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 - 6 of 6
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Open Source Implementation for Fall Classification and Fall Detection Systems

2020 , Ponce, Hiram , Martinez-Villaseñor, Lourdes , Nuñez Martínez, José Pablo , Moya-Albor, Ernesto , Brieva, Jorge

Distributed social coding has created many benefits for software developers. Open source code and publicly available datasets can leverage the development of fall detection and fall classification systems. These systems can help to improve the time in which a person receives help after a fall occurs. Many of the simulated falls datasets consider different types of fall however, very few fall detection systems actually identify and discriminate between each category of falls. In this chapter, we present an open source implementation for fall classification and detection systems using the public UP-Fall Detection dataset. This implementation comprises a set of open codes stored in a GitHub repository for full access and provides a tutorial for using the codes and a concise example for their application. © 2020, Springer Nature Switzerland AG.

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Mobile Robot with Movement Detection Controlled by a Real-Time Optical Flow Hermite Transform

2015 , Moya-Albor, Ernesto , Brieva, Jorge , Ponce, Hiram

This chapter presents a new algorithm inspired in the human visual system to compute optical flow in real-time based on the Hermite Transform. This algorithm is applied in a vision-based control system for a mobile robot. Its performance is compared for different texture scenarios with the classical Horn and Schunck algorithm. The design of the nature-inspired controller is based on the agent-environment model and agent’s architecture. Moreover, a case study of a robotic system with the proposed real-time Hermite optical flow method was implemented for braking and steering when mobile obstacles are close to the robot. Experimental results showed the controller to be fast enough for real-time applications, be robust to different background textures and colors, and its performance does not depend on inner parameters of the robotic system. © Springer International Publishing Switzerland 2016.

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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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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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Application of Convolutional Neural Networks for Fall Detection Using Multiple Cameras

2020 , Espinosa Loera, Ricardo Abel , Ponce, Hiram , Gutiérrez, Sebastián , Martinez-Villaseñor, Lourdes , Moya-Albor, Ernesto , Brieva, Jorge

Currently one of the most important research issue for artificial intelligence and computer vision tasks is the recognition of human falls. Due to the current exponential increase in the use of cameras is it common to use vision-based approach for fall detection and classification systems. On another hand deep learning algorithms have transformed the way that we see vision-based problems. The Convolutional Neural Network (CNN) as deep learning technique offers more reliable and robust solutions on detection and classification problems. Focusing only on a vision-based approach, for this work we used images from a new public multimodal data set for fall detection (UP-Fall Detection dataset) published by our research team. In this chapter we present fall detection system using a 2D CNN analyzing multiple camera information. This method analyzes images in fixed time window frames extracting features using an optical flow method that obtains information of relative motion between two consecutive images. For experimental results, we tested this approach in UP-Fall Detection dataset. Results showed that our proposed multi-vision-based approach detects human falls achieving 95.64% in accuracy with a simple CNN network architecture compared with other state-of-the-art methods.

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