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Robotic Kinematic measures of the arm in chronic Stroke: part 2 – strong correlation with clinical outcome measures
BACKGROUND: A detailed sensorimotor evaluation is essential in planning effective, individualized therapy post-stroke. Robotic kinematic assay may offer better accuracy and resolution to understand stroke recovery. Here we investigate the added value of distal wrist measurement to a proximal robotic...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8715630/ https://www.ncbi.nlm.nih.gov/pubmed/34963502 http://dx.doi.org/10.1186/s42234-021-00082-8 |
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author | Moretti, Caio B. Hamilton, Taya Edwards, Dylan J. Peltz, Avrielle Rykman Chang, Johanna L. Cortes, Mar Delbe, Alexandre C. B. Volpe, Bruce T. Krebs, Hermano I. |
author_facet | Moretti, Caio B. Hamilton, Taya Edwards, Dylan J. Peltz, Avrielle Rykman Chang, Johanna L. Cortes, Mar Delbe, Alexandre C. B. Volpe, Bruce T. Krebs, Hermano I. |
author_sort | Moretti, Caio B. |
collection | PubMed |
description | BACKGROUND: A detailed sensorimotor evaluation is essential in planning effective, individualized therapy post-stroke. Robotic kinematic assay may offer better accuracy and resolution to understand stroke recovery. Here we investigate the added value of distal wrist measurement to a proximal robotic kinematic assay to improve its correlation with clinical upper extremity measures in chronic stroke. Secondly, we compare linear and nonlinear regression models. METHODS: Data was sourced from a multicenter randomized controlled trial conducted from 2012 to 2016, investigating the combined effect of robotic therapy and transcranial direct current stimulation (tDCS). 24 kinematic metrics were derived from 4 shoulder-elbow tasks and 35 metrics from 3 wrist and forearm evaluation tasks. A correlation-based feature selection was performed, keeping only features substantially correlated with the target attribute (R > 0.5.) Nonlinear models took the form of a multilayer perceptron neural network: one hidden layer and one linear output. RESULTS: Shoulder-elbow metrics showed a significant correlation with the Fugl Meyer Assessment (upper extremity, FMA-UE), with a R = 0.82 (P < 0.001) for the linear model and R = 0.88 (P < 0.001) for the nonlinear model. Similarly, a high correlation was found for wrist kinematics and the FMA-UE (R = 0.91 (P < 0.001) and R = 0.92 (P < 0.001) for the linear and nonlinear model respectively). The combined analysis produced a correlation of R = 0.91 (P < 0.001) for the linear model and R = 0.91 (P < 0.001) for the nonlinear model. CONCLUSIONS: Distal wrist kinematics were highly correlated to clinical outcomes, warranting future investigation to explore our nonlinear wrist model with acute or subacute stroke populations. TRIAL REGISTRATION: http://www.clinicaltrials.gov. Actual study start date September 2012. First registered on 15 November 2012. Retrospectively registered. Unique identifiers: NCT01726673 and NCT03562663. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s42234-021-00082-8. |
format | Online Article Text |
id | pubmed-8715630 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-87156302022-01-05 Robotic Kinematic measures of the arm in chronic Stroke: part 2 – strong correlation with clinical outcome measures Moretti, Caio B. Hamilton, Taya Edwards, Dylan J. Peltz, Avrielle Rykman Chang, Johanna L. Cortes, Mar Delbe, Alexandre C. B. Volpe, Bruce T. Krebs, Hermano I. Bioelectron Med Research Article BACKGROUND: A detailed sensorimotor evaluation is essential in planning effective, individualized therapy post-stroke. Robotic kinematic assay may offer better accuracy and resolution to understand stroke recovery. Here we investigate the added value of distal wrist measurement to a proximal robotic kinematic assay to improve its correlation with clinical upper extremity measures in chronic stroke. Secondly, we compare linear and nonlinear regression models. METHODS: Data was sourced from a multicenter randomized controlled trial conducted from 2012 to 2016, investigating the combined effect of robotic therapy and transcranial direct current stimulation (tDCS). 24 kinematic metrics were derived from 4 shoulder-elbow tasks and 35 metrics from 3 wrist and forearm evaluation tasks. A correlation-based feature selection was performed, keeping only features substantially correlated with the target attribute (R > 0.5.) Nonlinear models took the form of a multilayer perceptron neural network: one hidden layer and one linear output. RESULTS: Shoulder-elbow metrics showed a significant correlation with the Fugl Meyer Assessment (upper extremity, FMA-UE), with a R = 0.82 (P < 0.001) for the linear model and R = 0.88 (P < 0.001) for the nonlinear model. Similarly, a high correlation was found for wrist kinematics and the FMA-UE (R = 0.91 (P < 0.001) and R = 0.92 (P < 0.001) for the linear and nonlinear model respectively). The combined analysis produced a correlation of R = 0.91 (P < 0.001) for the linear model and R = 0.91 (P < 0.001) for the nonlinear model. CONCLUSIONS: Distal wrist kinematics were highly correlated to clinical outcomes, warranting future investigation to explore our nonlinear wrist model with acute or subacute stroke populations. TRIAL REGISTRATION: http://www.clinicaltrials.gov. Actual study start date September 2012. First registered on 15 November 2012. Retrospectively registered. Unique identifiers: NCT01726673 and NCT03562663. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s42234-021-00082-8. BioMed Central 2021-12-29 /pmc/articles/PMC8715630/ /pubmed/34963502 http://dx.doi.org/10.1186/s42234-021-00082-8 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Research Article Moretti, Caio B. Hamilton, Taya Edwards, Dylan J. Peltz, Avrielle Rykman Chang, Johanna L. Cortes, Mar Delbe, Alexandre C. B. Volpe, Bruce T. Krebs, Hermano I. Robotic Kinematic measures of the arm in chronic Stroke: part 2 – strong correlation with clinical outcome measures |
title | Robotic Kinematic measures of the arm in chronic Stroke: part 2 – strong correlation with clinical outcome measures |
title_full | Robotic Kinematic measures of the arm in chronic Stroke: part 2 – strong correlation with clinical outcome measures |
title_fullStr | Robotic Kinematic measures of the arm in chronic Stroke: part 2 – strong correlation with clinical outcome measures |
title_full_unstemmed | Robotic Kinematic measures of the arm in chronic Stroke: part 2 – strong correlation with clinical outcome measures |
title_short | Robotic Kinematic measures of the arm in chronic Stroke: part 2 – strong correlation with clinical outcome measures |
title_sort | robotic kinematic measures of the arm in chronic stroke: part 2 – strong correlation with clinical outcome measures |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8715630/ https://www.ncbi.nlm.nih.gov/pubmed/34963502 http://dx.doi.org/10.1186/s42234-021-00082-8 |
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