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Artificial Neural Network Analyzing Wearable Device Gait Data for Identifying Patients With Stroke Unable to Return to Work
A potential dramatic effect of long-term disability due to stroke is the inability to return to work. An accurate prognosis and the identification of the parameters inflating the possibility of return to work after neurorehabilitation are crucial. Many factors may influence it, such as mobility and,...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8170310/ https://www.ncbi.nlm.nih.gov/pubmed/34093396 http://dx.doi.org/10.3389/fneur.2021.650542 |
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author | Iosa, Marco Capodaglio, Edda Pelà, Silvia Persechino, Benedetta Morone, Giovanni Antonucci, Gabriella Paolucci, Stefano Panigazzi, Monica |
author_facet | Iosa, Marco Capodaglio, Edda Pelà, Silvia Persechino, Benedetta Morone, Giovanni Antonucci, Gabriella Paolucci, Stefano Panigazzi, Monica |
author_sort | Iosa, Marco |
collection | PubMed |
description | A potential dramatic effect of long-term disability due to stroke is the inability to return to work. An accurate prognosis and the identification of the parameters inflating the possibility of return to work after neurorehabilitation are crucial. Many factors may influence it, such as mobility and, in particular, walking ability. In this pilot study, two emerging technologies have been combined with the aim of developing a prognostic tool for identifying patients able to return to work: a wearable inertial measurement unit for gait analysis and an artificial neural network (ANN). Compared with more conventional statistics, the ANN showed a higher accuracy in identifying patients with respect to healthy subjects (90.9 vs. 75.8%) and also in identifying the subjects unable to return to work (93.9 vs. 81.8%). In this last analysis, the duration of double support phase resulted the most important input of the ANN. The potentiality of the ANN, developed also in other fields such as marketing on social networks, could allow a powerful support for clinicians that today should manage a large amount of instrumentally recorded parameters in patients with stroke. |
format | Online Article Text |
id | pubmed-8170310 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-81703102021-06-03 Artificial Neural Network Analyzing Wearable Device Gait Data for Identifying Patients With Stroke Unable to Return to Work Iosa, Marco Capodaglio, Edda Pelà, Silvia Persechino, Benedetta Morone, Giovanni Antonucci, Gabriella Paolucci, Stefano Panigazzi, Monica Front Neurol Neurology A potential dramatic effect of long-term disability due to stroke is the inability to return to work. An accurate prognosis and the identification of the parameters inflating the possibility of return to work after neurorehabilitation are crucial. Many factors may influence it, such as mobility and, in particular, walking ability. In this pilot study, two emerging technologies have been combined with the aim of developing a prognostic tool for identifying patients able to return to work: a wearable inertial measurement unit for gait analysis and an artificial neural network (ANN). Compared with more conventional statistics, the ANN showed a higher accuracy in identifying patients with respect to healthy subjects (90.9 vs. 75.8%) and also in identifying the subjects unable to return to work (93.9 vs. 81.8%). In this last analysis, the duration of double support phase resulted the most important input of the ANN. The potentiality of the ANN, developed also in other fields such as marketing on social networks, could allow a powerful support for clinicians that today should manage a large amount of instrumentally recorded parameters in patients with stroke. Frontiers Media S.A. 2021-05-19 /pmc/articles/PMC8170310/ /pubmed/34093396 http://dx.doi.org/10.3389/fneur.2021.650542 Text en Copyright © 2021 Iosa, Capodaglio, Pelà, Persechino, Morone, Antonucci, Paolucci and Panigazzi. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neurology Iosa, Marco Capodaglio, Edda Pelà, Silvia Persechino, Benedetta Morone, Giovanni Antonucci, Gabriella Paolucci, Stefano Panigazzi, Monica Artificial Neural Network Analyzing Wearable Device Gait Data for Identifying Patients With Stroke Unable to Return to Work |
title | Artificial Neural Network Analyzing Wearable Device Gait Data for Identifying Patients With Stroke Unable to Return to Work |
title_full | Artificial Neural Network Analyzing Wearable Device Gait Data for Identifying Patients With Stroke Unable to Return to Work |
title_fullStr | Artificial Neural Network Analyzing Wearable Device Gait Data for Identifying Patients With Stroke Unable to Return to Work |
title_full_unstemmed | Artificial Neural Network Analyzing Wearable Device Gait Data for Identifying Patients With Stroke Unable to Return to Work |
title_short | Artificial Neural Network Analyzing Wearable Device Gait Data for Identifying Patients With Stroke Unable to Return to Work |
title_sort | artificial neural network analyzing wearable device gait data for identifying patients with stroke unable to return to work |
topic | Neurology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8170310/ https://www.ncbi.nlm.nih.gov/pubmed/34093396 http://dx.doi.org/10.3389/fneur.2021.650542 |
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