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.

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. - In: JOURNAL OF HEALTHCARE ENGINEERING. - ISSN 2040-2295. - 2019:(2019), pp. 1-8. [10.1155/2019/3796898]

Gait-Based Diplegia Classification Using LSMT Networks

Ferrari, Alberto;Bergamini, Luca;Calderara, Simone;Bicocchi, Nicola
;
Vitetta, Giorgio;Neviani, Rita;Ferrari, Adriano
2019

Abstract

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.
2019
1
8
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. - In: JOURNAL OF HEALTHCARE ENGINEERING. - ISSN 2040-2295. - 2019:(2019), pp. 1-8. [10.1155/2019/3796898]
Ferrari, Alberto; Bergamini, Luca; Guerzoni, Giorgio; Calderara, Simone; Bicocchi, Nicola; Vitetta, Giorgio; Borghi, Corrado; Neviani, Rita; Ferrari, Adriano
File in questo prodotto:
File Dimensione Formato  
3796898.pdf

accesso aperto

Descrizione: Articolo principale
Tipologia: Versione dell'editore (versione pubblicata)
Dimensione 1.48 MB
Formato Adobe PDF
1.48 MB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

Caricamento pubblicazioni consigliate

Licenza Creative Commons
I metadati presenti in IRIS UNIMORE sono rilasciati con licenza Creative Commons CC0 1.0 Universal, mentre i file delle pubblicazioni sono rilasciati con licenza Attribuzione 4.0 Internazionale (CC BY 4.0), salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1169976
Citazioni
  • ???jsp.display-item.citation.pmc??? 3
  • Scopus 8
  • ???jsp.display-item.citation.isi??? 11
social impact