Intelligent Portable Ventilation System Using RNN-Based Predictive Modeling

DOI

10.2991/978-94-6463-858-5_177How to use a DOI?

Keywords

Portable ventilation; artificial intelligence; respiratory therapy; pressure control; RNN model; patient monitoring

Abstract

This paper presents the design and implementation of a portable ventilation system with variable pressure per minute, leveraging artificial intelligence for precise respiratory support. The system incorporates sensors such as pressure sensors, flow sensors, and a pulse oximeter (MAX30100) to monitor vital parameters including heart rate and oxygen saturation. The core of the system is a microcontroller, integrated with an AI model that predicts the required airway pressure based on real-time patient data. This AI-driven approach not only enhances patient safety but also optimizes ventilation through a closed-loop feedback mechanism. The AI model utilizes a recurrent neural network (RNN) to analyze data and adjust the air pump speed, accordingly, ensuring effective and individualized patient care. Comparative analysis with conventional and semi-automated systems demonstrates the proposed model’s superiority in accuracy, efficiency, and patient safety. The portable design of the system makes it suitable for diverse healthcare settings, including home care and emergency scenarios. Future developments may integrate telemedicine capabilities for remote monitoring. The proposed system offers a promising solution for improving respiratory therapy, particularly in critical care environments where precision and adaptability are crucial.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - N. Khadar Bashai
AU  - K. Manikandan
AU  - E. Aravindraj
AU  - H. Arunkumar
AU  - S. Gowtham
PY  - 2025
DA  - 2025/11/04
TI  - Intelligent Portable Ventilation System Using RNN-Based Predictive Modeling
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 2125
EP  - 2136
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_177
DO  - 10.2991/978-94-6463-858-5_177
ID  - Bashai2025
ER  -