Title :
A nonparametric modeling approach of soft tissue deformation by ANFIS
Author :
Figueroa-García, Iván ; Sánchez-Sosa, Gisela ; Díaz-Domínguez, Ricardo ; Rodríguez-Villagómez, Francisco ; Huegel, Joel C. ; García-González, Alejandro
Author_Institution :
Biomechatronics Dept., Tecnol. de Monterrey, Zapopan, Mexico
Abstract :
This paper presents a nonparametric modeling approach to soft tissue deformation utilizing an Adaptive Neural Fuzzy Inference System (ANFIS). The model is tested with real data. In order to obtain a consistent set of experimental data, a variable-velocity electro-mechanical platform applies single-point force to deform a soft tissue sample. A Motion Capture system obtains the position of twenty markers on the surface of the sample tissue. With applied force and position data of the central marker as inputs and the position of the remaining markers as outputs, an ANFIS system was designed and trained. The trained estimator is tested with experimental data under artificial noise conditions. The estimation of the position for a particular marker compared with the Motion Capture position data shows that the algorithm performs with less than 1% error.
Keywords :
fuzzy neural nets; fuzzy reasoning; video signal processing; ANFIS system; adaptive neural fuzzy inference system; artificial noise condition; central marker; motion capture position data; motion capture system; nonparametric modeling; real data; single point force; soft tissue deformation; variable velocity electromechanical platform; Biological neural networks; Biological system modeling; Cameras; Computational modeling; Force; Fuzzy logic; Training;
Conference_Titel :
Biomedical Robotics and Biomechatronics (BioRob), 2012 4th IEEE RAS & EMBS International Conference on
Conference_Location :
Rome
Print_ISBN :
978-1-4577-1199-2
DOI :
10.1109/BioRob.2012.6290913