LoRa-based contactless long-range respiration classification system

• Published in: Proceedings - International Conference on Parallel and Distributed Systems pp. 114 - 121
• Main Authors: Luo, Juan, Zhou, Renjie, Cheng, Yue
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.01.2023
• Subjects: Bayesian Classifier; Feature extraction; Frequency-domain analysis; Interference; Linear regression; LoRa; Medical services; Respiration State Detection; Robustness; Training
• ISSN: 2690-5965
• DOI: 10.1109/ICPADS56603.2022.00023

Contactless respiration detection methods have a wide range of applications in medical services and personnel testing because they eliminate the need for contact between the device and the person being tested. However, existing respiration detection algorithms usually face the problem that the subject is in an unstable breathing state in a non-stationary situation, making it difficult to detect accurately. Therefore, in order to reduce the negative interference of different respiration states on the accuracy of respiration detection, a Bayesian classifier-based respiration state classification method, Res-Classifier, is proposed in this paper as a pre-processing step before respiration detection. Res-Classifier enables respiration detection to select a more targeted method based on respiration state to improve the accuracy of respiration detection. First, Res-Classifier extracts the energy distribution in the LoRa reflected signal spectrum from the frequency domain and the periodicity of the signal from the embedding space, while selects the Power Spectrum Density-Ratio, linear regression variance of the embedding space as features. The detected respiratory signals are then classified by these characteristics into three cases: normal breathing, unstable breathing, and stopped breathing, as a pre-processing step for respiratory state classification prior to respiratory detection. We use LoRa transmitting and receiving nodes to collect real signal data and evaluate Res-Classifier's performance in experiments. The experiment results show that the classification accuracy of the proposed Res-Classifier reaches 97%, which can demonstrate that the Res-Classifier improves the quality of the respiration signal and the robustness of the respiration frequency estimation.