期刊
IEEE SENSORS JOURNAL
卷 21, 期 18, 页码 20833-20840出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSEN.2021.3096641
关键词
Sensors; COVID-19; Monitoring; Wireless fidelity; Wireless communication; OFDM; Wireless sensor networks; COVID-19; abnormal respiratory; non-invasive; USRP; CSI; software defined radio; neural network
资金
- Coventry University internal Ph.D. studentship program
Contactless or non-invasive technology has a significant impact on healthcare applications, especially during the COVID-19 pandemic. The use of Software Defined Radio (SDR) and supervised machine learning algorithm provides a platform for effectively detecting and classifying various respiratory patterns with up to 99% accuracy.
Contactless or non-invasive technology has a significant impact on healthcare applications such as the prediction of COVID-19 symptoms. Non-invasive methods are essential especially during the COVID-19 pandemic as they minimise the burden on healthcare personnel. One notable symptom of COVID-19 infection is a rapid respiratory rate, which requires constant real-time monitoring of respiratory patterns. In this paper, Software Defined Radio (SDR) based Radio-Frequency sensing technique and supervised machine learning algorithm is employed to provide a platform for detecting and monitoring various respiratory: eupnea, biot, bradypnea, sighing, tachypnea, and kussmaul. The variations in Channel State Information produced by human respiratory were utilised to identify distinct respiratory patterns using fine-grained Orthogonal Frequency-Division Multiplexing signals. The proposed platform based on the SDR and the Deep Multilayer Perceptron classifier exhibits the ability to effectively detect and classify the afore-mentioned distinct respiratory with an accuracy of up to 99%. Moreover, the effectiveness of the proposed scheme in terms of diagnosis accuracy, precision, recall, F1-score, and confusion matrix is demonstrated by comparison with a state-of-the-art machine learning classifier: Random Forest.
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