Wi-Pulmo: Commodity WiFi Can Capture Your Pulmonary Function Without Mouth Clinging

Pulmonary function testing is a crucial examination for respiratory diseases. Current medical spirometers are bulky and inconvenient, while available portable spirometers are extremely expensive and often lack accuracy. Furthermore, both devices require direct contact, inevitably increasing the cros...

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Veröffentlicht in:IEEE internet of things journal Jg. 12; H. 1; S. 854 - 868
Hauptverfasser: Zhao, Peng, Huang, Jinyang, Zhang, Xiang, Liu, Zhi, Yan, Huan, Wang, Meng, Zhuang, Guohang, Guo, Yutong, Sun, Xiao, Li, Meng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.01.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Accuracy
Algorithms
Contact
Correlation
Deep learning
Dynamics
Effectiveness
Exhalation
Lung
Lung cancer
Machine learning
Noise
Older adults
Pepper spray
Portable equipment
Pulmonary function
Pulmonary functions
Respiratory diseases
Spirometers
Spirometry
the elderly
Tumors
variational encoder-decoder (VED) network
WiFi sensing
Wireless fidelity
ISSN:2327-4662, 2327-4662
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Zusammenfassung:Pulmonary function testing is a crucial examination for respiratory diseases. Current medical spirometers are bulky and inconvenient, while available portable spirometers are extremely expensive and often lack accuracy. Furthermore, both devices require direct contact, inevitably increasing the cross-infection risk. To tackle these challenges, we propose Wi-Pulmo, an end-to-end deep learning-based Wireless System that utilizes WiFi channel state information (CSI) to provide contact-free, convenient, cost-effective, and precise pulmonary function testing outside the clinical setting. Based on the analysis of thoracic and abdominal movement patterns, Wi-Pulmo first validates the feasibility of using WiFi to estimate pulmonary function. Then, Wi-Pulmo designs an efficient fine-grained sensing quality-based algorithm for complete exhalation segmentation. Additionally, a relevant interference-tolerant learning algorithm based on variational inference is proposed to accurately map the CSI of WiFi signals to pulmonary function. Extensive experiments achieved average monitoring error rates of 2.59% for normal subjects in daily scenarios and 5.87% for real patients in tertiary hospitals over a two-month period. These satisfactory results demonstrate the strong effectiveness and robustness of Wi-Pulmo. Furthermore, our findings in clinical reveal a close correlation between chronic diseases and pulmonary function.
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ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2024.3470321