Multi-sensor data fusion for non-invasive continuous glucose monitoring

Measurements of impedance spectra used for non-invasive glucose monitoring are affected by a variety of perturbing factors such as temperature and sweat/moisture fluctuations, changes in perfusion, and body movements. In order to quantify and compensate for these perturbing effects, a multi-sensor approach was suggested. Different sensors are used, measuring signals correlated with blood glucose and perturbing factors, respectively. Here, we investigate how the multiple sensor data can be transformed into meaningful information about changes in the concentration of blood glucose. Linear regression models and variable selection (stepwise for/back-ward and lasso) techniques are used to derive generally valid models allowing for the estimation of blood glucose concentration. We find that over-fitting is best avoided by using a special version of cross-validated prediction error as the model selection criterion. Indeed, the resulting models are reasonably small, plausible, and comprise an additive adjustment for the experimental run.