Computer Vision Based Jaundice Monitoring for Smart Low-Cost Phototherapy Light System

This paper describes the development and enhancements of the Smart Low-cost Phototherapy Light System (Smart LPLS) at Ateneo Innovation Center (AIC) and partners. The developed Smart LPLS has custom-built LED panels that can output an irradiance of up to 83.89 μW/cm2/n; 2.7 times stronger than the m...

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Main Authors: Manabat, Bryan Kristofer A., Delos Reyes, Isaiah Dale L., Matibag, Jela Patricia R., Santiago, Paul Ryan A., Chaves, Steve Maverick, Cao, Reymond P., Cabacungan, Paul M., Oppus, Carlos M, Libatique, Nathaniel Joseph C, Tangonan, Gregory L
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Published: Archīum Ateneo 2023
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Online Access:https://archium.ateneo.edu/es-faculty-pubs/122
https://doi.org/10.1109/GHTC56179.2023.10354881
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Institution: Ateneo De Manila University
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spelling ph-ateneo-arc.es-faculty-pubs-11212024-04-01T08:52:00Z Computer Vision Based Jaundice Monitoring for Smart Low-Cost Phototherapy Light System Manabat, Bryan Kristofer A. Delos Reyes, Isaiah Dale L. Matibag, Jela Patricia R. Santiago, Paul Ryan A. Chaves, Steve Maverick Cao, Reymond P. Cabacungan, Paul M. Oppus, Carlos M Libatique, Nathaniel Joseph C Tangonan, Gregory L This paper describes the development and enhancements of the Smart Low-cost Phototherapy Light System (Smart LPLS) at Ateneo Innovation Center (AIC) and partners. The developed Smart LPLS has custom-built LED panels that can output an irradiance of up to 83.89 μW/cm2/n; 2.7 times stronger than the minimum required of 30 μW/cm2/nm for Neonatal Jaundice treatment. The integrated monitoring system uses an ESP32 camera module for capturing and sending images and a Raspberry Pi that enables Near Cloud storage, processing, and rendering capabilities. Using a modified version of Kovac's rules for skin pixel identification and K-means clustering for color quantization proved to be effective in extracting the dominant colors present within a skin region. Extracted colors are checked for yellow pigmentation based on their b∗ axis value on the CIE L*a*b∗ color space, which indicates yellowness (-) or blueness (+). This enables the tracking of increase and decrease in yellow pigmentation over time, with a configurable threshold for sending alerts through email at perceived dangerous levels. The Smart LPLS offers a cost-effective and efficient alternative to commercial phototherapy units, with the additional functionality of an integrated smart monitoring system. 2023-01-01T08:00:00Z text https://archium.ateneo.edu/es-faculty-pubs/122 https://doi.org/10.1109/GHTC56179.2023.10354881 Environmental Science Faculty Publications Archīum Ateneo Computer Vision Edge Computing Healthcare Biomedical Computer Engineering Computer Sciences Electrical and Computer Engineering Engineering Physical Sciences and Mathematics
institution Ateneo De Manila University
building Ateneo De Manila University Library
continent Asia
country Philippines
Philippines
content_provider Ateneo De Manila University Library
collection archium.Ateneo Institutional Repository
topic Computer Vision
Edge Computing
Healthcare
Biomedical
Computer Engineering
Computer Sciences
Electrical and Computer Engineering
Engineering
Physical Sciences and Mathematics
spellingShingle Computer Vision
Edge Computing
Healthcare
Biomedical
Computer Engineering
Computer Sciences
Electrical and Computer Engineering
Engineering
Physical Sciences and Mathematics
Manabat, Bryan Kristofer A.
Delos Reyes, Isaiah Dale L.
Matibag, Jela Patricia R.
Santiago, Paul Ryan A.
Chaves, Steve Maverick
Cao, Reymond P.
Cabacungan, Paul M.
Oppus, Carlos M
Libatique, Nathaniel Joseph C
Tangonan, Gregory L
Computer Vision Based Jaundice Monitoring for Smart Low-Cost Phototherapy Light System
description This paper describes the development and enhancements of the Smart Low-cost Phototherapy Light System (Smart LPLS) at Ateneo Innovation Center (AIC) and partners. The developed Smart LPLS has custom-built LED panels that can output an irradiance of up to 83.89 μW/cm2/n; 2.7 times stronger than the minimum required of 30 μW/cm2/nm for Neonatal Jaundice treatment. The integrated monitoring system uses an ESP32 camera module for capturing and sending images and a Raspberry Pi that enables Near Cloud storage, processing, and rendering capabilities. Using a modified version of Kovac's rules for skin pixel identification and K-means clustering for color quantization proved to be effective in extracting the dominant colors present within a skin region. Extracted colors are checked for yellow pigmentation based on their b∗ axis value on the CIE L*a*b∗ color space, which indicates yellowness (-) or blueness (+). This enables the tracking of increase and decrease in yellow pigmentation over time, with a configurable threshold for sending alerts through email at perceived dangerous levels. The Smart LPLS offers a cost-effective and efficient alternative to commercial phototherapy units, with the additional functionality of an integrated smart monitoring system.
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author Manabat, Bryan Kristofer A.
Delos Reyes, Isaiah Dale L.
Matibag, Jela Patricia R.
Santiago, Paul Ryan A.
Chaves, Steve Maverick
Cao, Reymond P.
Cabacungan, Paul M.
Oppus, Carlos M
Libatique, Nathaniel Joseph C
Tangonan, Gregory L
author_facet Manabat, Bryan Kristofer A.
Delos Reyes, Isaiah Dale L.
Matibag, Jela Patricia R.
Santiago, Paul Ryan A.
Chaves, Steve Maverick
Cao, Reymond P.
Cabacungan, Paul M.
Oppus, Carlos M
Libatique, Nathaniel Joseph C
Tangonan, Gregory L
author_sort Manabat, Bryan Kristofer A.
title Computer Vision Based Jaundice Monitoring for Smart Low-Cost Phototherapy Light System
title_short Computer Vision Based Jaundice Monitoring for Smart Low-Cost Phototherapy Light System
title_full Computer Vision Based Jaundice Monitoring for Smart Low-Cost Phototherapy Light System
title_fullStr Computer Vision Based Jaundice Monitoring for Smart Low-Cost Phototherapy Light System
title_full_unstemmed Computer Vision Based Jaundice Monitoring for Smart Low-Cost Phototherapy Light System
title_sort computer vision based jaundice monitoring for smart low-cost phototherapy light system
publisher Archīum Ateneo
publishDate 2023
url https://archium.ateneo.edu/es-faculty-pubs/122
https://doi.org/10.1109/GHTC56179.2023.10354881
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