AbstractThe traditional blood glucose estimation method requires to take the invasive measurements several times a day. Therefore, it has a high infection risk and the users are suffered from the pain. Moreover, the long term consumable cost is high. Recently, the wearable and non‐invasive blood glucose estimation approach has been proposed. However, due to the unreliability of the acquisition device, the presence of the noise and the variations of the acquisition environments, the obtained features and the reference blood glucose values are highly unreliable. Moreover, different subjects have different responses of the infrared light to the blood glucose. To address this issue, a polynomial fitting approach to smooth the obtained features or the reference blood glucose values has been proposed. In particular, the design of the coefficients in the polynomial is formulated as the various optimisation problems. First, the blood glucose values are estimated based on the individual optimisation approaches. Second, the absolute difference values between the estimated blood glucose values and the actual blood glucose values based on each optimisation approach are computed. Third, these absolute difference values for each optimisation approach are sorted in the ascending order. Fourth, for each sorted blood glucose value, the optimisation method corresponding to the minimum absolute difference value is selected. Fifth, the accumulate probability of each selected optimisation method is computed. If the accumulate probability of any selected optimisation method at a point is greater than a threshold value, then the accumulate probabilities of these three selected optimisation methods at that point are reset to zero. A range of the sorted blood glucose values are defined as that with the corresponding boundaries points being the previous reset point and this reset point. Hence, after performing the above procedures for all the sorted reference blood glucose values in the validation set, the regions of the sorted reference blood glucose values and the corresponding optimisation methods in these regions are determined. It is worth noting that the conventional lowpass denoising method was performed in the signal domain (either in the time domain or in the frequency domain), while the authors’ proposed method is performed in the feature space or the reference blood glucose space. Hence, the authors’ proposed method can further improve the reliability of the obtained feature values or the reference blood glucose values so as to improve the accuracy of the blood glucose estimation. Moreover, the individual modelling regression method has been employed here to suppress the effects of different users having different responses of the infrared light to the blood glucose values. The computer numerical simulation results show that the authors’ proposed method yields the mean absolute relative deviation (MARD) at 0.0930 and the percentage of the test data falling in the zone A of the Clarke error grid at 94.1176%.