[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 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]
C. Coleman, D. Sobieraj, and L. Marinucci. Minimally important clinical difference of the Timed 25-Foot Walk Test: results from a randomized controlled trial in patients with multiple sclerosis. Current Medical Research and Opinion, 28(1):49-56, January 2012
S. Schwid, A. Goodman, M. McDermott, C. Bever, and S. Cook. Quantitative functional measures in MS: What is a reliable change? Neurology, 58(8):1294-1296, April 2002
H. Acharya and R. Camicioli. The forgotten thesis of Gilles de la Tourette. University of Alberta Health Sciences Journal, 2(2):32-33, December 2005
X. Shao, H. Zhao, K. Nakamura, R. Shibasaki, R. Zhang, and Z. Liu. Analyzing pedestrians' walking patterns using single-row laser range scanners. In IEEE International Conference on Systems, Man and Cybernetics (SMC), volume 2, pages 1202-1207, Taipei, Taiwan, October 2006
M. Teixidó, T. Pallejà, M. Tresanchez, M. Norgués, and J. Palacín. Measuring oscillating walking paths with a LIDAR. Sensors, 11:5071-5086, 2011
S. Piérard, M. Van Droogenbroeck, R. Phan-Ba, and S. Belachew. A new low-cost and non-intrusive feet tracker. In Workshop on Circuits, Systems and Signal Processing (ProRISC), pages 382-387, Veldhoven, The Netherlands, November 2011
S. Piérard, R. Phan-Ba, V. Delvaux, P. Maquet, and M. Van Droogenbroeck. GAIMS: a powerful gait analysis system satisfying the constraints of clinical routine. Multiple Sclerosis Journal, 19(S1):359, October 2013. Proceedings of ECTRIMS/RIMS 2013 (Copenhagen, Denmark), P800
R.Phan-Ba, S.Piérard, E.Lommers, G.Delrue, P.Calay, M.VanDroogenbroeck, V.Delvaux, and P. Maquet. Relative contribution of walking speed, ataxia and gait asymmetry to the composition of gait in multiple sclerosis. Multiple Sclerosis Journal, 19(S1):261, October 2013. Proceedings of ECTRIMS/RIMS 2013 (Copenhagen, Denmark), P608
S.Azrour, S.Piérard, andM. Van Droogenbroeck. Using gaitmeasuring system(GAIMS) to discriminate patients with multiple sclerosis from healthy persons. In BEMEKO Workshop on measurement: Challenges and Opportunities, Liège, Belgium, November 2013
P. Geurts, D. Ernst, and L. Wehenkel. Extremely randomized trees. Machine Learning, 63(1):3-42, April 2006
S. Piérard, S. Azrour, and M. Van Droogenbroeck. Design of a reliable processing pipeline for the non-intrusive measurement of feet trajectories with lasers. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Florence, Italy, May 2014.
