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Physical Behavior in Older Persons during Daily Life: Insights from Instrumented Shoes
Activity level and gait parameters during daily life are important indicators for clinicians because they can provide critical insights into modifications of mobility and function over time. Wearable activity monitoring has been gaining momentum in daily life health assessment. Consequently, this st...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5017390/ https://www.ncbi.nlm.nih.gov/pubmed/27527172 http://dx.doi.org/10.3390/s16081225 |
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author | Moufawad el Achkar, Christopher Lenoble-Hoskovec, Constanze Paraschiv-Ionescu, Anisoara Major, Kristof Büla, Christophe Aminian, Kamiar |
author_facet | Moufawad el Achkar, Christopher Lenoble-Hoskovec, Constanze Paraschiv-Ionescu, Anisoara Major, Kristof Büla, Christophe Aminian, Kamiar |
author_sort | Moufawad el Achkar, Christopher |
collection | PubMed |
description | Activity level and gait parameters during daily life are important indicators for clinicians because they can provide critical insights into modifications of mobility and function over time. Wearable activity monitoring has been gaining momentum in daily life health assessment. Consequently, this study seeks to validate an algorithm for the classification of daily life activities and to provide a detailed gait analysis in older adults. A system consisting of an inertial sensor combined with a pressure sensing insole has been developed. Using an algorithm that we previously validated during a semi structured protocol, activities in 10 healthy elderly participants were recorded and compared to a wearable reference system over a 4 h recording period at home. Detailed gait parameters were calculated from inertial sensors. Dynamics of physical behavior were characterized using barcodes that express the measure of behavioral complexity. Activity classification based on the algorithm led to a 93% accuracy in classifying basic activities of daily life, i.e., sitting, standing, and walking. Gait analysis emphasizes the importance of metrics such as foot clearance in daily life assessment. Results also underline that measures of physical behavior and gait performance are complementary, especially since gait parameters were not correlated to complexity. Participants gave positive feedback regarding the use of the instrumented shoes. These results extend previous observations in showing the concurrent validity of the instrumented shoes compared to a body-worn reference system for daily-life physical behavior monitoring in older adults. |
format | Online Article Text |
id | pubmed-5017390 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-50173902016-09-22 Physical Behavior in Older Persons during Daily Life: Insights from Instrumented Shoes Moufawad el Achkar, Christopher Lenoble-Hoskovec, Constanze Paraschiv-Ionescu, Anisoara Major, Kristof Büla, Christophe Aminian, Kamiar Sensors (Basel) Article Activity level and gait parameters during daily life are important indicators for clinicians because they can provide critical insights into modifications of mobility and function over time. Wearable activity monitoring has been gaining momentum in daily life health assessment. Consequently, this study seeks to validate an algorithm for the classification of daily life activities and to provide a detailed gait analysis in older adults. A system consisting of an inertial sensor combined with a pressure sensing insole has been developed. Using an algorithm that we previously validated during a semi structured protocol, activities in 10 healthy elderly participants were recorded and compared to a wearable reference system over a 4 h recording period at home. Detailed gait parameters were calculated from inertial sensors. Dynamics of physical behavior were characterized using barcodes that express the measure of behavioral complexity. Activity classification based on the algorithm led to a 93% accuracy in classifying basic activities of daily life, i.e., sitting, standing, and walking. Gait analysis emphasizes the importance of metrics such as foot clearance in daily life assessment. Results also underline that measures of physical behavior and gait performance are complementary, especially since gait parameters were not correlated to complexity. Participants gave positive feedback regarding the use of the instrumented shoes. These results extend previous observations in showing the concurrent validity of the instrumented shoes compared to a body-worn reference system for daily-life physical behavior monitoring in older adults. MDPI 2016-08-03 /pmc/articles/PMC5017390/ /pubmed/27527172 http://dx.doi.org/10.3390/s16081225 Text en © 2016 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 Moufawad el Achkar, Christopher Lenoble-Hoskovec, Constanze Paraschiv-Ionescu, Anisoara Major, Kristof Büla, Christophe Aminian, Kamiar Physical Behavior in Older Persons during Daily Life: Insights from Instrumented Shoes |
title | Physical Behavior in Older Persons during Daily Life: Insights from Instrumented Shoes |
title_full | Physical Behavior in Older Persons during Daily Life: Insights from Instrumented Shoes |
title_fullStr | Physical Behavior in Older Persons during Daily Life: Insights from Instrumented Shoes |
title_full_unstemmed | Physical Behavior in Older Persons during Daily Life: Insights from Instrumented Shoes |
title_short | Physical Behavior in Older Persons during Daily Life: Insights from Instrumented Shoes |
title_sort | physical behavior in older persons during daily life: insights from instrumented shoes |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5017390/ https://www.ncbi.nlm.nih.gov/pubmed/27527172 http://dx.doi.org/10.3390/s16081225 |
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