DietSkan: Food Volume Estimation for Dietary Intake Analysis Using 3D Mesh Scanning

  • Authors

    • Sep Makhsous
    • Jack Gentsch
    • Joshua Rollins
    • Zachary Feingold
    • Alexander Mamishev
    2018-12-03
    https://doi.org/10.14419/ijet.v7i4.38.27876
  • dietary measurement, 3D mech analysis, volume estimation, and 3D reconstruction.

  • Abstract

    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.

     

     

  • References

    1. [1] Kong, Fanyu, He, Hongsheng, Raynor, Hollie A., & Tan, Jindong. (2015). DietCam: Multi-view regular shape food recognition with a camera phone. Pervasive and Mobile Computing, 19, 108-121. doi:https://doi.org/10.1016/j.pmcj.2014.05.012

      [2] Kong, Fanyu, & Tan, Jindong. (2012). DietCam: Automatic dietary assessment with mobile camera phones. Pervasive and Mobile Computing, 8(1), 147-163. doi:https://doi.org/10.1016/j.pmcj.2011.07.003

      [3] Monteiro, Carlos A., & Cannon, Geoffrey. (2015). Calories do not add up. Public Health Nutrition; Cambridge, 18(4), 569-570. doi:http://dx.doi.org/10.1017/S1368980015000014

      [4] Occipital. (2018). Structure Sensor 3D Scanner. Retrieved from https://occipital.com/

      [5] Pais, S, Parry, D, Petrova, K, & Rowan, J. (2018). Acceptance of Using an Ecosystem of Mobile Apps for Use in Diabetes Clinic for Self-Management of Gestational Diabetes Mellitus. Paper presented at the MEDINFO 2017.

      [6] Pouladzadeh, Parisa. (2017). A Cloud-Assisted Mobile Food Recognition System. Université d'Ottawa/University of Ottawa,

      [7] Pouladzadeh, Parisa, & Shirmohammadi, Shervin. (2017). Mobile multi-food recognition using deep learning. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 13(3s), 36.

      [8] Pouladzadeh, Parisa, Shirmohammadi, Shervin, Bakirov, Aslan, Bulut, Ahmet, & Yassine, Abdulsalam. (2015). Cloud-based SVM for food categorization. Multimedia Tools and Applications, 74(14), 5243-5260. doi:10.1007/s11042-014-2116-x

      [9] Primer, Dietary Assessment. (2018, 2016/10/19/21:24:24). 24-hour Dietary Recall (24HR) At a Glance. Retrieved from https://dietassessmentprimer.cancer.gov/profiles/recall/

      [10] Shirmohammadi, S., & Ferrero, A. (2014). Camera as the instrument: the rising trend of vision based measurement. IEEE Instrumentation & Measurement Magazine, 17(3), 41-47. doi:10.1109/MIM.2014.6825388

      [11] Yanovski, Jack A. (2018). Obesity: Trends in underweight and obesity—scale of the problem. Nature Reviews Endocrinology, 14(1), 5.

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  • How to Cite

    Makhsous, S., Gentsch, J., Rollins, J., Feingold, Z., & Mamishev, A. (2018). DietSkan: Food Volume Estimation for Dietary Intake Analysis Using 3D Mesh Scanning. International Journal of Engineering & Technology, 7(4.38), 1368-1371. https://doi.org/10.14419/ijet.v7i4.38.27876

    Received date: 2019-02-24

    Accepted date: 2019-02-24

    Published date: 2018-12-03