Open AccessDietSkan: Food Volume Estimation for Dietary Intake Analysis Using 3D Mesh Scanning
Author(s) -
Sep Makhsous,
Jack Gentsch,
Joshua Rollins,
Zachary Feingold,
A.V. Mamishev
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i4.38.27876
Subject(s) - volume (thermodynamics) , calorie , scanner , tracking (education) , process (computing) , computer science , food intake , estimation , computer vision , artificial intelligence , engineering , medicine , psychology , pedagogy , physics , systems engineering , quantum mechanics , endocrinology , operating system
The prevalence of obesity, found in more than 38% of worldwide adults, is causing dietary measurements to become increasingly important. Most methods for tracking dietary intake utilize estimating the amount of food consumed to determine calories and nutritional content. Currently used methods of dietary tracking are either tedious or inaccurate. Our proposed method for dietary tracking is called DietSkan. It combines an off the shelf 3-Dimensional (3D) scanner, the Structure Sensor, with a smartphone application to produce a 3D reconstructed mesh scan of food items. The DietSkan process requires the desired food item to be scanned and exported for volume calculation. Then, using a 3D mesh manipulation tool, a 3D mesh, enclosing the consumed food, is constructed to obtain volume. The volume measurements achieved using the DietSkan algorithm average only 6% error and allow a user to track their dietary intake simply and effectively. The DietSkan system simplifies the estimation process and improves measurement accuracy when compared to current common practices.