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Use of a Low-Cost, Chest-Mounted Accelerometer to Evaluate Transfer Skills of Wheelchair Users During Everyday Activities: Observational Study

BACKGROUND: Transfers are an important skill for many wheelchair users (WU). However, they have also been related to the risk of falling or developing upper limb injuries. Transfer abilities are usually evaluated in clinical settings or biomechanics laboratories, and these methods of assessment are...

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Autores principales: Barbareschi, Giulia, Holloway, Catherine, Bianchi-Berthouze, Nadia, Sonenblum, Sharon, Sprigle, Stephen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6320409/
https://www.ncbi.nlm.nih.gov/pubmed/30573447
http://dx.doi.org/10.2196/11748
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author Barbareschi, Giulia
Holloway, Catherine
Bianchi-Berthouze, Nadia
Sonenblum, Sharon
Sprigle, Stephen
author_facet Barbareschi, Giulia
Holloway, Catherine
Bianchi-Berthouze, Nadia
Sonenblum, Sharon
Sprigle, Stephen
author_sort Barbareschi, Giulia
collection PubMed
description BACKGROUND: Transfers are an important skill for many wheelchair users (WU). However, they have also been related to the risk of falling or developing upper limb injuries. Transfer abilities are usually evaluated in clinical settings or biomechanics laboratories, and these methods of assessment are poorly suited to evaluation in real and unconstrained world settings where transfers take place. OBJECTIVE: The objective of this paper is to test the feasibility of a system based on a wearable low-cost sensor to monitor transfer skills in real-world settings. METHODS: We collected data from 9 WU wearing triaxial accelerometer on their chest while performing transfers to and from car seats and home furniture. We then extracted significant features from accelerometer data based on biomechanical considerations and previous relevant literature and used machine learning algorithms to evaluate the performance of wheelchair transfers and detect their occurrence from a continuous time series of data. RESULTS: Results show a good predictive accuracy of support vector machine classifiers when determining the use of head-hip relationship (75.9%) and smoothness of landing (79.6%) when the starting and ending of the transfer are known. Automatic transfer detection reaches performances that are similar to state of the art in this context (multinomial logistic regression accuracy 87.8%). However, we achieve these results using only a single sensor and collecting data in a more ecological manner. CONCLUSIONS: The use of a single chest-placed accelerometer shows good predictive accuracy for algorithms applied independently to both transfer evaluation and monitoring. This points to the opportunity for designing ubiquitous-technology based personalized skill development interventions for WU. However, monitoring transfers still require the use of external inputs or extra sensors to identify the start and end of the transfer, which is needed to perform an accurate evaluation.
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spelling pubmed-63204092019-01-28 Use of a Low-Cost, Chest-Mounted Accelerometer to Evaluate Transfer Skills of Wheelchair Users During Everyday Activities: Observational Study Barbareschi, Giulia Holloway, Catherine Bianchi-Berthouze, Nadia Sonenblum, Sharon Sprigle, Stephen JMIR Rehabil Assist Technol Original Paper BACKGROUND: Transfers are an important skill for many wheelchair users (WU). However, they have also been related to the risk of falling or developing upper limb injuries. Transfer abilities are usually evaluated in clinical settings or biomechanics laboratories, and these methods of assessment are poorly suited to evaluation in real and unconstrained world settings where transfers take place. OBJECTIVE: The objective of this paper is to test the feasibility of a system based on a wearable low-cost sensor to monitor transfer skills in real-world settings. METHODS: We collected data from 9 WU wearing triaxial accelerometer on their chest while performing transfers to and from car seats and home furniture. We then extracted significant features from accelerometer data based on biomechanical considerations and previous relevant literature and used machine learning algorithms to evaluate the performance of wheelchair transfers and detect their occurrence from a continuous time series of data. RESULTS: Results show a good predictive accuracy of support vector machine classifiers when determining the use of head-hip relationship (75.9%) and smoothness of landing (79.6%) when the starting and ending of the transfer are known. Automatic transfer detection reaches performances that are similar to state of the art in this context (multinomial logistic regression accuracy 87.8%). However, we achieve these results using only a single sensor and collecting data in a more ecological manner. CONCLUSIONS: The use of a single chest-placed accelerometer shows good predictive accuracy for algorithms applied independently to both transfer evaluation and monitoring. This points to the opportunity for designing ubiquitous-technology based personalized skill development interventions for WU. However, monitoring transfers still require the use of external inputs or extra sensors to identify the start and end of the transfer, which is needed to perform an accurate evaluation. JMIR Publications 2018-12-20 /pmc/articles/PMC6320409/ /pubmed/30573447 http://dx.doi.org/10.2196/11748 Text en ©Giulia Barbareschi, Catherine Holloway, Nadia Bianchi-Berthouze, Sharon Sonenblum, Stephen Sprigle. Originally published in JMIR Rehabilitation and Assistive Technology (http://rehab.jmir.org), 20.12.2018. 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 work, first published in JMIR Rehabilitation and Assistive Technology, is properly cited. The complete bibliographic information, a link to the original publication on http://rehab.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Barbareschi, Giulia
Holloway, Catherine
Bianchi-Berthouze, Nadia
Sonenblum, Sharon
Sprigle, Stephen
Use of a Low-Cost, Chest-Mounted Accelerometer to Evaluate Transfer Skills of Wheelchair Users During Everyday Activities: Observational Study
title Use of a Low-Cost, Chest-Mounted Accelerometer to Evaluate Transfer Skills of Wheelchair Users During Everyday Activities: Observational Study
title_full Use of a Low-Cost, Chest-Mounted Accelerometer to Evaluate Transfer Skills of Wheelchair Users During Everyday Activities: Observational Study
title_fullStr Use of a Low-Cost, Chest-Mounted Accelerometer to Evaluate Transfer Skills of Wheelchair Users During Everyday Activities: Observational Study
title_full_unstemmed Use of a Low-Cost, Chest-Mounted Accelerometer to Evaluate Transfer Skills of Wheelchair Users During Everyday Activities: Observational Study
title_short Use of a Low-Cost, Chest-Mounted Accelerometer to Evaluate Transfer Skills of Wheelchair Users During Everyday Activities: Observational Study
title_sort use of a low-cost, chest-mounted accelerometer to evaluate transfer skills of wheelchair users during everyday activities: observational study
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6320409/
https://www.ncbi.nlm.nih.gov/pubmed/30573447
http://dx.doi.org/10.2196/11748
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