Detection and Spatial Correlation Analysis of Infectious Diseases Using Wireless Body Area Network Under Imperfect Wireless Channel
| Autor: | Ruhul Amin Khalil, Dost Muhammad Saqib Bhatti, Haewoon Nam, Nasir Saeed |
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| Rok vydání: | 2022 |
| Předmět: |
Spatial correlation
Information Systems and Management SARS-CoV-2 business.industry Computer science Deep learning Real-time computing COVID-19 Body movement Context (language use) Communicable Diseases Computer Science Applications Computer Communication Networks Body area network Humans Wireless Fading Artificial intelligence business Wireless Technology Information Systems Communication channel |
| Zdroj: | Big Data. 10:54-64 |
| ISSN: | 2167-647X 2167-6461 |
| DOI: | 10.1089/big.2021.0187 |
| Popis: | The biosensors on a human body form a wireless body area network (WBAN) that can examine various physiological parameters, such as body temperature, electrooculography, electromyography, electroencephalography, and electrocardiography. Deep learning can use health information from the embedded sensors on the human body that can help monitoring diseases and medical disorders, including breathing issues and fever. In the context of communication, the links between the sensors are influenced by fading due to diffraction, reflection, shadowing by the body, clothes, body movement, and the surrounding environment. Hence, the channel between sensors and the central unit (CU), which collects data from sensors, is practically imperfect. Therefore, in this article, we propose a deep learning-based COVID-19 detection scheme using a WBAN setup in the presence of an imperfect channel between the sensors and the CU. Moreover, we also analyze the impact of correlation on WBAN by considering the imperfect channel. Our proposed algorithm shows promising results for real-time monitoring of COVID-19 patients. |
| Databáze: | OpenAIRE |
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