Cargando…
Gait-Based Diplegia Classification Using LSMT Networks
Diplegia is a specific subcategory of the wide spectrum of motion disorders gathered under the name of cerebral palsy. Recent works proposed to use gait analysis for diplegia classification paving the way for automated analysis. A clinically established gait-based classification system divides diple...
Autores principales: | , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6360037/ https://www.ncbi.nlm.nih.gov/pubmed/30800255 http://dx.doi.org/10.1155/2019/3796898 |
_version_ | 1783392393722068992 |
---|---|
author | Ferrari, Alberto Bergamini, Luca Guerzoni, Giorgio Calderara, Simone Bicocchi, Nicola Vitetta, Giorgio Borghi, Corrado Neviani, Rita Ferrari, Adriano |
author_facet | Ferrari, Alberto Bergamini, Luca Guerzoni, Giorgio Calderara, Simone Bicocchi, Nicola Vitetta, Giorgio Borghi, Corrado Neviani, Rita Ferrari, Adriano |
author_sort | Ferrari, Alberto |
collection | PubMed |
description | Diplegia is a specific subcategory of the wide spectrum of motion disorders gathered under the name of cerebral palsy. Recent works proposed to use gait analysis for diplegia classification paving the way for automated analysis. A clinically established gait-based classification system divides diplegic patients into 4 main forms, each one associated with a peculiar walking pattern. In this work, we apply two different deep learning techniques, namely, multilayer perceptron and recurrent neural networks, to automatically classify children into the 4 clinical forms. For the analysis, we used a dataset comprising gait data of 174 patients collected by means of an optoelectronic system. The measurements describing walking patterns have been processed to extract 27 angular parameters and then used to train both kinds of neural networks. Classification results are comparable with those provided by experts in 3 out of 4 forms. |
format | Online Article Text |
id | pubmed-6360037 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-63600372019-02-24 Gait-Based Diplegia Classification Using LSMT Networks Ferrari, Alberto Bergamini, Luca Guerzoni, Giorgio Calderara, Simone Bicocchi, Nicola Vitetta, Giorgio Borghi, Corrado Neviani, Rita Ferrari, Adriano J Healthc Eng Research Article Diplegia is a specific subcategory of the wide spectrum of motion disorders gathered under the name of cerebral palsy. Recent works proposed to use gait analysis for diplegia classification paving the way for automated analysis. A clinically established gait-based classification system divides diplegic patients into 4 main forms, each one associated with a peculiar walking pattern. In this work, we apply two different deep learning techniques, namely, multilayer perceptron and recurrent neural networks, to automatically classify children into the 4 clinical forms. For the analysis, we used a dataset comprising gait data of 174 patients collected by means of an optoelectronic system. The measurements describing walking patterns have been processed to extract 27 angular parameters and then used to train both kinds of neural networks. Classification results are comparable with those provided by experts in 3 out of 4 forms. Hindawi 2019-01-17 /pmc/articles/PMC6360037/ /pubmed/30800255 http://dx.doi.org/10.1155/2019/3796898 Text en Copyright © 2019 Alberto Ferrari et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Ferrari, Alberto Bergamini, Luca Guerzoni, Giorgio Calderara, Simone Bicocchi, Nicola Vitetta, Giorgio Borghi, Corrado Neviani, Rita Ferrari, Adriano Gait-Based Diplegia Classification Using LSMT Networks |
title | Gait-Based Diplegia Classification Using LSMT Networks |
title_full | Gait-Based Diplegia Classification Using LSMT Networks |
title_fullStr | Gait-Based Diplegia Classification Using LSMT Networks |
title_full_unstemmed | Gait-Based Diplegia Classification Using LSMT Networks |
title_short | Gait-Based Diplegia Classification Using LSMT Networks |
title_sort | gait-based diplegia classification using lsmt networks |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6360037/ https://www.ncbi.nlm.nih.gov/pubmed/30800255 http://dx.doi.org/10.1155/2019/3796898 |
work_keys_str_mv | AT ferrarialberto gaitbaseddiplegiaclassificationusinglsmtnetworks AT bergaminiluca gaitbaseddiplegiaclassificationusinglsmtnetworks AT guerzonigiorgio gaitbaseddiplegiaclassificationusinglsmtnetworks AT calderarasimone gaitbaseddiplegiaclassificationusinglsmtnetworks AT bicocchinicola gaitbaseddiplegiaclassificationusinglsmtnetworks AT vitettagiorgio gaitbaseddiplegiaclassificationusinglsmtnetworks AT borghicorrado gaitbaseddiplegiaclassificationusinglsmtnetworks AT nevianirita gaitbaseddiplegiaclassificationusinglsmtnetworks AT ferrariadriano gaitbaseddiplegiaclassificationusinglsmtnetworks |