Automatic Labeled LiDAR Data Generation based on Precise Human Model

Following improvements in deep neural networks, state-of-the-art networks have been proposed for human recognition using point clouds captured by LiDAR. However, the performance of these networks strongly depends on the training data. An issue with collecting training data is labeling. Labeling by humans is necessary to obtain the ground truth label; however, labeling requires huge costs. Therefore, we propose an automatic labeled data generation pipeline, for which we can change any parameters or data generation environments. Our approach uses a human model named Dhaiba and a background of Miraikan and consequently generated realistic artificial data. We present 500k+ data generated by the proposed pipeline. This paper also describes the specification of the pipeline and data details with evaluations of various approaches.Comment: Accepted at ICRA201

The Association between Concentrations of Green Tea and Blood Glucose Levels

Our objective was to examine whether habitual green tea consumption is associated with blood glucose levels and other biomarkers of glucose metabolism. We conducted a cross-sectional study of 35 male volunteers, 23–63 years old and residing in Shizuoka Prefecture in Japan. Biochemical data were measured and we conducted a questionnaire survey on health, lifestyle, and nutrition, as well as frequency of consumption and concentrations (1%, 2%, and 3%) of green tea. Men who consumed a 3% concentration of green tea showed lower mean values of fasting blood glucose and fructosamine than those who consumed a 1% concentration. Fasting blood glucose levels were found to be significantly associated with green tea concentration (β = −0.14, p = 0.03). However, green tea consumption frequency showed no significant differences in mean levels of blood glucose, fructosamine and hemoglobin A1c. In conclusion, our findings suggest that the consumption of green tea at a high concentration has the potential to reduce blood glucose levels