Infrared Infusion Monitor Based on Data Dimensionality Reduction and Logistics Classifier

• Main Authors: Xiaoli Wang, Haonan Zhou, Yong Song
• Format: Article
• Language: English
• Published: MDPI AG 2020-04-01
• Series: Processes
• Subjects: drop count; logistics classifier; data dimensionality reduction
• Online Access: https://www.mdpi.com/2227-9717/8/4/437

This paper presents an infrared infusion monitoring method based on data dimensionality reduction and a logistics classifier. In today’s social environment, nurses with hospital infusion work are under excessive pressure. In order to improve the information level of the traditional medical process, hospitals have introduced a variety of infusion monitoring devices. The current infusion monitoring equipment mainly adopts the detection method of infrared liquid drop detection to realize non-contact measurements. However, a large number of experiments have found that the traditional infrared detection method has the problems of low voltage signal amplitude variation and low signal-to-noise ratio (SNR). Conventional threshold judgment or signal shaping cannot accurately judge whether droplets exist or not, and complex signal processing circuits can greatly increase the cost and power consumption of equipment. In order to solve these problems, this paper proposes a method for the accurate measurement of droplets without increasing the cost, that is, a method combining data drop and a logistics classifier. The dimensionalized data and time information are input into the logistics classifier to judge the drop landing. The test results show that this method can significantly improve the accuracy of droplet judgment without increasing the hardware cost.