BibTex Citation Data :
@article{JPA31636, author = {Hotromasari Dabukke and Flora Sijabat and Salomo Sijabat and Sri Ulina and Mhd Aldi Primasyukra}, title = {Characterization and Performance Evaluation of the TCRT5000 Reflective Optical Sensor for Early Detection of Flow Disturbances in an IoT-Based Peristaltic Infusion System}, journal = {Journal of Physics and Its Applications}, volume = {8}, number = {3}, year = {2026}, keywords = {TCRT5000 reflective optical sensor; Peristaltic infusion; Flow disturbance detection; Internet of Things; Sensor characterization}, abstract = { Flow disturbances in peristaltic infusion systems, including reduced drip rates, unstable dripping, and occlusion, may compromise infusion therapy and increase patient safety risks. This study aimed to characterize the performance of a TCRT5000 reflective optical sensor integrated into an Internet of Things (IoT)-based peristaltic infusion monitoring system for early detection of infusion flow disturbances. The proposed prototype consisted of a TCRT5000 reflective optical sensor for drip detection, a NodeMCU ESP32 for data acquisition and processing, Firebase for cloud-based data storage, and an MIT App Inventor mobile application for real-time monitoring. Experiments were conducted at infusion flow rates of 20, 50, 80, 100, 150, 200, 250, 300, 350, and 400 drops/min. Sensor performance was evaluated through sensitivity, linearity, and repeatability analyses. The results showed that the sensor exhibited a sensitivity ranging from 0.78 to 1.25 ADC/(drops/min) and a highly linear response described by the regression equation y = 1.0082x + 3.2590, with a coefficient of determination R² = 0.9992 and a correlation coefficient r = 0.9996. Repeatability analysis produced coefficients of variation below 5% for most measurements, indicating consistent sensor performance, although slightly higher variations were observed at lower flow rates. These findings demonstrate that the TCRT5000 reflective optical sensor provides reliable sensitivity, linearity, and repeatability, supporting its application as a sensing element in IoT-based infusion monitoring and the development of early infusion flow disturbance detection systems. }, issn = {2622-5956}, doi = {10.14710/jpa.v8i3.31636}, url = {https://ejournal2.undip.ac.id/index.php/jpa/article/view/31636} }
Refworks Citation Data :
Flow disturbances in peristaltic infusion systems, including reduced drip rates, unstable dripping, and occlusion, may compromise infusion therapy and increase patient safety risks. This study aimed to characterize the performance of a TCRT5000 reflective optical sensor integrated into an Internet of Things (IoT)-based peristaltic infusion monitoring system for early detection of infusion flow disturbances. The proposed prototype consisted of a TCRT5000 reflective optical sensor for drip detection, a NodeMCU ESP32 for data acquisition and processing, Firebase for cloud-based data storage, and an MIT App Inventor mobile application for real-time monitoring. Experiments were conducted at infusion flow rates of 20, 50, 80, 100, 150, 200, 250, 300, 350, and 400 drops/min. Sensor performance was evaluated through sensitivity, linearity, and repeatability analyses. The results showed that the sensor exhibited a sensitivity ranging from 0.78 to 1.25 ADC/(drops/min) and a highly linear response described by the regression equation y = 1.0082x + 3.2590, with a coefficient of determination R² = 0.9992 and a correlation coefficient r = 0.9996. Repeatability analysis produced coefficients of variation below 5% for most measurements, indicating consistent sensor performance, although slightly higher variations were observed at lower flow rates. These findings demonstrate that the TCRT5000 reflective optical sensor provides reliable sensitivity, linearity, and repeatability, supporting its application as a sensing element in IoT-based infusion monitoring and the development of early infusion flow disturbance detection systems.
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