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A novel approach for modelling and classifying sit-to-stand kinematics using inertial sensors

Sit-to-stand transitions are an important part of activities of daily living and play a key role in functional mobility in humans. The sit-to-stand movement is often affected in older adults due to frailty and in patients with motor impairments such as Parkinson’s disease leading to falls. Studying...

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Autores principales: Wairagkar, Maitreyee, Villeneuve, Emma, King, Rachel, Janko, Balazs, Burnett, Malcolm, Agarwal, Veena, Kunkel, Dorit, Ashburn, Ann, Sherratt, R. Simon, Holderbaum, William, Harwin, William S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9578638/
https://www.ncbi.nlm.nih.gov/pubmed/36256622
http://dx.doi.org/10.1371/journal.pone.0264126
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author Wairagkar, Maitreyee
Villeneuve, Emma
King, Rachel
Janko, Balazs
Burnett, Malcolm
Agarwal, Veena
Kunkel, Dorit
Ashburn, Ann
Sherratt, R. Simon
Holderbaum, William
Harwin, William S.
author_facet Wairagkar, Maitreyee
Villeneuve, Emma
King, Rachel
Janko, Balazs
Burnett, Malcolm
Agarwal, Veena
Kunkel, Dorit
Ashburn, Ann
Sherratt, R. Simon
Holderbaum, William
Harwin, William S.
author_sort Wairagkar, Maitreyee
collection PubMed
description Sit-to-stand transitions are an important part of activities of daily living and play a key role in functional mobility in humans. The sit-to-stand movement is often affected in older adults due to frailty and in patients with motor impairments such as Parkinson’s disease leading to falls. Studying kinematics of sit-to-stand transitions can provide insight in assessment, monitoring and developing rehabilitation strategies for the affected populations. We propose a three-segment body model for estimating sit-to-stand kinematics using only two wearable inertial sensors, placed on the shank and back. Reducing the number of sensors to two instead of one per body segment facilitates monitoring and classifying movements over extended periods, making it more comfortable to wear while reducing the power requirements of sensors. We applied this model on 10 younger healthy adults (YH), 12 older healthy adults (OH) and 12 people with Parkinson’s disease (PwP). We have achieved this by incorporating unique sit-to-stand classification technique using unsupervised learning in the model based reconstruction of angular kinematics using extended Kalman filter. Our proposed model showed that it was possible to successfully estimate thigh kinematics despite not measuring the thigh motion with inertial sensor. We classified sit-to-stand transitions, sitting and standing states with the accuracies of 98.67%, 94.20% and 91.41% for YH, OH and PwP respectively. We have proposed a novel integrated approach of modelling and classification for estimating the body kinematics during sit-to-stand motion and successfully applied it on YH, OH and PwP groups.
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spelling pubmed-95786382022-10-19 A novel approach for modelling and classifying sit-to-stand kinematics using inertial sensors Wairagkar, Maitreyee Villeneuve, Emma King, Rachel Janko, Balazs Burnett, Malcolm Agarwal, Veena Kunkel, Dorit Ashburn, Ann Sherratt, R. Simon Holderbaum, William Harwin, William S. PLoS One Research Article Sit-to-stand transitions are an important part of activities of daily living and play a key role in functional mobility in humans. The sit-to-stand movement is often affected in older adults due to frailty and in patients with motor impairments such as Parkinson’s disease leading to falls. Studying kinematics of sit-to-stand transitions can provide insight in assessment, monitoring and developing rehabilitation strategies for the affected populations. We propose a three-segment body model for estimating sit-to-stand kinematics using only two wearable inertial sensors, placed on the shank and back. Reducing the number of sensors to two instead of one per body segment facilitates monitoring and classifying movements over extended periods, making it more comfortable to wear while reducing the power requirements of sensors. We applied this model on 10 younger healthy adults (YH), 12 older healthy adults (OH) and 12 people with Parkinson’s disease (PwP). We have achieved this by incorporating unique sit-to-stand classification technique using unsupervised learning in the model based reconstruction of angular kinematics using extended Kalman filter. Our proposed model showed that it was possible to successfully estimate thigh kinematics despite not measuring the thigh motion with inertial sensor. We classified sit-to-stand transitions, sitting and standing states with the accuracies of 98.67%, 94.20% and 91.41% for YH, OH and PwP respectively. We have proposed a novel integrated approach of modelling and classification for estimating the body kinematics during sit-to-stand motion and successfully applied it on YH, OH and PwP groups. Public Library of Science 2022-10-18 /pmc/articles/PMC9578638/ /pubmed/36256622 http://dx.doi.org/10.1371/journal.pone.0264126 Text en © 2022 Wairagkar et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Wairagkar, Maitreyee
Villeneuve, Emma
King, Rachel
Janko, Balazs
Burnett, Malcolm
Agarwal, Veena
Kunkel, Dorit
Ashburn, Ann
Sherratt, R. Simon
Holderbaum, William
Harwin, William S.
A novel approach for modelling and classifying sit-to-stand kinematics using inertial sensors
title A novel approach for modelling and classifying sit-to-stand kinematics using inertial sensors
title_full A novel approach for modelling and classifying sit-to-stand kinematics using inertial sensors
title_fullStr A novel approach for modelling and classifying sit-to-stand kinematics using inertial sensors
title_full_unstemmed A novel approach for modelling and classifying sit-to-stand kinematics using inertial sensors
title_short A novel approach for modelling and classifying sit-to-stand kinematics using inertial sensors
title_sort novel approach for modelling and classifying sit-to-stand kinematics using inertial sensors
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9578638/
https://www.ncbi.nlm.nih.gov/pubmed/36256622
http://dx.doi.org/10.1371/journal.pone.0264126
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