In this paper, a non-invasive blood glucose sensing system is presented using near infra-red(NIR) spectroscopy. The signal from the NIR optodes is processed using artificial neural networks (ANN) to estimate the glucose level in blood. In order to obtain accurate values of the synaptic weights of the ANN, inverse delayed (ID) function model of neuron has been used. The ANN model has been implemented on field programmable gate array (FPGA). Error in estimating glucose levels using ANN based on ID function model of neuron implemented on FPGA, came out to be 1.02 mg/dl using 15 hidden neurons in the hidden layer as against 5.48 mg/dl using ANN based on conventional neuron model.

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This paper proposes a non invasive blood glucose sensing system using photoplethysmography (PPG). Neural network based adaptive noise cancellation (adaline) is employed to reduce the motion artifact. Also artificial neural network is used to create the predictive model which estimates the glucose levels based on PPG signals. Error in estimating glucose levels came out to be 5.48 mg/dl using ANN on MATLAB. This predictive model created by ANN has been implemented on FPGA. Error in estimating glucose levels by the ANN model implemented on FPGA, came out to be 7.23mg/dl. The results have been validated by performing Clarke error grid analysis.

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