Browsing by Autor "Rommer Alex Ortega-Martinez"

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Design and implementation of a control system in an autoclave as an educational tool for biomedical engineering students
(2025) Deybi Maldonado Lopez; Eynar Calle Viles; Elio Denis Machuca Flores; Rommer Alex Ortega-Martinez
This study focused on the design and implementation of an automated control system for a Pelton & Crane model MC autoclave, with the aim of transforming it into a modern and didactic tool for Biomedical Engineering students. The equipment was structurally redesigned, and sensors, actuators, and a graphical user interface were integrated to illustrate the internal operation of the sterilizer in real time. The educational strategy was structured into four sequential stages: (1) identification of components and maintenance practices, (2) animated visualization of the sterilization cycle, (3) demonstration of the water treatment system, and (4) diagnosis of common faults. To validate the academic impact, knowledge surveys were applied before and after the practical experience. The results showed a significant improvement in students’ understanding of sterilization principles and autoclave maintenance. The modified device achieved standard sterilization conditions (121 °C, 205 kPa), and its educational use led to a 94% increase in average student performance on evaluations. Overall, the educational autoclave proved to be an effective tool for reinforcing hands-on learning and enhancing the training of future biomedical engineers.
Smart iot-based cpr training platform chest compression optimization and remote performance monitoring
(2025) Patricia Nataly Flores Ponce; Eynar Calle Viles; Edgar Roberto Ramos Silvestre; Rommer Alex Ortega-Martinez
This paper presents the development of a smart Cardiopulmonary Resuscitation (CPR) training kit based on Internet of Things (IoT) principles. The system, designed to optimize chest compression techniques, integrates force and distance sensors, local processing through an ESP32 microcontroller, and MQTT wireless communication for remote monitoring and data analysis. Experimental validation with 20 novice participants showed improvements of 35% in compression depth and 28% in frequency within the recommended range (100–120 CPM) compared to standard commercial mannequins. Results showed an average feedback latency of 150ms and 94% accuracy in depth measurement, establishing the feasibility of this IoT solution to improve CPR training quality, especially in resource-limited environments