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NE-Motion: Visual Analysis of Stroke Patients Using Motion Sensor Networks
A large number of stroke survivors suffer from a significant decrease in upper extremity (UE) function, requiring rehabilitation therapy to boost recovery of UE motion. Assessing the efficacy of treatment strategies is a challenging problem in this context, and is typically accomplished by observing...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8271972/ https://www.ncbi.nlm.nih.gov/pubmed/34208996 http://dx.doi.org/10.3390/s21134482 |
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author | Contreras, Rodrigo Colnago Parnandi, Avinash Coelho, Bruno Gomes Silva, Claudio Schambra, Heidi Nonato, Luis Gustavo |
author_facet | Contreras, Rodrigo Colnago Parnandi, Avinash Coelho, Bruno Gomes Silva, Claudio Schambra, Heidi Nonato, Luis Gustavo |
author_sort | Contreras, Rodrigo Colnago |
collection | PubMed |
description | A large number of stroke survivors suffer from a significant decrease in upper extremity (UE) function, requiring rehabilitation therapy to boost recovery of UE motion. Assessing the efficacy of treatment strategies is a challenging problem in this context, and is typically accomplished by observing the performance of patients during their execution of daily activities. A more detailed assessment of UE impairment can be undertaken with a clinical bedside test, the UE Fugl–Meyer Assessment, but it fails to examine compensatory movements of functioning body segments that are used to bypass impairment. In this work, we use a graph learning method to build a visualization tool tailored to support the analysis of stroke patients. Called NE-Motion, or Network Environment for Motion Capture Data Analysis, the proposed analytic tool handles a set of time series captured by motion sensors worn by patients so as to enable visual analytic resources to identify abnormalities in movement patterns. Developed in close collaboration with domain experts, NE-Motion is capable of uncovering important phenomena, such as compensation while revealing differences between stroke patients and healthy individuals. The effectiveness of NE-Motion is shown in two case studies designed to analyze particular patients and to compare groups of subjects. |
format | Online Article Text |
id | pubmed-8271972 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-82719722021-07-11 NE-Motion: Visual Analysis of Stroke Patients Using Motion Sensor Networks Contreras, Rodrigo Colnago Parnandi, Avinash Coelho, Bruno Gomes Silva, Claudio Schambra, Heidi Nonato, Luis Gustavo Sensors (Basel) Article A large number of stroke survivors suffer from a significant decrease in upper extremity (UE) function, requiring rehabilitation therapy to boost recovery of UE motion. Assessing the efficacy of treatment strategies is a challenging problem in this context, and is typically accomplished by observing the performance of patients during their execution of daily activities. A more detailed assessment of UE impairment can be undertaken with a clinical bedside test, the UE Fugl–Meyer Assessment, but it fails to examine compensatory movements of functioning body segments that are used to bypass impairment. In this work, we use a graph learning method to build a visualization tool tailored to support the analysis of stroke patients. Called NE-Motion, or Network Environment for Motion Capture Data Analysis, the proposed analytic tool handles a set of time series captured by motion sensors worn by patients so as to enable visual analytic resources to identify abnormalities in movement patterns. Developed in close collaboration with domain experts, NE-Motion is capable of uncovering important phenomena, such as compensation while revealing differences between stroke patients and healthy individuals. The effectiveness of NE-Motion is shown in two case studies designed to analyze particular patients and to compare groups of subjects. MDPI 2021-06-30 /pmc/articles/PMC8271972/ /pubmed/34208996 http://dx.doi.org/10.3390/s21134482 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Contreras, Rodrigo Colnago Parnandi, Avinash Coelho, Bruno Gomes Silva, Claudio Schambra, Heidi Nonato, Luis Gustavo NE-Motion: Visual Analysis of Stroke Patients Using Motion Sensor Networks |
title | NE-Motion: Visual Analysis of Stroke Patients Using Motion Sensor Networks |
title_full | NE-Motion: Visual Analysis of Stroke Patients Using Motion Sensor Networks |
title_fullStr | NE-Motion: Visual Analysis of Stroke Patients Using Motion Sensor Networks |
title_full_unstemmed | NE-Motion: Visual Analysis of Stroke Patients Using Motion Sensor Networks |
title_short | NE-Motion: Visual Analysis of Stroke Patients Using Motion Sensor Networks |
title_sort | ne-motion: visual analysis of stroke patients using motion sensor networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8271972/ https://www.ncbi.nlm.nih.gov/pubmed/34208996 http://dx.doi.org/10.3390/s21134482 |
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