{"title":"mSTROKE:一种面向中风识别的群众性移动性","authors":"Richa Tibrewal, Ankita Singh, M. Bhattacharyya","doi":"10.1145/2957265.2961831","DOIUrl":null,"url":null,"abstract":"We demonstrate a crowd-powered model for the early diagnosis of stroke using a mobile device. The simple approach consists of monitoring the subject's health in three simple steps including the smile test for facial weakness, raising hands test for arm weakness and speech test for slurring of speech. Our demonstrated system shows a performance accuracy of 87.5% over a total number of 40 test cases.","PeriodicalId":131157,"journal":{"name":"Proceedings of the 18th International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct","volume":"66 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"mSTROKE: a crowd-powered mobility towards stroke recognition\",\"authors\":\"Richa Tibrewal, Ankita Singh, M. Bhattacharyya\",\"doi\":\"10.1145/2957265.2961831\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We demonstrate a crowd-powered model for the early diagnosis of stroke using a mobile device. The simple approach consists of monitoring the subject's health in three simple steps including the smile test for facial weakness, raising hands test for arm weakness and speech test for slurring of speech. Our demonstrated system shows a performance accuracy of 87.5% over a total number of 40 test cases.\",\"PeriodicalId\":131157,\"journal\":{\"name\":\"Proceedings of the 18th International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct\",\"volume\":\"66 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-09-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 18th International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2957265.2961831\",\"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 18th International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2957265.2961831","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
mSTROKE: a crowd-powered mobility towards stroke recognition
We demonstrate a crowd-powered model for the early diagnosis of stroke using a mobile device. The simple approach consists of monitoring the subject's health in three simple steps including the smile test for facial weakness, raising hands test for arm weakness and speech test for slurring of speech. Our demonstrated system shows a performance accuracy of 87.5% over a total number of 40 test cases.