Monitoring Respiratory Motion with Wi-Fi CSI:Characterizing performance and the BreatheSmart Algorithm

• Published in: IEEE access Vol. 10; p. 1
• Main Authors: Mosleh, Susanna, Coder, Jason B., Scully, Christopher G., Forsyth, Keith, Kalaa, Mohamad Omar Al
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Biomedical monitoring; Channel state information; Classification; Data acquisition; Data mining; deep learning; Estimation; Feasibility; Hardware; Human motion; LSTM; Machine learning; MIMO-OFDM; Monitoring; respiration monitoring; respiratory motion classification; Software algorithms; Wi-Fi; Wireless fidelity; deep learning; LSTM; Wi-Fi; MIMO-OFDM; respiration monitoring; respiratory motion classification; Channel state information
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3230003

Respiratory motion (i.e., motion pattern and rate) can provide valuable information for many medical situations. This information may help in the diagnosis of different health disorders and diseases. Wi-Fi-based respiratory monitoring schemes utilizing commercial off-the-shelf (COTS) devices can provide contactless, low-cost, simple, and scalable respiratory monitoring without requiring specialized hardware. Despite intense research efforts, an in-depth investigation on how to evaluate this type of technology is missing. We demonstrated and assessed the feasibility of monitoring and extracting human respiratory motion from Wi-Fi channel state information (CSI) data. This demonstration involves implementing an end-to-end system for a COTS-based hardware platform, control software, data acquisition, and a proposed processing algorithm. The processing algorithm is a novel deep-learning-based approach that exploits small changes in both CSI amplitude and phase information to learn high-level abstractions of breathing-induced chest movements and to reveal the unique characteristics of their difference. We also conducted extensive laboratory experiments demonstrating an assessment technique that can be replicated when quantifying the performance of similar systems. The results indicate that the proposed scheme can classify respiratory patterns and rates with an accuracy of 99.54% and 98.69%, respectively, in moderately degraded RF channels. Comprehensive data acquisition revealed the capability of the proposed system in detecting and classifying respiratory motions. Understanding the feasible limits and potential failure factors of Wi-Fi CSI-based respiratory monitoring scheme-and how to evaluate them-is an essential step toward the practical deployment of this technology. This study discusses ideas for further expansion of this technology.