A Comprehensive Analysis of Respiration Detection Technology Based on WiFi Signals

DOI

10.2991/978-94-6463-823-3_32How to use a DOI?

Keywords

Wifi Signal; Breath Detection; Machine Learning

Abstract

s: The utilization of WiFi signals for respiration detection techniques has garnered significant attention within the domain of telemedicine and health monitoring. This is primarily due to the non-contact, convenient, and cost-effective nature of these techniques. In this study, WiFi signal-based breath detection techniques are reviewed and systematically classified into three main categories: principal component analysis (PCA), support vector machine (SVM), and convolutional neural network (CNN)-based methods. Through the comparative analysis of these three categories of techniques in terms of key evaluation indices such as accuracy, refinement, and mean absolute error, we gain insight into their respective applicability and advantages. The paper summarizes the existing research results and provides an outlook on future research directions, aiming to provide theoretical support and technical reference for the further development of WiFi signals in the field of telemedicine and health monitoring.

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  - Zhimin Yin
PY  - 2025
DA  - 2025/08/31
TI  - A Comprehensive Analysis of Respiration Detection Technology Based on WiFi Signals
BT  - Proceedings of the 2025 3rd International Conference on Image, Algorithms, and Artificial Intelligence (ICIAAI 2025)
PB  - Atlantis Press
SP  - 328
EP  - 337
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-823-3_32
DO  - 10.2991/978-94-6463-823-3_32
ID  - Yin2025
ER  -