Studying electronic blood pressure monitor digital recognition algorithm based on computer vision and design

Abstract

This study proposed an intelligent algorithm based on digital image processing and character recognition to address the current situation in which the National Medical Products Administration promulgated the relevant regulations on the complete prohibition of the use of mercury sphygmomanometers in 2020 and the currently widely used electronic sphygmomanometers need to undergo regular verification and quality testing. The intelligent algorithm made it possible to automatically acquire the electronic sphygmomanometer indication value during quality assurance or verification. The images of the electronic sphygmomanometer were captured using a Raspberry Pi-connected video camera head; in the software development, automatic detection of the electronic sphygmomanometer's indication value was achieved by running the computer vision-based OpenCV library on the Raspberry Pi and utilizing image preprocessing techniques like scale transformation, grayscale conversion, Gaussian smoothing and edge detection, and character segmentation. The development of intelligent devices for the automatic verification of electronic sphygmomanometers has a technical foundation in the research and design of the digital recognition algorithm, and it has a certain reference value for character recognition of electronic instruments or the creation of automatic instrument indication value recording devices.

Keywords

• Electronic sphygmomanometer
• Digital recognition
• Computer vision
• Raspberry Pi
• OpenCV