EatAR tango: portion estimation on mobile devices with a depth sensor

R. Dinic, Michael Domhardt, Simon W. Ginzinger, Thomas Stütz
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引用次数: 4

Abstract

The accurate assessment of nutrition information is a challenging task, but crucial for people with certain diseases, such as diabetes. An important part of the assessment of nutrition information is portion estimation, i.e. volume estimation. Given the volume and the food type, the nutrition information can be computed on the basis of the food type specific nutrition density. Recently mobile devices with depth sensors have been made available for the public (Google's project tango platform). In this work, an app for mobile devices with a depth sensor is presented which assists users in portion estimation. Furthermore, we present the design of a user study for the app and preliminary results.
EatAR tango:带深度传感器的移动设备上的部分估计
准确评估营养信息是一项具有挑战性的任务,但对患有某些疾病(如糖尿病)的人来说至关重要。营养信息评估的一个重要部分是分量估计,即体积估计。给定体积和食物种类,就可以根据食物种类特定营养密度计算出营养信息。最近,带有深度传感器的移动设备已经向公众开放(谷歌的探戈项目平台)。在这项工作中,提出了一个带有深度传感器的移动设备应用程序,它可以帮助用户进行部分估计。此外,我们还介绍了该应用程序的用户研究设计和初步结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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