R. Dinic, Michael Domhardt, Simon W. Ginzinger, Thomas Stütz
{"title":"EatAR tango:带深度传感器的移动设备上的部分估计","authors":"R. Dinic, Michael Domhardt, Simon W. Ginzinger, Thomas Stütz","doi":"10.1145/3098279.3125434","DOIUrl":null,"url":null,"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.","PeriodicalId":120153,"journal":{"name":"Proceedings of the 19th International Conference on Human-Computer Interaction with Mobile Devices and Services","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2017-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"EatAR tango: portion estimation on mobile devices with a depth sensor\",\"authors\":\"R. Dinic, Michael Domhardt, Simon W. Ginzinger, Thomas Stütz\",\"doi\":\"10.1145/3098279.3125434\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":120153,\"journal\":{\"name\":\"Proceedings of the 19th International Conference on Human-Computer Interaction with Mobile Devices and Services\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-09-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 19th International Conference on Human-Computer Interaction with Mobile Devices and Services\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3098279.3125434\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 19th International Conference on Human-Computer Interaction with Mobile Devices and Services","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3098279.3125434","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
EatAR tango: portion estimation on mobile devices with a depth sensor
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.