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" JOURNAL OF HEALTHCARE ENGINEERING, vol. 2019, pp. 1 -8 , 2019 DOI: 10.1155/2019/3796898

Bibtex entry:

 @article{
11380_1169976,
author = {Ferrari, Alberto and Bergamini, Luca and Guerzoni, Giorgio and Calderara, Simone and Bicocchi, Nicola and Vitetta, Giorgio and Borghi, Corrado and Neviani, Rita and Ferrari, Adriano},
title = {Gait-Based Diplegia Classification Using LSMT Networks},
year = {2019},
journal = {JOURNAL OF HEALTHCARE ENGINEERING},
volume = {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.},
keywords = {Diplegia, gait, classification, multilayer perceptron, recurrent neural network},
doi = {10.1155/2019/3796898},
pages = {1--8}
}

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