Early Fluid Resuscitation of Burn Patients Based on High-Precision Weighing System

Correct and continuous weight measurement of a burn patient is important for fluid resuscitation. Generally, a body weight bed uses four sensors to obtain signals. However, owing to signals dynamic characteristics, the air fluidized therapy bed has a large error in this mode, which is difficult to reduce. In this study, we optimized the sensor configuration scheme and developed a set of signal processing and methods to evaluate noise reduction. Additionally, the wavelet neural network was used to improve the weighing accuracy. Furthermore, medical tools are located on different body parts of a patient to acquire weight characteristics to assist with the rehydration of patient. The average error of 183.33g is acceptable from a medical perspective. An average error of 250.21g in dynamic weight after using wavelet de-noising and wavelet neural network methods was obtained.