ArticleIEEE sensors journal2021
Wireless Channel Modelling for Identifying Six Types of Respiratory Patterns With SDR Sensing and Deep Multilayer Perceptron.
Article in IEEE sensors journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 36 citations in OpenAlex.
- Auscultation-Based Pulmonary Disease Detection through Parallel Transformation and Deep Learning.Bioengineering (Basel, Switzerland) · 2024Article
- Non-Contact Heart Rate Monitoring Method Based on Wi-Fi CSI Signal.Sensors (Basel, Switzerland) · 2024Article
- Non-Contact Measurement of Cardiopulmonary Activity Using Software Defined Radios.IEEE journal of translational engineering in health and medicine · 2024Article
- Doppler radar remote sensing of respiratory function.Frontiers in physiology · 2023Review
- Intelligent Reflecting Surface-Based Non-LOS Human Activity Recognition for Next-Generation 6G-Enabled Healthcare System.Sensors (Basel, Switzerland) · 2022Article
- Multiple Participants' Discrete Activity Recognition in a Well-Controlled Environment Using Universal Software Radio Peripheral Wireless Sensing.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
7 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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Registered trials
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