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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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10870051/ |
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| author | Sumaiya Afroz Mila Bhagawat Baanav Yedla Ravi Md Rafiul Kabir Sandip Ray |
| author_facet | Sumaiya Afroz Mila Bhagawat Baanav Yedla Ravi Md Rafiul Kabir Sandip Ray |
| author_sort | Sumaiya Afroz Mila |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a66390e8d93d4d8f9af7838f63fd9f45 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-a66390e8d93d4d8f9af7838f63fd9f452025-02-11T00:01:16ZengIEEEIEEE Access2169-35362025-01-0113241082412310.1109/ACCESS.2025.353851810870051MASC: Wearable Design for Infectious Disease Detection Through Machine LearningSumaiya Afroz Mila0https://orcid.org/0000-0002-0989-5571Bhagawat Baanav Yedla Ravi1https://orcid.org/0000-0002-1058-2134Md Rafiul Kabir2https://orcid.org/0000-0002-8251-4368Sandip Ray3Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, USADepartment of Electrical and Computer Engineering, University of Florida, Gainesville, FL, USASchool of Engineering and Technology, Central Michigan University, Mount Pleasant, MI, USADepartment of Electrical and Computer Engineering, University of Florida, Gainesville, FL, USAWe 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.https://ieeexplore.ieee.org/document/10870051/Wearablemachine learningtime series analysisinfection detection |
| spellingShingle | Sumaiya Afroz Mila Bhagawat Baanav Yedla Ravi Md Rafiul Kabir Sandip Ray MASC: Wearable Design for Infectious Disease Detection Through Machine Learning IEEE Access Wearable machine learning time series analysis infection detection |
| title | MASC: Wearable Design for Infectious Disease Detection Through Machine Learning |
| title_full | MASC: Wearable Design for Infectious Disease Detection Through Machine Learning |
| title_fullStr | MASC: Wearable Design for Infectious Disease Detection Through Machine Learning |
| title_full_unstemmed | MASC: Wearable Design for Infectious Disease Detection Through Machine Learning |
| title_short | MASC: Wearable Design for Infectious Disease Detection Through Machine Learning |
| title_sort | masc wearable design for infectious disease detection through machine learning |
| topic | Wearable machine learning time series analysis infection detection |
| url | https://ieeexplore.ieee.org/document/10870051/ |
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