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Computer Vision-Based Carbohydrate Estimation for Type 1 Patients With Diabetes Using Smartphones by Marios Anthimopoulos; Joachim Dehais; Sergey Shevchik; Botwey H. Ransford; David Duke; Peter Diem; Stavroula Mougiakakou is a Medicine article available to read on EtoBox.

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## Background: Individuals with type 1 diabetes (t1d) have to count the carbohydrates (chos) of their meal to estimate the prandial insulin dose needed to compensate for the meal's effect on blood glucose levels. cho counting is very challenging but also crucial, since an error of 20 grams can substantially impair postprandial control. ## Method: The gocarb system is a smartphone application designed to support t1d patients with cho counting of nonpacked foods. in a typical scenario, the user places a reference card next to the dish and acquires 2 images with his/her smartphone. from these images, the plate is detected and the different food items on the plate are automatically segmented and recognized, while their 3d shape is reconstructed. finally, the food volumes are calculated and the cho content is estimated by combining the previous results and using the usda nutritional database. ## Results: To evaluate the proposed system, a set of 24 multi-food dishes was used. for each dish, 3 pairs of images were taken and for each pair, the system was applied 4 times. the mean absolute percentage error in cho estimation was 10 ± 12%, which led to a mean absolute error of 6 ± 8 cho gram

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Author

Marios Anthimopoulos; Joachim Dehais; Sergey Shevchik; Botwey H. Ransford; David Duke; Peter Diem; Stavroula Mougiakakou

Publisher

SAGE Publications

Published

2015

Language

EN

Field

Medicine (Health Sciences)