Ponce, Hiram
Main Affiliation
Preferred name
Ponce, Hiram
Official Name
Ponce Espinosa, Hiram Eredín
ORCID
0000-0002-6559-7501
Researcher ID
K-7593-2019
Scopus Author ID
54911890000
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Item type:Publication, Research ecosystem 2025(Universidad Panamericana, 2025); ; Dear campus members, It is with great satisfaction that we present our Research Ecosystem 2025, a publication that reflects the sustained growth and strategic maturation of research at Universidad Panamericana. This edition builds on an increasingly comprehensive vision of university research, one in which technology transfer and innovation, institutional funding for research, and international rankings positioning affirm themselves as complementary pillars of our scientific activity. Our scientific production maintains its commitment to quality, with more than 50% of our indexed publications appearing in high-impact Q1 and Q2 journals. This year’s report also introduces, for the first time, a dedicated section on copyright and industrial models alongside patents, reflecting the growing diversity and maturity of our intellectual property portfolio, now spanning areas as varied as aeronautics, semiconductor technology, healthcare devices, and sustainable materials. © Universidad Panamericana. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Data-Driven Innovation for Intelligent Technology : Perspectives and Applications in ICT(Springer Cham, 2024); ; ; ; This book focuses on new perspectives and applications of data-driven innovation technologies, applied artificial intelligence, applied machine learning and deep learning, data science, and topics related to transforming data into value. It includes theory and use cases to help readers understand the basics of data-driven innovation and to highlight the applicability of the technologies. It emphasizes how the data lifecycle is applied in current technologies in different business domains and industries, such as advanced materials, healthcare and medicine, resource optimization, control and automation, among others. This book is useful for anyone interested in data-driven innovation for smart technologies, as well as those curious in implementing cutting-edge technologies to solve impactful artificial intelligence, data science, and related information technology and communication problems. ©Springer. ©The authors. ©The editors28 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An Intelligent Human Fall Detection System Using a Vision-Based StrategyElderly people is increasing dramatically during the current years, and it is expected that this population reaches 2.1 billion of individuals by 2050. In this regard, new care strategies are required. Assisted living technologies have proposed alternatives to support professional caregivers and families to take care of elderly people, such as in risk of falls. Currently, fall detection systems are able to alleviate the latter problem and reduce the time a person who suffered a fall receives assistance. Thus, this paper proposes a fall detection system based on image processing strategy to extract motion features through an optical flow method. For classification, we use these features as inputs to a convolutional neural network. We applied our approach in a dataset comprises video recordings of one subject performing different types of falls. In experimental results, our approach showed 92% accuracy on the dataset used. © 2019 IEEE.1 29Scopus© Citations 9 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Special Issue on Interdisciplinary Artificial Intelligence: Methods and Applications of Nature-Inspired Computing(2022); Inspiration in nature has been widely explored, from the macro to micro-scale. From a scientific perspective, these methods inspired by nature have proven to be efficient tools for tackling real-world problems because most of the latter are highly complex or the resources are limited to analyze them. This inspiration is justified by the fact that natural phenomena mainly emphasize adaptability, optimization, robustness, and organization, among other properties, to deal with complexity. In that sense, three methodologies are commonly considered: human-designed problem-solving techniques inspired by nature, the synthesis of natural phenomena to develop algorithms, and the use of nature-inspired materials to perform computations. Some applications of nature-inspired computing include data mining, machine learning, optimization, robotics, engineering control systems, human–machine interaction, healthcare, the Internet of Things, cloud computing, smart cities, and many others.|| This Special Issue aimed to cover original research works with emphasis on the methodologies and applications of nature-inspired computing to handle the above-mentioned complex systems. We received a total of 38 submitted papers, and 18 papers were accepted (covering 47% of acceptance rate).|| The Special Issue presents different works related to metaheuristic optimization methods and their applications of human brain inspiration and neural networks, natural language processing-based applications, and fuzzy-logic-based applications. ©2022 Applied Sciences, MDPI.1 6 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Non-contact breathing rate monitoring system using a magnification technique and artificial hydrocarbon networks(2020); ; In this paper, we present a new non-contact strategy to estimate the breathing rate based on the Eulerian motion video magnification technique and an Artificial Hydrocarbon Networks (AHN) as classifier. After the magnification procedure, a AHN is trained to detect the inhalation and exhalation frames in the video. From this classification, the respiratory rate is estimated. The magnification procedure was carried out using the Hermite decomposition. The respiratory rate (RR) is estimated from the classified frames. We have tested the method on 10 healthy subjects in different positions. To compare performance of methods to respiratory rate the mean average error and a Bland and Altman analysis is used to investigate the agreement of the methods. The mean average error for our strategy is 4.46 ± 3.68% with and agreement with respect of the reference of ˜ 98 %. © 2020 SPIE1 6 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Contactless Video-Based Vital-Sign Measurement Methods: A Data-Driven Review(Springer, 2024-01-01); ; 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.25 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Open Source Implementation for Fall Classification and Fall Detection Systems(2020); ; ; 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.Scopus© Citations 2 2 19 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Cardiovascular Disease Detection Using Machine Learning(2022)Scopus© Citations 3 1 10 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Price Estimation for Pre-owned Vehicles Using Machine Learning(2024)15 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Conceptual Design of a Firefighter Drone(2018)Scopus© Citations 17 1 33