Design of an Infusion Monitoring System Based On Image Processing

At present, infusion monitoring heavily relies on nursing rounds or supervision. In many cases, nursing staff fail to remove patients' cannula in a timely manner after intravenous infusion due to negligence, which leads to serious swelling and blood backflow at the chosen venipuncture area over time, causing pain and even endangering patient lives. This study designed a smart real-time liquid level detection system based on image processing to solve this difficulty. By running the canny edge detection algorithm and Hough Transform (HT) algorithm on a Raspberry Pi computer with an industrial camera, the system extracted and calculated the image's pixels for judgment. As the number of pixels in the detected area reaches the alarm value, the system shall issue an alarm. In this experiment, a 62mm drip chamber was selected, and the system could achieve a high success rate of 98% with a set detection width of 5mm. The experimental results showed that whether the liquid level reached the alarm level could be accurately and effectively-identified utilizing the system. The information could be transmitted to the receiving end promptly with a high success rate, which verified the system's effectiveness. Given its real-time performance and high accuracy, the system has excellent application prospects.