MASC: Wearable Design for Infectious Disease Detection Through Machine Learning
We present an innovative approach for designing a wearable solution that utilizes machine learning to systematically optimize the monitoring of vital signs for early detection of COVID-19 infections in symptomatic patients. This approach correlates sensor data trends with disease predictions, utiliz...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10870051/ |
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| Summary: | We present an innovative approach for designing a wearable solution that utilizes machine learning to systematically optimize the monitoring of vital signs for early detection of COVID-19 infections in symptomatic patients. This approach correlates sensor data trends with disease predictions, utilizing existing hospital patient data to enhance diagnosis accuracy. Our methodology offers a scalable, cost-effective solution to manage and prevent infectious diseases beyond COVID-19, addressing the limitations of traditional diagnostic methods. A functional prototype has been developed, supporting the effectiveness of continuous health monitoring in infection detection. The wearable continuously monitors key vitals such as body temperature, heart rate, respiratory rate, and oxygen saturation levels, providing an early warning system for timely medical intervention. This wearable device holds promise for transforming infectious disease detection and management, benefiting healthcare professionals and individuals alike. |
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| ISSN: | 2169-3536 |