Yi Zhen Quak, Yi Xin Loke, Zhi Yuan Chan, Sze Qi Chew, P. Ooi
{"title":"IoT-Based Tracing and Communication Platform for Disease Control","authors":"Yi Zhen Quak, Yi Xin Loke, Zhi Yuan Chan, Sze Qi Chew, P. Ooi","doi":"10.1109/IICAIET51634.2021.9574005","DOIUrl":null,"url":null,"abstract":"Coronavirus disease 2019 (COVID-19) is highly contagious and has swept the globe. Countries worldwide is in urgent need of efficient technological solutions to control the transmission of COVID-19 disease. The objective of this project is to develop an artificial intelligence-driven contact tracing platform and communication to come up with an integrated solution to block the transmission chain of the disease. Three elements are included in this platform, which are behavioral recognition system, mobile application and smart wristband. Mobile application developed through Android Studio SDK, has multiple functions, which are Quick Response (QR) code scanner for location tracking, close contact identification, COVID-19 cases update, district color alert system and exposure notification. Behavioral recognition system developed on Raspberry Pi v4 and Faster Region Based Convolutional Neural Network Version 2 (RCNN_v2) and Single Shot Multibox Detection MobileNet Version 2 (SSD MobileNet_v2) are adopted as machine learning algorithm can carry out close-proximity detection, people counting, and face mask detection. Smart wristband built with Arduino MKR GSM1400 microcontroller and various sensors are developed through Arduino Integrated Development Environment (IDE) to keep track on the location and vital signs of the quarantined people and is designed with an emergency button to allow the quarantined people to get help immediately if they are not feeling well. The data obtained from the three elements is uploaded to a centralized database, Firestore associating with accurate timestamp and location. This system integrated with various preventive measure and control measure can mitigate and manage COVID-19 pandemic effectively and efficiently.","PeriodicalId":234229,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IICAIET51634.2021.9574005","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Coronavirus disease 2019 (COVID-19) is highly contagious and has swept the globe. Countries worldwide is in urgent need of efficient technological solutions to control the transmission of COVID-19 disease. The objective of this project is to develop an artificial intelligence-driven contact tracing platform and communication to come up with an integrated solution to block the transmission chain of the disease. Three elements are included in this platform, which are behavioral recognition system, mobile application and smart wristband. Mobile application developed through Android Studio SDK, has multiple functions, which are Quick Response (QR) code scanner for location tracking, close contact identification, COVID-19 cases update, district color alert system and exposure notification. Behavioral recognition system developed on Raspberry Pi v4 and Faster Region Based Convolutional Neural Network Version 2 (RCNN_v2) and Single Shot Multibox Detection MobileNet Version 2 (SSD MobileNet_v2) are adopted as machine learning algorithm can carry out close-proximity detection, people counting, and face mask detection. Smart wristband built with Arduino MKR GSM1400 microcontroller and various sensors are developed through Arduino Integrated Development Environment (IDE) to keep track on the location and vital signs of the quarantined people and is designed with an emergency button to allow the quarantined people to get help immediately if they are not feeling well. The data obtained from the three elements is uploaded to a centralized database, Firestore associating with accurate timestamp and location. This system integrated with various preventive measure and control measure can mitigate and manage COVID-19 pandemic effectively and efficiently.
2019冠状病毒病(COVID-19)具有高度传染性,已席卷全球。世界各国迫切需要有效的技术解决方案来控制COVID-19疾病的传播。该项目的目标是开发人工智能驱动的接触者追踪平台和通信,以提出阻断疾病传播链的综合解决方案。该平台包括行为识别系统、移动应用和智能手环三部分。通过Android Studio SDK开发的手机应用程序,具有位置跟踪快速响应(QR)扫描、密切接触者识别、COVID-19病例更新、区域颜色警报系统和暴露通知等多种功能。采用基于Raspberry Pi v4和Faster Region Based Convolutional Neural Network Version 2 (RCNN_v2)和Single Shot Multibox Detection MobileNet Version 2 (SSD MobileNet_v2)开发的行为识别系统作为机器学习算法,可以进行近距离检测、人计数、人脸检测。智能腕带采用Arduino MKR GSM1400微控制器和各种传感器,通过Arduino集成开发环境(IDE)开发,跟踪被隔离者的位置和生命体征,并设计了紧急按钮,让被隔离者在感觉不适时立即获得帮助。从这三个元素获得的数据被上传到集中式数据库,Firestore与准确的时间戳和位置相关联。该系统与各种防控措施相结合,能够有效、高效地缓解和管理COVID-19大流行。