Duangsoithong, RakkritJaruenpunyasak, JermphiphutGarcía Seco de Herrera, Alba2025-03-272025-03-272019Duangsoithong, Rakkrit, Jermphiphut Jaruenpunyasak, y Alba García Seco de Herrera. «Foot Recognition Using Deep Learning for Knee Rehabilitation». International Journal of Information, Control and Computer Sciences 13 (2019). https://doi.org/10.5281/ZENODO.33005961307-6892https://doi.org/10.5281/ZENODO.3300596https://hdl.handle.net/20.500.14468/26370La versión registrada de este artículo, publicado por primera vez en International Journal of Information, Control and Computer Sciences 13 , está disponible en línea en el sitio web del editor: https://publications.waset.org/10010586.pdf. The registered version of this article, first published in the International Journal of Information, Control and Computer Sciences 13, is available online at the publisher's website: https://publications.waset.org/10010586.pdf.The use of foot recognition can be applied in many medical fields such as the gait pattern analysis and the knee exercises of patients in rehabilitation. Generally, a camera-based foot recognition system is intended to capture a patient image in a controlled room and background to recognize the foot in the limited views. However, this system can be inconvenient to monitor the knee exercises at home. In order to overcome these problems, this paper proposes to use the deep learning method using Convolutional Neural Networks (CNNs) for foot recognition. The results are compared with the traditional classification method using LBP and HOG features with kNN and SVM classifiers. According to the results, deep learning method provides better accuracy but with higher complexity to recognize the foot images from online databases than the traditional classification method.eninfo:eu-repo/semantics/openAccess12 Matemáticas::1203 Ciencia de los ordenadores ::1203.17 InformáticaartículoConvolutional neural networksdeep learningfoot recognitionknee rehabilitation