NIVNGM  A Novel Noninvasive Blood Glucose Monitoring Method based on Nearinfrared Videos A Noninvasive blood Glucose Monitoring (NGM) method is desired for diabetes patients. Unfortunately, there is no practical NGM technique available for considering clinical accuracy requirements. One of the major causes is raised from the interferences of Environmental Background (EB) and Physiological Background (PB) noises. In this study, we propose a novel NGM method, called NIVNGM. Based on the nearinfrared video of a useru0027s finger, NIVNGM automatically separates the blood vessel and the background (other tissues) by an image segmentation algorithm and estimates the glucose level using features extracted from them. Compared with other NGM methods, NIVNGM can differentiate the glucoserelated information from the background, and also compensates for the measurement results due to the background noises. Clinical experimental results demonstrate that NIVNGM is robust and immune from EB and PB noise influence, while can achieve the expected monitoring accuracy  (Clarke(A)0.96) .

Automatic Estimation of the Nutritional Composition of Foods as Part of the GlucoseML Type 1 Diabetes SelfManagement System The daily care of type 1 diabetes has been considerably improved through the increased adoption of continuous glucose monitoring, continuous subcutaneous insulin infusion, and precise behavioral monitoring (diet, physical activity) mHealth solutions. In this study, we present the food recognition and nutrient estimation components of the GlucoseML system; a type 1 diabetes selfmanagement system relying on shortterm predictive analytics of the glucose trajectory. A computervisionbased approach is outlined combining image processing and machine learning to plate detection, food segmentation, food recognition and volume estimation of a plateu0027s content. The systematic collection of an annotated Greek food images dataset allows the evaluation of the proposed methodology.