基于树莓派的懒苏珊卡路里监测餐桌

Tonny Kurniawan, Hervinsen Zhang, Rico Wijaya, J. Linggarjati, Savita Sondhi, R. Hedwig
{"title":"基于树莓派的懒苏珊卡路里监测餐桌","authors":"Tonny Kurniawan, Hervinsen Zhang, Rico Wijaya, J. Linggarjati, Savita Sondhi, R. Hedwig","doi":"10.1109/citisia53721.2021.9719894","DOIUrl":null,"url":null,"abstract":"Living healthy is a current lifestyle especially in big cities. People are concerned about the number of calories they consume per day, and they even hire nutritionists and personal trainers to help them in monitoring their healthy life. In this research, the researcher is using a calorie monitoring dining table with a revolving stand or tray on a table to hold condiments. This device, which is commonly called as Lazy Susan, is modified by adding Raspberry Pi and load cell sensor to calculate the number of calories transferred from serving plate to user’s plate. Users can select the menu and the Lazy Susan will rotate to serve the desired menu while also calculating the calories consumed by the user. The information will be transferred to the user’s Android smartphone and will be adjusted according to the user’s activity. The error reading rate of the system is 6% to 7% and the total price of the product is $1,308, which is cheaper than the \"made to measureautomated Lazy Susan dining table\" available in the market.","PeriodicalId":252063,"journal":{"name":"2021 6th International Conference on Innovative Technology in Intelligent System and Industrial Applications (CITISIA)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Lazy Susan Calorie Monitoring Dining Table Based on Raspberry Pi\",\"authors\":\"Tonny Kurniawan, Hervinsen Zhang, Rico Wijaya, J. Linggarjati, Savita Sondhi, R. Hedwig\",\"doi\":\"10.1109/citisia53721.2021.9719894\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Living healthy is a current lifestyle especially in big cities. People are concerned about the number of calories they consume per day, and they even hire nutritionists and personal trainers to help them in monitoring their healthy life. In this research, the researcher is using a calorie monitoring dining table with a revolving stand or tray on a table to hold condiments. This device, which is commonly called as Lazy Susan, is modified by adding Raspberry Pi and load cell sensor to calculate the number of calories transferred from serving plate to user’s plate. Users can select the menu and the Lazy Susan will rotate to serve the desired menu while also calculating the calories consumed by the user. The information will be transferred to the user’s Android smartphone and will be adjusted according to the user’s activity. The error reading rate of the system is 6% to 7% and the total price of the product is $1,308, which is cheaper than the \\\"made to measureautomated Lazy Susan dining table\\\" available in the market.\",\"PeriodicalId\":252063,\"journal\":{\"name\":\"2021 6th International Conference on Innovative Technology in Intelligent System and Industrial Applications (CITISIA)\",\"volume\":\"14 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-11-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 6th International Conference on Innovative Technology in Intelligent System and Industrial Applications (CITISIA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/citisia53721.2021.9719894\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 6th International Conference on Innovative Technology in Intelligent System and Industrial Applications (CITISIA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/citisia53721.2021.9719894","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

健康生活是当前的一种生活方式,尤其是在大城市。人们关心自己每天摄入的卡路里数,他们甚至聘请营养学家和私人教练来帮助他们监控自己的健康生活。在这项研究中,研究人员使用了一个卡路里监测餐桌,桌子上有一个旋转的支架或托盘来放置调味品。这个设备通常被称为“懒苏珊”,通过添加树莓派和称重传感器来计算从餐盘转移到用户餐盘的卡路里数量。用户可以选择菜单,转盘会旋转,提供所需的菜单,同时还会计算用户消耗的卡路里。这些信息将被传输到用户的安卓智能手机上,并根据用户的活动进行调整。该系统的误读率为6% ~ 7%,产品总价为1308美元,比市场上的“定制自动化懒苏珊餐桌”便宜。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Lazy Susan Calorie Monitoring Dining Table Based on Raspberry Pi
Living healthy is a current lifestyle especially in big cities. People are concerned about the number of calories they consume per day, and they even hire nutritionists and personal trainers to help them in monitoring their healthy life. In this research, the researcher is using a calorie monitoring dining table with a revolving stand or tray on a table to hold condiments. This device, which is commonly called as Lazy Susan, is modified by adding Raspberry Pi and load cell sensor to calculate the number of calories transferred from serving plate to user’s plate. Users can select the menu and the Lazy Susan will rotate to serve the desired menu while also calculating the calories consumed by the user. The information will be transferred to the user’s Android smartphone and will be adjusted according to the user’s activity. The error reading rate of the system is 6% to 7% and the total price of the product is $1,308, which is cheaper than the "made to measureautomated Lazy Susan dining table" available in the market.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
A Heuristic Approach using Block Chain to Fight Novel COVID-19 During an Election Customer data extraction techniques based on natural language processing for e-commerce business analytics Identifying Parkinson’s Disease using Multimodal Approach and Deep Learning DCV: A Taxonomy on Deep Learning Based Lung Cancer Classification Review of network-forensic analysis optimization using deep learning against attacks on IoT devices
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1