Gait-Based Diplegia Classification Using LSMT Networks

Journal of Healthcare Engineering
Alberto FerrariAdriano Ferrari

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.

References

Feb 24, 2006·Gait & Posture·Fiona DobsonH Kerr Graham
Jun 21, 2006·Gait & Posture·Gabor BartonSteve Attfield
Dec 13, 2006·IEEE Transactions on Bio-medical Engineering·Joarder Kamruzzaman, Rezaul K Begg
Feb 13, 2007·Gait & Posture·Alberto LeardiniMaria Grazia Benedetti
Aug 3, 2011·Research in Developmental Disabilities·Leen Van GestelKaat Desloovere

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Citations

Apr 3, 2020·Sensors·Tristan DotDamien Ricard
Nov 17, 2020·Frontiers in Neurorobotics·Bin FangQin Zhang

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