Smart iot-based cpr training platform chest compression optimization and remote performance monitoring

Authors

DOI:

https://doi.org/10.56294/evk2026400

Keywords:

Cardiopulmonary Resuscitation (CPR), Internet of Things (IoT), MQTT, ESP32, Bioinformatics, Smart Networks, Biomedical Sensors, Real-time Feedback

Abstract

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

Downloads

Published

2026-01-01

Issue

Vol. 5 (2026): eVitroKhem

Section

Original

License

Copyright (c) 2026 Patricia Nataly Flores Ponce , Eynar Calle Viles, Edgar Roberto Ramos Silvestre, Rommer Alex Ortega-Martinez (Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.

How to Cite

1.
Flores Ponce PN, Calle Viles E, Ramos Silvestre ER, Ortega-Martinez RA. Smart iot-based cpr training platform chest compression optimization and remote performance monitoring. eVitroKhem [Internet]. 2026 Jan. 1 [cited 2025 Dec. 27];5:400. Available from: https://evk.southam.pub/index.php/evk/article/view/400