stride time) to 73.7% (toe-out angle) and overall was more accurate in temporal parameters than spatial parameters. The most and least accurate sensor combinations were feet-thighs and singular pelvis, respectively. DNNs showed promising results in predicting STGPs for OA and TKA subjects based on signals from IMU sensors and overcomes the dependency on sensor locations that can hinder the design of patient monitoring systems for clinical application.
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spelling pubmed-75822462020-10-28 Deep Learning in Gait Parameter Prediction for OA and TKA Patients Wearing IMU Sensors Sharifi Renani, Mohsen Myers, Casey A. Zandie, Rohola Mahoor, Mohammad H. Davidson, Bradley S. Clary, Chadd W. Sensors (Basel) Article Quantitative assessments of patient movement quality in osteoarthritis (OA), specifically spatiotemporal gait parameters (STGPs), can provide in-depth insight into gait patterns, activity types, and changes in mobility after total knee arthroplasty (TKA). A study was conducted to benchmark the ability of multiple deep neural network (DNN) architectures to predict 12 STGPs from inertial measurement unit (IMU) data and to identify an optimal sensor combination, which has yet to be studied for OA and TKA subjects. DNNs were trained using movement data from 29 subjects, walking at slow, normal, and fast paces and evaluated with cross-fold validation over the subjects. Optimal sensor locations were determined by comparing prediction accuracy with 15 IMU configurations (pelvis, thigh, shank, and feet). Percent error across the 12 STGPs ranged from 2.1% (stride time) to 73.7% (toe-out angle) and overall was more accurate in temporal parameters than spatial parameters. The most and least accurate sensor combinations were feet-thighs and singular pelvis, respectively. DNNs showed promising results in predicting STGPs for OA and TKA subjects based on signals from IMU sensors and overcomes the dependency on sensor locations that can hinder the design of patient monitoring systems for clinical application. MDPI 2020-09-28 /pmc/articles/PMC7582246/ /pubmed/32998329 http://dx.doi.org/10.3390/s20195553 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
Sharifi Renani, Mohsen
Myers, Casey A.
Zandie, Rohola
Mahoor, Mohammad H.
Davidson, Bradley S.
Clary, Chadd W.
Deep Learning in Gait Parameter Prediction for OA and TKA Patients Wearing IMU Sensors
title Deep Learning in Gait Parameter Prediction for OA and TKA Patients Wearing IMU Sensors
title_full Deep Learning in Gait Parameter Prediction for OA and TKA Patients Wearing IMU Sensors
title_fullStr Deep Learning in Gait Parameter Prediction for OA and TKA Patients Wearing IMU Sensors
title_full_unstemmed Deep Learning in Gait Parameter Prediction for OA and TKA Patients Wearing IMU Sensors
title_short Deep Learning in Gait Parameter Prediction for OA and TKA Patients Wearing IMU Sensors
title_sort deep learning in gait parameter prediction for oa and tka patients wearing imu sensors
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7582246/
https://www.ncbi.nlm.nih.gov/pubmed/32998329
http://dx.doi.org/10.3390/s20195553
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