International Journal of Research and Scientific Innovation (IJRSI)
Integration of AI and Iot Technologies for Non-Invasive Sleep Apnea Detection and Monitoring
Published December 20, 2025 • Vol. 12, Issue 11, pp. 1765–1767Open Access
DOI: 10.51244/IJRSI.2025.12110155
Abstract
The prevalence of sleep apnea has driven demand for low-cost, non-invasive, and continuous home-based monitoring systems. This research paper presents an AI-enabled IoT architecture integrating wearable sensors, ESP32 microcontroller processing, and cloud-based analytics for real-time detection of apnea events. The system monitors physiological parameters including SpO2, heart rate, respiratory rate, and body movement. Machine learning models such as SVM, Random Forest, CNN, and LSTM enhance detection accuracy. Findings demonstrate 93% accuracy and 94% sensitivity, validating the effectiveness of the system as a scalable alternative to clinical polysomnography.
Keywords: Engineering
| Journal | International Journal of Research and Scientific Innovation (IJRSI) |
|---|---|
| ISSN | 2321-2705 |
| Volume / Issue | Volume 12, Issue 11 |
| Pages | 1765–1767 |
| Publication date | December 20, 2025 |
| DOI | 10.51244/IJRSI.2025.12110155 |
| Publisher | RSIS International |
| License | Open Access |
How to cite this article
Patrice Adlino, Parth Jadhav, Pavan Pawar, & Amey Prayag (2025). Integration of AI and Iot Technologies for Non-Invasive Sleep Apnea Detection and Monitoring. International Journal of Research and Scientific Innovation (IJRSI), 12(11), 1765-1767. https://doi.org/10.51244/IJRSI.2025.12110155
BibTeX
@article{Patrice2025,
title = {Integration of AI and Iot Technologies for Non-Invasive Sleep Apnea Detection and Monitoring},
author = {Patrice Adlino and Parth Jadhav and Pavan Pawar and Amey Prayag},
journal = {International Journal of Research and Scientific Innovation (IJRSI)},
volume = {12},
number = {11},
pages = {1765--1767},
year = {2025},
doi = {10.51244/IJRSI.2025.12110155},
publisher = {RSIS International}
}