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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...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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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. |
format | Online Article Text |
id | pubmed-9578638 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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