Moya-Albor, Ernesto

Main Affiliation

Official Name

Moya Albor, Ernesto

ORCID

0000-0002-9637-786X

Preferred name

Moya-Albor, Ernesto

Researcher ID

J-1891-2019

Scopus Author ID

55523223700

Web Site

https://www.up.edu.mx/investigacion/ernesto-moya-albor/

Now showing 1 - 10 of 83

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Metaheuristic Strategy in Automatic Robotics Navigation for Patient Care in Hospitals
(Springer Nature Switzerland, 2025-10-11)
Irvine J. Monroy-Rueda
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Edge-Enhanced Knowledge Distillation System for Diabetic Retinopathy Lesions Computer-Aided Diagnosis
(Springer Nature Switzerland, 2025-10-11)
Alberto Lopez-Figueroa
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Sebastian Jacome-Herrera
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Diego Renza
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Preface : Machine Learning Methods in Biomedical Field Computer-Aided Diagnostics, Healthcare and Biology Applications
(Springer Science and Business Media Deutschland GmbH, 2026)
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Gomez-Coronel, Sandra L.
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Renza Torres, Diego
This book presents a multidisciplinary collection of machine learning approaches applied to the biomedical field, with a focus on computer-aided diagnostic systems, healthcare support tools, biological applications, and sustainable development in health. Computer-aided diagnostic systems leverage machine learning methods to support medical diagnosis, while healthcare support tools, biological applications, and sustainability-oriented studies aim to improve patients’ quality of life, propose innovative solutions to biological challenges, and incorporate sustainability into healthcare processes. The contributions in this book offer readers a holistic view of new methods used to address current biomedical challenges in medicine, biology, and health sciences. By applying artificial intelligence algorithms, mathematical theories, and emergent systems, these works demonstrate how such approaches can improve specific problems or propose innovative solutions. This book is valuable for readers interested in recent advances in machine learning for diagnostic systems, healthcare applications, biological research, and sustainability-related issues. ©The authors ©Springer.
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Orthosis Control Based on Electromyographic Signals and Machine Learning
(Springer Nature Switzerland, 2025)
Escobedo-Gordillo, Andrés
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Díaz, Fernando
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Villa, Jesús
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Sepúlveda, Miguel
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García-Casas, Sebastián
The human hand is indispensable for daily activities, and those who suffer from dysfunction due to strokes or accidents often require therapy to improve their condition. This study has developed a hand orthosis that uses surface Electromyographic (sEMG) signals and machine learning to address therapeutic needs and improve the quality of life for individuals with reduced motor skills in their hands and/or wrists. While current orthoses meet therapy requirements, they do not incorporate machine learning (ML) or sEMG sensors to optimize performance and accessibility. This chapter describes a remote-controlled, electro-mechanical orthosis that can replicate six basic movements of the human hand using three sEMG channels and ML. Our dataset of 14,400 samples, each labeled with a hand gesture, was generated by eight participants. The orthosis is comfortable and customizable for different users, as shown in prototype testing. The convolutional neural network (CNN) used achieves an accuracy of 90.38% with an inference time of 1.515 ms. Therefore, this orthosis system has significant potential for further development and practical application in patients who require such intervention. ©The authors ©Springer.
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Non-Contact Oxygen Saturation Estimation Using Deep Learning Ensemble Models and Bayesian Optimization
(MDPI AG, 2025)
Escobedo Gordillo, Andrés Emiliano
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Monitoring Peripheral Oxygen Saturation (SpO2) is an important vital sign both in Intensive Care Units (ICUs), during surgery and convalescence, and as part of remote medical consultations after of the COVID-19 pandemic. This has made the development of new SpO2-measurement tools an area of active research and opportunity. In this paper, we present a new Deep Learning (DL) combined strategy to estimate SpO2 without contact, using pre-magnified facial videos to reveal subtle color changes related to blood flow and with no calibration per subject required. We applied the Eulerian Video Magnification technique using the Hermite Transform (EVM-HT) as a feature detector to feed a Three-Dimensional Convolutional Neural Network (3D-CNN). Additionally, parameters and hyperparameter Bayesian optimization and an ensemble technique over the dataset magnified were applied. We tested the method on 18 healthy subjects, where facial videos of the subjects, including the automatic detection of the reference from a contact pulse oximeter device, were acquired. As performance metrics for the SpO2-estimation proposal, we calculated the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and other parameters from the Bland–Altman (BA) analysis with respect to the reference. Therefore, a significant improvement was observed by adding the ensemble technique with respect to the only optimization, obtaining 14.32% in RMSE (reduction from 0.6204 to 0.5315) and 13.23% in MAE (reduction from 0.4323 to 0.3751). On the other hand, regarding Bland–Altman analysis, the upper and lower limits of agreement for the Mean of Differences (MOD) between the estimation and the ground truth were 1.04 and −1.05, with an MOD (bias) of −0.00175; therefore, MOD ±1.96𝜎 = −0.00175 ± 1.04. Thus, by leveraging Bayesian optimization for hyperparameter tuning and integrating a Bagging Ensemble, we achieved a significant reduction in the training error (bias), achieving a better generalization over the test set, and reducing the variance in comparison with the baseline model for SpO2 estimation. ©The authors ©Technologies ©MDPI.
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Challenges and Advances in Digital Processing of Fetal Phonocardiography Signal: A Review
(Springer Nature Switzerland, 2025)
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Gomez-Coronel, Sandra L.
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Renza, Diego
This chapter presents a state-of-the-art review of different investigations focused on Fetal Phonocardiography (fPCG). fPCG signals allow the identification of the fetus’s cardiac alterations during pregnancy through a noninvasive and secure approach. However, fPCG signals present some challenges, for example: very weak signal sources, high levels of noise, source mixing, and significant signal attenuation. This work provides a review of available fPCG datasets and the methods proposed for source separation, extraction, and filtering of fPCG signals, as well as the methods for estimating fetal heart rate (fHR) and detecting fetal Heart Sounds (fHS). Additionally, since it is sometimes necessary to transmit or store fPCG signals, the chapter also discusses signal compression approaches and applications involving fPCG signals. ©The authors ©Springer.
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Design of an Underwater Mechatronics System for Detecting Dissolved Solids in Water
(IEEE, 2024)
Estudillo, Eduardo
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Bautista, Luis
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Cardenas, Gerardo
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Mendoza, Aura
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Perez, Francisco
Marine pollution has been one of the most important problems we need to address in modern days, as it can cause an imbalance in the marine life of a body of water. This work presents a proof-of-concept of an underwater unmanned autonomous vehicle for detecting dissolved solids. The system was designed based on the mechatronics engineering model, so that it can move around a body water while measures the solid particles in water. The preliminary results are promising, and we anticipate the use of this low-cost system to help measuring water pollution in near city body waters. ©The authors ©IEEE.
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Identifying and Mitigating Label Noise in Deep Learning for Image Classification
(MDPI AG, 2025)
González-Santoyo, César
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Renza, Diego
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Labeling errors in datasets are a persistent challenge in machine learning because they introduce noise and bias and reduce the model’s generalization. This study proposes a novel methodology for detecting and correcting mislabeled samples in image datasets by using the Cumulative Spectral Gradient (CSG) metric to assess the intrinsic complexity of the data. This methodology is applied to the noisy CIFAR-10/100 and CIFAR-10n/100n datasets, where mislabeled samples in CIFAR-10n/100n are identified and relabeled using CIFAR-10/100 as a reference. The DenseNet and Xception models pre-trained on ImageNet are fine-tuned to evaluate the impact of label correction on the model performance. Evaluation metrics based on the confusion matrix are used to compare the model performance on the original and noisy datasets and on the label-corrected datasets. The results show that correcting the mislabeled samples significantly improves the accuracy and robustness of the model, highlighting the importance of dataset quality in machine learning. ©The authors ©MDPI.
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Automatic Robotics Medication Delivery System: The ANDIS Case Study
(Springer Nature Switzerland, 2025-10-11)
Pablo Carbajal
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Ethan Cobb
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César Hernández
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Alfredo Mejía
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Lucía Menchaca
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Machine Learning Methods in Biomedical Field : Computer-Aided Diagnostics, Healthcare and Biology Applications
(Springer Nature Switzerland, 2026)
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Gomez-Coronel, Sandra L.
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Renza Torres, Diego
This book provides an in-depth exploration of machine learning techniques and their biomedical applications, particularly in developing intelligent computer-aided diagnostic systems, creating groundbreaking healthcare technologies, uncovering novel biological applications, and fostering sustainable health solutions. Integrating artificial intelligence, mathematical modeling, and emergent systems, this book highlights the profound impact of these advanced tools in not only enhancing problem-solving within the biomedical field but also in catalyzing the development of novel solutions. This book is a valuable resource for readers interested in understanding the revolutionary impact of novel machine learning methodologies on the biomedical landscape. Furthermore, it offers a blend of theory and practical applications for those interested in biomedical education and training, biology, medicine, and sustainable health development. ©The authors ©Springer.

Moya-Albor, Ernesto

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