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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.10489 |
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| _version_ | 1866916948653113344 |
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| author | Purkayastha, Saptarshi Bhagwat, Hrishikesh Sunchu, Keerthika Hoilett, Orlando Odari, Eddy Thuo, Reuben Wafula, Martin Kariuki, Celia Bucher, Sherri |
| author_facet | Purkayastha, Saptarshi Bhagwat, Hrishikesh Sunchu, Keerthika Hoilett, Orlando Odari, Eddy Thuo, Reuben Wafula, Martin Kariuki, Celia Bucher, Sherri |
| contents | Premature infant mortality remains a critical challenge in low- and middle-income countries (LMICs), with continuous vital sign monitoring being essential for early detection of life-threatening conditions. This paper presents an integrated system combining NeoWarm, a novel biomedical device, with NeoRoo, a mobile application, and NeoSmartML, a machine learning infrastructure, to enable comprehensive vital sign monitoring during Kangaroo Mother Care (KMC). Our power-optimized device achieves 6-6.5 days of continuous operation on a single charge, while the mobile application implements an offline-first architecture with efficient data synchronization. The optical character recognition pipeline demonstrates promising accuracy (F1 scores 0.78-0.875) for automated vital sign extraction from existing NICU monitors. Experimental validation shows the system's feasibility for deployment in resource-constrained settings, though further optimization of heart rate and temperature detection, along with the risk classification foundation model is needed. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_10489 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Development of AI-integrated infrastructure with biomedical device and mobile app for neonatal vital monitoring during and in between kangaroo care sessions Purkayastha, Saptarshi Bhagwat, Hrishikesh Sunchu, Keerthika Hoilett, Orlando Odari, Eddy Thuo, Reuben Wafula, Martin Kariuki, Celia Bucher, Sherri Signal Processing Systems and Control Premature infant mortality remains a critical challenge in low- and middle-income countries (LMICs), with continuous vital sign monitoring being essential for early detection of life-threatening conditions. This paper presents an integrated system combining NeoWarm, a novel biomedical device, with NeoRoo, a mobile application, and NeoSmartML, a machine learning infrastructure, to enable comprehensive vital sign monitoring during Kangaroo Mother Care (KMC). Our power-optimized device achieves 6-6.5 days of continuous operation on a single charge, while the mobile application implements an offline-first architecture with efficient data synchronization. The optical character recognition pipeline demonstrates promising accuracy (F1 scores 0.78-0.875) for automated vital sign extraction from existing NICU monitors. Experimental validation shows the system's feasibility for deployment in resource-constrained settings, though further optimization of heart rate and temperature detection, along with the risk classification foundation model is needed. |
| title | Development of AI-integrated infrastructure with biomedical device and mobile app for neonatal vital monitoring during and in between kangaroo care sessions |
| topic | Signal Processing Systems and Control |
| url | https://arxiv.org/abs/2509.10489 |