Moya-Albor, Ernesto
Thumbnail Image
Preferred name
Moya-Albor, Ernesto
Official Name
Moya Albor, Ernesto
Alternative Name
emoya
Moya-Albor, E.
Main Affiliation
Web Site
ORCID
Scopus Author ID
55523223700
Researcher ID
J-1891-2019

Research Output
Now showing 1 - 10 of 41
Thumbnail Image
Publication

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.

View
No Thumbnail Available
Publication

A Computer Vision Approach to Terminus Movement Analysis of Viedma Glacier

2023 , Moya-Albor, Ernesto , Schwartzman, Armin , Brieva, Jorge , Pardo, Mauricio , Ponce, Hiram , Chávez-Domínguez, Rodrigo

In this paper, an automatic segmentation approach of the Viedma glacier terminus is proposed. The method uses multi-spectral images from the Landsat-5 satellite to determine the area of the glacier through computer vision techniques. The area of the glacier is estimated, and a linear model is fitted, obtaining a correlation of 0.968 between the measured area and a fit linear regression model. On the other hand, a bio-inspired optical flow estimation approach is used to calculate and visualize the displacement of the glacier through time. In addition, an analysis is performed between the temperature variation in the Southern Cone and the decrease of the glacier in the function of time. A linear trend (r2=0.95) shows that the analyzed area of the glacier has decreased by about 1.9% annually in the observation season. It reveals an inverse relationship between the change in the size of the glacier and global warming, showing that if the same conditions remain, the glacier's zone analyzed in this work would be close to its disappearance in around 70 years, the time lapse in which a global temperature increase of 1.24 oC would be reached. © 2023 IEEE.

View
No Thumbnail Available
Publication

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.

View
No Thumbnail Available
Publication

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.

View
No Thumbnail Available
Publication

A non-contact heart rate estimation method using video magnification and neural networks

2020 , Moya-Albor, Ernesto , Brieva, Jorge , Ponce, Hiram , Martinez-Villaseñor, Lourdes

Heart rate (HR) monitoring is a significant task in many medical, sports and aged care in assisted living applications, among other disciplines. In the literature, several works have reported effectiveness in addressing the measurement of HR using contact sensors such as adhesive or dry electro-conductive electrodes. However, there are several issues associated with contact sensors like portability problems, skin irritation, discomfort and body movement constraints. In this regard, this paper presents a non-contact HR estimation method using vision-based methods and neural networks. This work uses a bio-inspired Eulerian motion magnification approach to highlight the blood irrigation process of the cardiac pulse, which is later inputted to a feed-forward neural network trained to estimate the HR. For experimental analysis, we compare two magnification procedures, based on Gaussian and Hermite decomposition, over video recordings collected from the wrists of five subjects. Results show that the Hermite-based magnification method is robust under noise analysis (4.24 bpm of root mean squared-error in the worst case scenario). Furthermore, our results demonstrate that the Hermite-based method is competitive in the state-of-the-art (1.86 bpm in average of root mean squared-error) and can be implemented using a single camera for contactless HR estimation. ©2020 IEEE Instrumentation and Measurement Magazine, Institute of Electrical and Electronics Engineers Inc.

View
No Thumbnail Available
Publication

Non-contact breathing rate monitoring system based on a Hermite video magnification technique

2018 , Brieva, Jorge , Moya-Albor, Ernesto , Yael Rivas Scott, Orlando , Ponce, Hiram

In this paper we present a new non-contact strategy to estimate the breathing rate based on the Eulerian motion magnification technique and a system based on di€erent images processing steps. After the magnification procedure, a ROI is selected manually, an enhancement algorithm based on an adaptive histogram equalization is applied and finally the frames are binarized using the Otsu algorithm. Morphological operations are carry out on the video frames and a tracking temporal strategy is implemented to estimate the breathing rate. The magnification procedure was carried out using an Hermite decomposition. We have tested the method on three subjects in four positions (seat, lying face down, lying face up and lying in fetal position). The motion magnification approach is compared to the Laplacian decomposition strategy computing the mean absolute error. © SPIE. Downloading of the abstract is permitted for personal use only.

View
No Thumbnail Available
Publication

Towards the Distributed Wound Treatment Optimization Method for Training CNN Models: Analysis on the MNIST Dataset

2023 , Ponce, Hiram , Moya-Albor, Ernesto , Brieva, Jorge

Convolutional neural network (CNN) is a prominent algorithm in Deep Learning methods. CNN architectures have been used successfully to solve various problems in image processing, for example, segmentation, classification, and enhancement task. However, automatic search for suitable architectures and training parameters remain an open area of research, where metaheuristic algorithms have been used to fine-tuning the hyperparameters and learning parameters. This work presents a bio-inspired distributed strategy based on Wound Treatment optimization (WTO) for training the learning parameters of a LenNet CNN model fast and accurate. The proposed method was evaluated over the popular benchmark dataset MNIST for handwritten digit recognition. Experimental results showed an improvement of 36.87% in training time using the distributed WTO method compared to the baseline with a single learning agent, and the accuracy increases 4.69% more using the proposed method in contrast with the baseline. As this is a preliminary study towards the distributed WTO method for training CNN models, we anticipate this approach can be used in robotics, multi-agent systems, federated learning, complex optimization problems, and many others, where an optimization task is required to be solved fast and accurate. © 2023 IEEE.

View
No Thumbnail Available
Publication

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.

View
No Thumbnail Available
Publication

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

View
No Thumbnail Available
Publication

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.

View