Predicting the bladder urinary volume with a reabsorbed primitive urine model

Abstract

With the rapid aging of the population, urination management is one of the challenges experienced in nursing homes. Although constrained devices, such as ultrasonic sensors, have been used for urination management, and they can sequentially measure urinary volume in the bladder, unconstrained methods to obtain urinary volume are needed. To accomplish such goals, a mathematical model is required that considers the nature of the bladder, especially reabsorption of the primitive urine. In this paper, we propose a model based on the primary delay system with five parameters, which are determined based on the absorption spectrum of urine that is obtained immediately after urination, through regression analysis. In the regression analysis, the values of the five parameters and the absorption spectrum of urine are objective and explanatory variables, respectively, and the partial regression coefficients are determined through a genetic algorithm. When the values of the five parameters are estimated using the absorption spectrum of urine immediately after urination, we can predict the next time series of the urinary volume in the bladder based on the model. Finally, the predicted urinary volume is corrected using a multitask Gaussian process and the final predicted urinary volume is obtained. We performed a series of experiments to evaluate the proposed method and calculated the error rate between the actual urinary volume and the urinary volume predicted using the proposed method at the time of urination. The mean error rate of the proposed method is 13.32%.

Keywords

• urinary tract infection
• urinary volume
• absorption spectrum
• genetic algorithm
• multitask gaussian process