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García Seco de Herrera, Alba

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García Seco de Herrera
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2019 - 201912020 - 20221
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Autor: Jaruenpunyasak, Jermphiphut ×

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Mostrando 1 - 2 de 2
  • Miniatura
    Publicación
    Foot Recognition Using Deep Learning for Knee Rehabilitation
    (ASET, 2019) Duangsoithong, Rakkrit; Jaruenpunyasak, Jermphiphut; García Seco de Herrera, Alba
    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.
  • Miniatura
    Publicación
    Anthropometric Ratios for Lower-Body Detection Based on Deep Learning and Traditional Methods
    (MDPI, 2022-03-04) Jaruenpunyasak, Jermphiphut; García Seco de Herrera, Alba; Duangsoithong, Rakkrit
    Lower-body detection can be useful in many applications, such as the detection of falling and injuries during exercises. However, it can be challenging to detect the lower-body, especially under various lighting and occlusion conditions. This paper presents a novel lower-body detection framework using proposed anthropometric ratios and compares the performance of deep learning (convolutional neural networks and OpenPose) and traditional detection methods. According to the results, the proposed framework helps to successfully detect the accurate boundaries of the lower-body under various illumination and occlusion conditions for lower-limb monitoring. The proposed framework of anthropometric ratios combined with convolutional neural networks (A-CNNs) also achieves high accuracy (90.14%), while the combination of anthropometric ratios and traditional techniques (A-Traditional) for lower-body detection shows satisfactory performance with an averaged accuracy (74.81%). Although the accuracy of OpenPose (95.82%) is higher than the A-CNNs for lower-body detection, the A-CNNs provides lower complexity than the OpenPose, which is advantageous for lower-body detection and implementation on monitoring systems.