A Software-Defined Sensor System Using Semantic Segmentation for Monitoring Remaining Intravenous Fluids
Accurate intravenous (IV) fluid monitoring is critical in healthcare to prevent infusion errors and ensure patient safety. Traditional monitoring methods often depend on dedicated hardware, such as weight sensors or optical systems, which can be costly, complex, and challenging to scale across diver...
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MDPI AG
2025-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/10/3082 |
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| author | Hasik Sunwoo Seungwoo Lee Woojin Paik |
| author_facet | Hasik Sunwoo Seungwoo Lee Woojin Paik |
| author_sort | Hasik Sunwoo |
| collection | DOAJ |
| description | Accurate intravenous (IV) fluid monitoring is critical in healthcare to prevent infusion errors and ensure patient safety. Traditional monitoring methods often depend on dedicated hardware, such as weight sensors or optical systems, which can be costly, complex, and challenging to scale across diverse clinical settings. This study introduces a software-defined sensing approach that leverages semantic segmentation using the pyramid scene parsing network (PSPNet) to estimate the remaining IV fluid volumes directly from images captured by standard smartphones. The system identifies the IV container (vessel) and its fluid content (liquid) using pixel-level segmentation and estimates the remaining fluid volume without requiring physical sensors. Trained on a custom IV-specific image dataset, the proposed model achieved high accuracy with mean intersection over union (mIoU) scores of 0.94 for the vessel and 0.92 for the fluid regions. Comparative analysis with the segment anything model (SAM) demonstrated that the PSPNet-based system significantly outperformed the SAM, particularly in segmenting transparent fluids without requiring manual threshold tuning. This approach provides a scalable, cost-effective alternative to hardware-dependent monitoring systems and opens the door to AI-powered fluid sensing in smart healthcare environments. Preliminary benchmarking demonstrated that the system achieves near-real-time inference on mobile devices such as the iPhone 12, confirming its suitability for bedside and point-of-care use. |
| format | Article |
| id | doaj-art-a471f4fa3131434da8dc6f3a30bdf3ee |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-a471f4fa3131434da8dc6f3a30bdf3ee2025-08-20T03:12:04ZengMDPI AGSensors1424-82202025-05-012510308210.3390/s25103082A Software-Defined Sensor System Using Semantic Segmentation for Monitoring Remaining Intravenous FluidsHasik Sunwoo0Seungwoo Lee1Woojin Paik2Department of Computer Engineering, Konkuk University Glocal Campus, 268 Chungwon-daero, Chungju-si 27478, Chungcheongbuk-do, Republic of KoreaDepartment of Computer Engineering, Konkuk University Glocal Campus, 268 Chungwon-daero, Chungju-si 27478, Chungcheongbuk-do, Republic of KoreaDepartment of Computer Engineering, Konkuk University Glocal Campus, 268 Chungwon-daero, Chungju-si 27478, Chungcheongbuk-do, Republic of KoreaAccurate intravenous (IV) fluid monitoring is critical in healthcare to prevent infusion errors and ensure patient safety. Traditional monitoring methods often depend on dedicated hardware, such as weight sensors or optical systems, which can be costly, complex, and challenging to scale across diverse clinical settings. This study introduces a software-defined sensing approach that leverages semantic segmentation using the pyramid scene parsing network (PSPNet) to estimate the remaining IV fluid volumes directly from images captured by standard smartphones. The system identifies the IV container (vessel) and its fluid content (liquid) using pixel-level segmentation and estimates the remaining fluid volume without requiring physical sensors. Trained on a custom IV-specific image dataset, the proposed model achieved high accuracy with mean intersection over union (mIoU) scores of 0.94 for the vessel and 0.92 for the fluid regions. Comparative analysis with the segment anything model (SAM) demonstrated that the PSPNet-based system significantly outperformed the SAM, particularly in segmenting transparent fluids without requiring manual threshold tuning. This approach provides a scalable, cost-effective alternative to hardware-dependent monitoring systems and opens the door to AI-powered fluid sensing in smart healthcare environments. Preliminary benchmarking demonstrated that the system achieves near-real-time inference on mobile devices such as the iPhone 12, confirming its suitability for bedside and point-of-care use.https://www.mdpi.com/1424-8220/25/10/3082smart healthcaredeep learningsemantic segmentationPSPNetmedical image processingIV fluid monitoring |
| spellingShingle | Hasik Sunwoo Seungwoo Lee Woojin Paik A Software-Defined Sensor System Using Semantic Segmentation for Monitoring Remaining Intravenous Fluids Sensors smart healthcare deep learning semantic segmentation PSPNet medical image processing IV fluid monitoring |
| title | A Software-Defined Sensor System Using Semantic Segmentation for Monitoring Remaining Intravenous Fluids |
| title_full | A Software-Defined Sensor System Using Semantic Segmentation for Monitoring Remaining Intravenous Fluids |
| title_fullStr | A Software-Defined Sensor System Using Semantic Segmentation for Monitoring Remaining Intravenous Fluids |
| title_full_unstemmed | A Software-Defined Sensor System Using Semantic Segmentation for Monitoring Remaining Intravenous Fluids |
| title_short | A Software-Defined Sensor System Using Semantic Segmentation for Monitoring Remaining Intravenous Fluids |
| title_sort | software defined sensor system using semantic segmentation for monitoring remaining intravenous fluids |
| topic | smart healthcare deep learning semantic segmentation PSPNet medical image processing IV fluid monitoring |
| url | https://www.mdpi.com/1424-8220/25/10/3082 |
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