Publicación: Foot Recognition Using Deep Learning for Knee Rehabilitation
Fecha
2019
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Derechos de acceso
info:eu-repo/semantics/openAccess
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Editor
ASET
Resumen
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.
Descripción
La 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.
Categorías UNESCO
Palabras clave
Convolutional neural networks, deep learning, foot recognition, knee rehabilitation
Citación
Duangsoithong, 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.3300596
Centro
E.T.S. de Ingeniería Informática
Departamento
Lenguajes y Sistemas Informáticos