[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.
Research center :
Intelsig
Disciplines :
Electrical & electronics engineering
Author, co-author :
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
Language :
English
Title :
Machine learning techniques to assess the performance of a gait analysis system
Publication date :
24 April 2014
Event name :
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)
Event place :
Bruges, Belgium
Event date :
from 23-04-2014 to 25-04-2014
Audience :
International
Main work title :
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)
Pages :
419-424
Peer reviewed :
Peer reviewed
Name of the research project :
GAIMS
Funders :
SPW DGO6 - Service Public de Wallonie. Economie, Emploi, Recherche [BE]
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