Abstract
Smart infusion and insulin pumps are widely used these days. The prediction and decision making are indeed an important task within the cyber physical system environment. This paper is an attempt to provide the expert system for the optimal speed for the motor employed in infusion pump systems, according to the flow rate of the appropriate substance to be administered to the patient. The optimum flow of the dosage is necessary, and it can be performed by the appropriate speed of the motor. Hence, the motor speed and its revolution for the corresponding flow rate are calculated for all the glycemic conditions. In this research, the implementation of the artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) is utilized for prediction. The model is assessed by root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE). The performances in ANFIS has been compared with different membership functions such as trapezoidal, Pi, Gaussian, Gaussian bell, triangular membership function. The RMSE is obtained as 4.96899e−06 and MAPE is 1.33e−06 for trapezoidal membership function which is less when compared to the other membership functions as well as with ANN. The predictive decision analysis will be the supportive functionality for continuous subcutaneous insulin infusion (CSII) and continuous glucose monitoring (CGM) systems.
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Alamelu, J.V., Mythili, A. (2021). Prediction of Speed for Smart Insulin Pump Utilizing Adaptive Neuro-fuzzy Inference System and ANN. In: Sabut, S.K., Ray, A.K., Pati, B., Acharya, U.R. (eds) Proceedings of International Conference on Communication, Circuits, and Systems. Lecture Notes in Electrical Engineering, vol 728. Springer, Singapore. https://doi.org/10.1007/978-981-33-4866-0_24
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DOI: https://doi.org/10.1007/978-981-33-4866-0_24
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