Efficient Pandemic Infection Detection Using Wearable Sensors and Machine Learning
Author | Abdel-Ghani, Ayah |
Author | Abughazzah, Zaineh |
Author | Akhund, Mahnoor |
Author | Abualsaud, Khalid |
Author | Yaacoub, Elias |
Available date | 2024-03-26T11:56:47Z |
Publication Date | 2023 |
Publication Name | 2023 International Wireless Communications and Mobile Computing, IWCMC 2023 |
Resource | Scopus |
Abstract | More than three years into the coronavirus disease 2019 (COVID-19) pandemic, it can be noted that the measures put in place for societies to manage the spread of this disease could have been better. For example, contact tracing mobile applications used to curb the spread of COVID-19 need additional enhancements to allow health care professionals to better understand the proliferation of the disease and to lessen the burden on hospitals and medical centers. In this paper, we present an intelligent solution to remotely self-monitor COVID-19 symptoms to help rapidly identify and detect suspected positives. The proposed intelligent solution is based on using a near-field communications (NFC) wristband that collects body temperature heart rate and SpO2 levels. It is connected to a dedicated mobile application to intelligently draw conclusions from the data (COVID-19 symptoms) it collects. Moreover, the application is trained to analyze cough sounds and detect the probability of infection. Results show more than 90% of detection accuracy. The proposed system can be adapted to future pandemics based on respiratory symptoms. |
Sponsor | ACKNOWLEDGMENT This publication was made possible by NPRP grant # 13-0205-200270 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. |
Language | en |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Subject | COVID-19 health machine learning pandemic symptoms |
Type | Conference Paper |
Pagination | 1562-1567 |
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