[en] This paper presents a methodology based on machine learning techniques to assess the performance of a system measuring the trajectories of the lower limbs extremities for the follow-up of patients with multiple sclerosis. We show how we have established, with the help of machine learning, four important properties about this system: (1) an automated analysis of gait characteristics provides an improved analysis with respect to that of a human expert, (2) after learning, the gait characteristics provided by this system are valuable compared to measures taken by stopwatches, as used in the standardized tests, (3) the motion of the lower limbs extremities contains a lot of useful information about the gait, even if it is only a small part of the body motion, (4) a measurement system combined with a machine learning tool is sensitive to intra-subject modifications of the walking pattern.
Centre/Unité de recherche :
Intelsig
Disciplines :
Ingénierie électrique & électronique
Auteur, co-auteur :
Pierard, Sébastien ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Télécommunications
Phan-Ba, Rémy ; Université de Liège - ULiège > Département des sciences cliniques > Département des sciences cliniques
Van Droogenbroeck, Marc ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Télécommunications
Langue du document :
Anglais
Titre :
Machine learning techniques to assess the performance of a gait analysis system
Date de publication/diffusion :
24 avril 2014
Nom de la manifestation :
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)
Lieu de la manifestation :
Bruges, Belgique
Date de la manifestation :
from 23-04-2014 to 25-04-2014
Manifestation à portée :
International
Titre de l'ouvrage principal :
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)
Pagination :
419-424
Peer reviewed :
Peer reviewed
Intitulé du projet de recherche :
GAIMS
Organisme subsidiant :
SPW DGO6 - Service Public de Wallonie. Economie, Emploi, Recherche [BE]
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