Reference : Machine learning techniques to assess the performance of a gait analysis system
Scientific congresses and symposiums : Paper published in a book
Engineering, computing & technology : Electrical & electronics engineering
http://hdl.handle.net/2268/163051
Machine learning techniques to assess the performance of a gait analysis system
English
Pierard, Sébastien mailto [Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Télécommunications >]
Phan-Ba, Rémy mailto [Université de Liège - ULiège > Département des sciences cliniques > Département des sciences cliniques]
Van Droogenbroeck, Marc mailto [Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Télécommunications >]
24-Apr-2014
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)
419-424
Yes
No
International
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)
from 23-04-2014 to 25-04-2014
Bruges
Belgium
[en] GAIMS
[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.
Intelsig
DGEE - Région wallonne. Direction générale de l'Economie et de l'Emploi
GAIMS
Researchers ; Professionals
http://hdl.handle.net/2268/163051
also: http://hdl.handle.net/2268/163322 ; http://hdl.handle.net/2268/166450

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