Zapata, Mireya

Preferred name

Zapata, Mireya

Main Affiliation

Centro de investigación en Mecatrónica y Sistemas Interactivos

Quito

mireyazapata@uti.edu.ec

ORCID

0000-0003-3382-2724

Scopus Author ID

56818703100

Now showing 1 - 10 of 38

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Real-Time Industrial Risk Mitigation through an IIoT-Based Acoustic Alert System with Embedded Edge Processing and Remote Monitoring
(Springer Nature Switzerland, 2026)
Vargas-Bustamante, Miguel
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Álvarez-Tello, Jorge
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Cevallos-Sornoza, Angie
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Zapata, Mireya
Environmental monitoring schemes have been transformed by the incorporation of IoT technologies in industrial environments, enabling a shift toward continuous monitoring models and immediate response to risk situations. However, many available solutions rely on highly complex and costly SCADA infrastructures, which limits their adoption in facilities with budgetary constraints. In this context, the present research posed as its central question whether it is possible to design and implement a low-cost IoT system, based on open-source technologies, capable of providing real-time environmental monitoring with immediate acoustic alerts and reliable remote notifications. To address this question, an experimental architecture was developed based on Arduino and ESP32 platforms, integrating temperature and gas sensors, a DFPlayer Mini module for customized acoustic feedback, and a wireless transmission scheme to a remote platform. The system was validated through controlled tests involving the exceedance of critical thresholds, evaluating detection accuracy, alert activation latency, and communication stability during continuous operation. The results showed a correct detection rate above 95%, an average alert activation time below 1.5 s, and remote transmission stability greater than 97%. In addition, an approximate 40% reduction in operator response time was observed compared to traditional manual schemes. Consequently, the study demonstrates that an open and scalable IoT architecture can achieve adequate performance levels for industrial safety, contributing a robust human–computer interaction approach and establishing a solid foundation for future extensions toward predictive and interoperable capabilities.
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Low-Cost IoT System with Containerized AI and Telegram Bot for Real-Time Air Quality Risk Communication and Preventive Behavior Change
(Springer Nature Switzerland, 2026)
Zapata, Mireya
;
Álvarez-Tello, Jorge
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Rugel-Sanchez, Keyla
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Vargas-Bustamante, Miguel
Air pollution constitutes one of the main environmental risk factors for public health, particularly in urban environments with limited real time monitoring infrastructure. Although low-cost IoT architectures have emerged as scalable alternatives to extend the spatial coverage of measurements, many implementations lack statistically validated risk classification models capable of translating the environmental data into information to service the public. This study presents the development and statistically validates in real time a risk index for air quality, implemented though a low-cost IoT architecture which integrates supervised artificial intelligence (AI) models deployed at the edge. The system was implemented for eight weeks the Universidad de Guayaquil campus, taking records of PM2.5, PM10 concentrations, temperature and humidity using calibrated sensors. The classification model based on Classification and Regression Trees reached a global accuracy greater which surpassed 90%, with 91.7% concordance to data retrieved from official stations for moderate conditions of PM2.5. K-fold method was used during the validation process and direct comparison with certified infrastructure. Additionally, user evaluation (n = 85) showed 82% adoption of preventive behavior after the implementation of proactive communication with a conversational bot. The results show that the integration of the presented low-cost IoT with validated risk models and user focused communication can generate reliable environmental intelligence and supports preventive decision-making in urban contexts with infrastructure constraints.
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Addressing the Accessibility Gap in Biometric Security: A Low-Cost, Open-Source Approach for High-Risk Environments
(Springer Nature Switzerland, 2026)
Vargas-Bustamante, Miguel
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Zapata, Mireya
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García-Muñoz, Gabriel
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Álvarez-Tello, Jorge
Facial recognition has become established as a key technology for access control in high-insecurity contexts; however, its adoption in resource-constrained environments remains limited. This study addresses the existing gap in the empirical validation of low-cost biometric systems deployed in real-world scenarios. A prototype access control system based on facial recognition was developed using low-cost embedded hardware and open-source software. The research followed a quantitative, experimental, and applied approach, evaluating system performance using standardized biometric metrics (accuracy, FAR, and FRR) under different environmental conditions. The system achieved 97% accuracy in authorized access attempts and 96% accuracy in unauthorized attempts, with an average latency of 1.8 s and complete resistance to basic spoofing attacks. The results demonstrate that low-cost biometric solutions can achieve acceptable levels of reliability and usability for access control in high-insecurity contexts, contributing to the democratization of technological security
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Using Predictive Modeling to Assess Social Media's Impact on Parental Well-Being: A Pilot Study
(Springer Nature Switzerland, 2026)
Tutillo, Jimena
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Martinez, Danilo
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Zapata, Mireya
Social media is part of daily life, facilitating communication, entertainment, and immediate access to information. However, it can negatively affect personal well-being and contribute to the development of addictive behavior. The objective of this paper is to examine how social media users in the economically productive age group of 36 to 56 years are affected by these platforms, and how machine learning (ML) and app development can be used to mitigate the impacts of these dopamine-stimulated behaviors. For this purpose, an empirical study using the Social Media Disorder Test showed that 37.5% of the sample had prolonged usage. To aid in the solution, an app was developed that uses machine learning (ML) to classify addiction levels into 8 categories across 3 social media apps. The app monitors usage in real time and issues alerts for excessive consumption patterns to reduce digital dependence. The results show real patterns of moderate addiction among users. It also seeks to raise awareness of social media use so they can set limits and reduce the impact of their use
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nanoHEENS: Biomimetic Near-Memory-Computing 16-Core SIMD Processor Node for Evolutive Spiking Neural Networks
(IEEE, 2026-05-24)
Larre-Alos, Arnau
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Vallejo-Mancero, Bernardo
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Torres, Victor
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Fernandez, Daniel
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Zapata, Mireya
The design of a digital spiking neural processor (NP) ASIC for biomimetic evolutive applications is reported. The architecture is briefly described, highlighting its fundamental characteristics, namely: Near-Memory Computing (NMC), efficient Single-Instruction Multiple-Data (SIMD) computation, multi-model support, 16-bit precision, axonal delay support and on-the-fly reconfiguration. A sub-mm2, 16-core NP testchip has been designed in 28 nm CMOS and sent to manufacture. A post-implementation simulation, including a controller and two NPs, is shown to demonstrate the system's correct operation and capabilities
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Learning Through Play: Implementing an Educational Escape Room for Teaching Traditions and Culture
(2025)
Valencia-Aragón, Kevin
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Arias Flores, Hugo Patricio
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Zapata, Mireya
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Luis Aguirre-Morales
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Sandra Sanchez-Gordon
This study explores the implementation of an educational Escape Room as a pedagogical strategy to teach tangible cultural heritage to students in Ecuador. This Escape Room was designed using the ADDIE model (Analysis, Design, Development, Implementation and Evaluation) and integrated into the basic education curriculum for Ecuadorian schools. The primary objective was to enhance students’ engagement and understanding of Ecuador’s cultural heritage through an interactive and gamified learning experience. The Escape Room was structured around five missions, each focusing on different aspects of the cultural heritage of five parishes in Ecuador. Students were required to solve puzzles, answer questions, and complete challenges to progress through the game. The results of the implementation, evaluated through observation and student feedback, indicated a high level of student engagement, improved problem-solving skills, and a deeper appreciation for Ecuador’s cultural heritage. The study concludes that Escape Rooms can be an effective tool for teaching cultural heritage, fostering teamwork, creativity, and critical thinking among students. This innovative approach not only makes learning more dynamic and enjoyable but also aligns with modern educational trends that emphasize active and experiential learning.
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Optimizing Agriculture with LoRaWAN and HCI: A Smart Approach to Sustainable Farming
(2025)
Valencia-Aragón, Kevin
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Zapata, Mireya
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Cristopher Toapanta
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Arias Flores, Hugo Patricio
Modern agriculture faces challenges including water scarcity, excessive fertilizer use, and limited connectivity in rural areas, all exacerbated by climate change. This paper presents a smart agriculture system leveraging LoRaWAN technology and human-computer interfaces (HCI) to address these issues. The proposed system integrates low-cost sensors, a LoRaWAN-based network, and a user-friendly dashboard for real-time monitoring of critical variables such as soil moisture, ambient humidity and temperature. A proof-of-concept implementation demonstrates the system’s effectiveness in optimizing water and fertilizer use while maintaining scalability for large agricultural operations. The system operates reliably within rural environments without relying on traditional internet infrastructure, offering an affordable and sustainable solution. Field tests validate the system’s performance, highlighting its potential to enhance decision-making and resource efficiency in floriculture and beyond. Future work aims to expand the system’s capabilities with additional sensors, artificial intelligence for predictive analytics, and automated control mechanisms, further supporting sustainable farming practices.
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Advancements in Assistive Robotics: A Systematic Review of Inclusive Technologies for People With Disabilities
(2025)
Zapata, Mireya
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Diego Guevara
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Jennifer Obregón
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Arias Flores, Hugo Patricio
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Ramos Galarza, Carlos
According to the World Health Organization, approximately 16% of the global population lives with some form of disability, be it physical, sensory, intellectual, or psychosocial. Individuals with disabilities face numerous challenges related to mobility, communication, access to education, and other essential aspects of daily life. In this context, robotic technologies have emerged as innovative solutions aimed at improving autonomy, rehabilitation, and social inclusion. The aim of this systematic review was to identify and synthesize the scientific evidence on robotic technologies developed to support people with disabilities. More specifically, the review sought to analyze the temporal and geographical distribution of research, classify the types of robotic technologies and their applications, examine methodological characteristics and participant demographics, and highlight the key contributions and gaps reported in the included studies. Following PRISMA guidelines, a comprehensive search was conducted in Scopus, PubMed, and IEEE Xplore. From an initial pool of 6,290 articles, 89 studies met the inclusion criteria and were analyzed. The results were categorized into five main themes: publication trends, types of robotic technologies and applications, methodological characteristics, participant demographics, and key contributions. The findings reveal a strong concentration of research on articulated robots for physical disabilities, alongside limited exploration of intellectual, sensory, and psychosocial contexts. While robotic interventions demonstrate significant potential for rehabilitation and daily assistance, challenges remain regarding sample sizes, diversity of disabilities addressed, and long-term validation studies. This review contributes to a deeper understanding of the state of the art in assistive robotics and identifies future research directions to enhance inclusion, accessibility, and clinical integration
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Low-Cost Human–Machine Interface for Computer Control with Facial Landmark Detection and Voice Commands
(2022)
Ramos, P.
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Zapata, Mireya
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Valencia, K.
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Vargas, V.
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Ramos Galarza, Carlos
Nowadays, daily life involves the extensive use of computers, since human beings are immersed in a technological society. Therefore, it is mandatory to interact with computers, which represents a true disadvantage for people with upper limb disabilities. In this context, this work aims to develop an interface for emulating mouse and keyboard functions (EMKEY) by applying concepts of artificial vision and voice recognition to replace the use of hands. Pointer control is achieved by head movement, whereas voice recognition is used to perform interface functionalities, including speech-to-text transcription. To evaluate the interface’s usability and usefulness, two studies were carried out. The first study was performed with 30 participants without physical disabilities. Throughout this study, there were significant correlations found between the emulator’s usability and aspects such as adaptability, execution time, and the participant’s age. In the second study, the use of the emulator was analyzed by four participants with motor disabilities. It was found that the interface was best used by the participant with cerebral palsy, followed by the participants with upper limb paralysis, spina bifida, and muscular dystrophy. In general, the results show that the proposed interface is easy to use, practical, fairly accurate, and works on a wide range of computers. © 2022 by the authors.
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Digital Marketing Strategy for B2B Metalworking Industry: A Case Study for Ecuador
(2025)
Cruz Cusme-Palma
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Jorge Alvarez-Tello
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Zapata, Mireya
In a global environment where digitalization is key to competitiveness, B2B companies in the metalworking sector face challenges in adopting digital tools. In Ecuador, the low digital presence of small and medium-sized enterprises limits their ability to attract clients and expand. This study analyzes the case of Manufacturas ESCA Cía. Ltda., an Ecuadorian company with no digital presence, which requires a digital marketing strategy to improve its visibility and market positioning. The research follows a mixed-method approach, combining qualitative and quantitative methods within a non-experimental, descriptive design. Strategic analysis tools such as SWOT, PESTEL, and CANVAS were applied, along with digital audits and keyword research using Google Keyword Planner, Semrush, and Tubular. Additionally, SEO, SEM, social media, and email marketing strategies were integrated into a comprehensive digital marketing plan. The projected results indicate that the strategy will help increase website traffic to 7,000 annual visits, generate 1,200 leads, and achieve an estimated revenue of $183,814.16 in the first year, with a positive return on investment. It is concluded that implementing digital strategies not only enhances visibility and customer acquisition but also strengthens the competitiveness and sustainability of industrial companies in emerging digital markets. Digital transformation is a strategic necessity for B2B companies aiming to optimize their positioning and growth in the digital era.
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Zapata, Mireya

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