Authors:
Omar A. Galarraga C.
1
;
Vincent Vigneron
2
;
Bernadette Dorizzi
3
;
Néjib Khouri
4
and
Eric Desailly
5
Affiliations:
1
Fondation Ellen Poidatz and Université d'Evry Val d'Essonne, France
;
2
Université d'Evry Val d'Essonne, France
;
3
Institut Mines-Télécom and Télécom SudParis, France
;
4
Fondation Ellen Poidatz and Hôpital Universitaire Necker-Enfants malades, France
;
5
Fondation Ellen Poidatz, France
Keyword(s):
Clinical Gait Analysis, Nonlinear Data Fitting, Neural Networks, Cerebral Palsy, Biomechanics.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Economics, Business and Forecasting Applications
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Medical Imaging
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Regression
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Software Engineering
;
Theory and Methods
Abstract:
Cerebral Palsy affects walking and often produces excessive knee flexion at initial contact (KFIC). Hamstring lengthening surgery (HL) is applied to decrease KFIC. The objective of this work is to design a simulator of the effect of HL on KFIC that could be used as a decision-making tool. The postoperative KFIC is estimated given the preoperative gait, physical examination and the type of surgery. Nonlinear data fitting is performed by feedforward neural networks. The mean regression error on test is 9.25 degrees and 63.21% of subjects are estimated within an error range of 10 degrees. The simulator is able to give good estimations independently of the preoperative gait parameters and the type of surgery. This system predicts the outcomes of orthopaedic surgery on CP children with real gait parameters, and not with qualitative characteristics.