# Overcoming Individual Discrepancies, a Learning Model for Non-Invasive Blood Glucose Measurement
## 1 Introduction
In the light propagation path in skin tissue, there are many individual factors that can affect the final spectrum and glucose measurement results, e.g., the thickness and color of skin \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type** & **Device** & **Target site** & **Principle** & **Progress** & **Problems** \\ \hline \multirow{7}{*}{Mini-invasive} & Abbott & Arm & Glucose & & & \\  & _FreeStyle1\({}^{\text{TM}}\)_ & subcutaneous & & oxidase & CE and FDA certified & 1. Skin damage and prone to infection; \\  & _Libre_ & tissue fluid & & & & 2. Time lag in glucose changing; \\ \cline{2-6}  & Echo & Abdominal & & & & 3. Poor accuracy in hypoglycemia; \\  & Therapeutic & subcutaneous & & & 4. _FreeStyle1\({}^{\text{TM}}\) Libre_ needs to be replaced every 14 days; \\  & _Symphy1\({}^{\text{TM}}\)_ & interstitial fluid & & & & 5. GS/\(G6^{\text{TM}}\) needs to be calibrated every 12 h; \\ \cline{2-6}  & Decom & Abdominal & & Glucose & & \\  & _GS/G6\({}^{\text{TM}}\)_ & subcutaneous & & & & \\  & _GlucowWatch1\({}^{\text{TM}}\)_ & & & & & \\  & & & & & & \\ \hline \multirow{7}{*}{Non-invasive} & Google Smart & & & & & \\  & contact lens & & & & & \\ \cline{2-6}  & \multirow{2}{*}{Tears} & Absorption & & & 1. Low accuracy; \\  & _TensorTiP1\({}^{\text{TM}}\)_ & & & & 2. Need more than 100 invasive data for \\  & CoG & & & & & \\ \cline{2-6}  & Cs & & & & & \\ \cline{2-6}  & MedSensors & Abdominal & Raman & CE certified & 1. Sensitive to environmental noise; \\  & _HG1\(<\)_ & & & & 2. The measurement principle is not clear; \\ \cline{2-6}  & Integrity & & & & 3. Susceptible to individual differences \\ Applications & Earbbe & & electricity, & & & and physiological background changes; \\  & _GlucoT_ Arch1\({}^{\text{TM}}\) & & thermal & & & 4. Some optical components are expensive; \\ \cline{2-6}  & LifeTac & & & & & 5. Low accuracy; \\  & _SugarTac1\({}^{\text{TM}}\)_ & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison and analysis of mini-invasive and non-invasive blood glucose meters. CE:Communate Europpene, FDA:U.S. Food and Drug Administration. tissue, cuticle, epidermis, dermis and subcutaneous tissue, muscle, bone, etc. [13]. In non-invasive optical measurement of other blood components, e.g., blood oxygen, hemoglobin, etc., some methods have been proposed which are able to eliminate the interference of IDs. The dynamic spectroscopy method is able to extract the information of blood's components, removing background and noise at the same time [14]. Double-sampling is used to improve signal-to-noise ratio of dynamic spectroscopy [15]. In the field of NGM, different measurement methods need different ways to overcome IDs, which needs further research. In this paper, a learning NGM model that can overcome IDs, to a certain extent, is presented. This model is based on human earlobe light absorbances with five different wavelengths and a glucose estimating algorithm with automatically parameters tuning for individuals. The earlobe was chosen due to the absence of bone tissues, and also because of its relatively small thickness [16]. We choose three wavelengths (\(730,850\), and \(930\) nm) in the near infrared wavelength range whose absorbance mainly depends on the amount of glucose in blood to measure blood glucose level. In order to overcome the IDs, green (\(490\) nm) and red (\(660\) nm) light is used to estimate the tissue thickness and blood volume separately to compensate the measurements [12]. Based on the linear relation between absorbances of the five lights and blood components, we use Independent Component Analysis (ICA) [17] to extract independent components from transmittances data, and use them as new features data. Glucose concentration is obtained with Random Forest (RF) [18], and test its performance with a reference invasive glucose meter in terms of measurement accuracy. ICA is a method for separating complex signals to independent signals by matrix decomposition which is frequently used in signal processing [17].