Moya-Albor, Ernesto
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Moya-Albor, Ernesto
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Moya Albor, Ernesto
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emoya
Moya-Albor, E.
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55523223700
Researcher ID
J-1891-2019

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Now showing 1 - 10 of 41
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Contactless Video-Based Vital-Sign Measurement Methods: A Data-Driven Review

2024-01-01 , Brieva, Jorge , Moya-Albor, Ernesto , Ponce, Hiram , Escobedo-Gordillo, Andrés

Nowadays, the healthcare is a priority for both governments and persons. Vital sign monitoring allows knowing the health status and is widely used for prevention, diagnosis, and treatment of determined illnesses. In particular, breathing and heart rate are traditionally considered the most relevant and accessible vital signs. However, oxygen saturation was essential in the COVID-19 pandemic. On the other hand, contact techniques to estimate these vital signs are a standard monitoring reference. However, non-contact estimation methods have gained relevance in the last few years in those cases where there is the possibility of suffering stress, pain, and skin irritation in specific situations, as in the case of vulnerable skin in burn patients and neonates. In this chapter, a review of contactless video-based vital-sign methods is presented. The selected methods have a data-driven approach as an alternative when there is not theoretical model of the physiological phenomenon. Finally, a new framework with a general data-driven approach to estimate the most used vital signs is proposed. This framework includes a region of interest extraction stage, a video magnification technique to reveals subtle changes, and a machine learning method to estimate the vital signs. In addition, each step describes some recommendations and best practices found ©Springer.

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Automatic classification of coronary stenosis using convolutional neural networks and simulated annealing

2022 , Rendon-Aguilar, Luis Diego , Cruz-Aceves, Ivan , Fernandez-Jaramillo, Arturo Alfonso , Moya-Albor, Ernesto , Brieva, Jorge , Ponce, Hiram

Automatic detection of coronary stenosis plays an essential role in systems that perform computer-aided diagnosis in cardiology. Coronary stenosis is a narrowing of the coronary arteries caused by plaque that reduces the blood flow to the heart. Automatic classification of coronary stenosis images has been re-cently addressed using deep and machine learning techniques. Generally, the machine learning methods form a bank of empirical and automatic features from the angiographic images. In the present work, a novel method for the automatic classification of coronary stenosis X-ray images is presented. The method is based on convolutional neural networks, where the neural architecture search is performed by using the path-based metaheuristics of simulated annealing. To perform the neural architecture search, the maximization of the F1-score metric is used as the fitness function. The automatically generated convolutional neural network was compared with three deep learning methods in terms of the accuracy and F1-score metrics using a testing set of images obtaining 0.88 and 0.89, respectively. In addition, the proposed method was evaluated with different sets of coronary stenosis images obtained via data augmentation. The results involving a number of different instances have shown that the proposed architecture is robust preserving the efficiency with different datasets © 2023 Şaban öztürk. All rights reserved.

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A perceptive watermarking approach applied to COVID-19 imaging data

2020 , Gomez-Coronel, Sandra L. , Moya-Albor, Ernesto , Brieva, Jorge , Ponce, Hiram

This work presents a watermarking algorithm applied to medical images of COVID-19 patients. The principal objective is to protect the information of the patient using an imperceptible watermarking and to preserve its diagnose. Our technique is based on a perceptive approach to insert the watermark by decomposing the medical image using the Hermite transform. We use as watermark two image logos, including text strings to demonstrate that the watermark can contain relevant information of the patient. Some metrics were applied to evaluate the performance of the algorithm. Finally, we present some results about robustness with some attacks applied to watermark images. © 2020 SPIE

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A vision-based approach for fall detection using multiple cameras and convolutional neural networks: A case study using the UP-Fall detection dataset

2019 , Espinosa Loera, Ricardo Abel , Ponce, Hiram , Moya-Albor, Ernesto , Martinez-Villaseñor, Lourdes , Brieva, Jorge , Gutiérrez, Sebastián

The automatic recognition of human falls is currently an important topic of research for the computer vision and artificial intelligence communities. In image analysis, it is common to use a vision-based approach for fall detection and classification systems due to the recent exponential increase in the use of cameras. Moreover, deep learning techniques have revolutionized vision-based approaches. These techniques are considered robust and reliable solutions for detection and classification problems, mostly using convolutional neural networks (CNNs). Recently, our research group released a public multimodal dataset for fall detection called the UP-Fall Detection dataset, and studies on modality approaches for fall detection and classification are required. Focusing only on a vision-based approach, in this paper, we present a fall detection system based on a 2D CNN inference method and multiple cameras. This approach analyzes images in fixed time windows and extracts features using an optical flow method that obtains information on the relative motion between two consecutive images. We tested this approach on our public dataset, and the results showed that our proposed multi-vision-based approach detects human falls and achieves an accuracy of 95.64% compared to state-of-the-art methods with a simple CNN network architecture. © 2019 Elsevier Ltd

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From Project-Based Learning to Innovative Technologies in Mechatronics Course: A Case Study in a Private University in Mexico City

2024-01-01 , Ponce, Hiram , Moya-Albor, Ernesto , Brieva, Jorge

Mechatronics engineering is a challenging discipline that needs different thinking and practices in contrast with traditional engineering. This challenging is mainly due to the demand of integration, collaboration and holistic approaches required during the design methodology. This study examines the transformation from traditional education to an in-deep professional and research focused projects. The key factor in the mechatronics learning practice includes the implementation of a major project focused on positive social impact solutions and the road map developed for this purpose. This work proposes a methodology that allows students develop a major project with a holistic view, including design constraints related to specific contextual aspects as economics, environmental, societal, ethical, health and sustainability. Also, students are able to develop professional and research skills. The methodology also allows students propose a major project focused on positive social impact with design constraints. It also exposes students different engineering and computational tools for collaboration and integration. The study uses data from 69 students enrolled along four years, from 2016 to 2019. Results show that the student learning outcomes increased significantly at the end of the period time, from to (in range between 0 to 4), reaching the satisfactory level (year-2016 as baseline). Also, 100% of the scientific papers derived from the major projects were accepted for publication in international conferences © 2024 Springer Nature

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Challenges and trends in multimodal fall detection for healthcare

2020 , Ponce, Hiram , Brieva, Jorge , Martinez-Villaseñor, Lourdes , Moya-Albor, Ernesto , HIRAM EREDIN PONCE ESPINOSA;376768 , JORGE EDUARDO BRIEVA RICO;121435

This book focuses on novel implementations of sensor technologies, artificial intelligence, machine learning, computer vision and statistics for automated, human fall recognition systems and related topics using data fusion. It includes theory and coding implementations to help readers quickly grasp the concepts and to highlight the applicability of this technology. For convenience, it is divided into two parts. The first part reviews the state of the art in human fall and activity recognition systems, while the second part describes a public dataset especially curated for multimodal fall detection. It also gathers contributions demonstrating the use of this dataset and showing examples. This book is useful for anyone who is interested in fall detection systems, as well as for those interested in solving challenging, signal recognition, vision and machine learning problems. Potential applications include health care, robotics, sports, human–machine interaction, among others.

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SCOMA hand prosthetic

2021 , García, Carlos , Reyes, Arturo , Canul, Monserrat , Gurza, Octavio , Cruzado, Sebastián , Díaz, Joaquín , Brieva, Jorge , Moya-Albor, Ernesto , Ponce, Hiram

SCOMA Prosthetic Hand is a robotic hand that can give to the patient the ability to resume a good part of their daily activities. It is not only designed to resume daily activities, but also to improve the mental health of the patient. Worldwide, each year the number of amputees increases from 150,000 to 200,000 in which 30% of these amputees have suffered an upper limb amputation but only between 27% and 44% of them use arm prostheses. There are many reasons behind this, but some aspects to consider about existing prostheses are: uncomfortable, very expensive, have a robotic appearance, or need invasive procedures to fit patients. In our proposal we used the mechatronic design methodology and the data from the Amputee Coalition to create a new hand prosthesis. We analyzed it through different studies such as SWOT diagrams, quality matrix, goal tree and pairwise comparison matrix and simulation tests. In addition, we tested a 3D printing to find a suitable design and the most assertive components. By building the robotic hand with cheaper, more common components and limited functions, we can offer to the patients a comfortable prosthesis and a new more realistic option than those offer on the market. This prosthesis have limited functions but can be accessible to many people and the design could be improved in the future easily. © 2021 IEEE.

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Stair Climbing Robot Based on Convolutional Neural Networks for Visual Impaired

2019 , Campos, Guillermo , Poza, David , Reyes, Moises , Zacate, Alma , Ponce, Hiram , Brieva, Jorge , Moya-Albor, Ernesto

When a person loses the sense of sight, in general, it is suggested to use a white cane to perform daily activities. However, using a white cane limits the movement of a person. In addition, guide dogs can be served in this impairment. However, the acquisition and maintenance of a guide dog is extremely high for people in development countries. In this regard, this paper presents a proof-of-concept of a low-cost robotic system able to guide a visual impaired, as a guide dog. The robot is specially designed for climbing stairs at indoors, and it uses convolutional neural networks (CNN) for both object detection and hand gesture recognition for special instructions from the user. Experimental results showed that our prototype robot can climb stairs with 86.7% of efficiency in concrete stair surfaces. Also, the visual representation by CNN performed more than 98% accuracy. © 2019 IEEE.

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Modular IoT-based Automated Hydroponic System

2021 , Aranda, Manlio , Savage, Alejandra , San Roman, José Luis , Noguera, Luis , Ponce, Hiram , Brieva, Jorge , Moya-Albor, Ernesto

The growth of population and the rapid urbanization have demanded overproduction of food while reducing the water supply, increasing energy consumption, limiting the land, and augmenting the cost of food transportation. Moreover, overproduction comes with food waste and a negative impact on the ecosystem. As an alternative, urban farming has been proposed to mitigate this problem. It includes cultivating in rooftops and residential indoors. In this regard, this work proposes the development of an automatic modular and vertical hydroponic system capable to regulate the water flow, the artificial lights, and the pH of the water. The hydroponic system is made of modules which means the user can upgrade the system depending on the needs like higher production or space reconfiguration. Also, the hydroponic system implements Internet-of-Things to monitor and operate it remotely. A first prototype is presented, and the experimental results validate its potential applicability. © 2021 IEEE.

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Bio-inspired Optical Flow-based Autonomous Obstacle Avoidance Control

2019 , Moya-Albor, Ernesto , Gomez-Coronel, Sandra L. , Ponce, Hiram , Brieva, Jorge , Chávez Domínguez, Rodrigo , Guadarrama-Muñoz, Alexis E.

In this paper, we propose a new methodology for autonomous obstacle avoidance control using a bio-inspired optical flow estimation. The main difference with other methods is that we use an image model inspired by the human vision system to define the constraints in the optical flow formulation which includes a Hermite transform (HT) and a perceptive mask. We use a physical robot platform to test the control algorithm, where due to the structure of the chassis a forward, reverse and turn movements were defined. The robot has a RBG camera to capture images of the path and then calculate optical flow estimation. To define velocity and direction robot response we propose a fuzzy controller. Finally, we made some experiments to demonstrate the performance of control navigation, and how responds algorithm using HT and a perceptive mask. © 2019 IEEE.

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