{"title":"评估MS:使用Kinect支持多发性硬化症的临床评估","authors":"C. Morrison, R. Corish, A. Sellen","doi":"10.4108/ICST.PERVASIVEHEALTH.2014.255429","DOIUrl":null,"url":null,"abstract":"We present an early prototype of the ASSESS MS system, developed to help health professionals more accurately monitor the progression of Multiple Sclerosis (MS). Specifically, the system aims to detect and quantify changes in motor dysfunction more objectively and accurately than human assessors. We focus on key interaction design decisions needed to deploy a machine-learning based system in a real clinical context.","PeriodicalId":120856,"journal":{"name":"Proceedings of the 8th International Conference on Pervasive Computing Technologies for Healthcare","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"ASSESS MS: supporting the clinical assessment of Multiple Sclerosis using Kinect\",\"authors\":\"C. Morrison, R. Corish, A. Sellen\",\"doi\":\"10.4108/ICST.PERVASIVEHEALTH.2014.255429\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present an early prototype of the ASSESS MS system, developed to help health professionals more accurately monitor the progression of Multiple Sclerosis (MS). Specifically, the system aims to detect and quantify changes in motor dysfunction more objectively and accurately than human assessors. We focus on key interaction design decisions needed to deploy a machine-learning based system in a real clinical context.\",\"PeriodicalId\":120856,\"journal\":{\"name\":\"Proceedings of the 8th International Conference on Pervasive Computing Technologies for Healthcare\",\"volume\":\"25 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-05-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 8th International Conference on Pervasive Computing Technologies for Healthcare\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4108/ICST.PERVASIVEHEALTH.2014.255429\",\"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 8th International Conference on Pervasive Computing Technologies for Healthcare","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4108/ICST.PERVASIVEHEALTH.2014.255429","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
ASSESS MS: supporting the clinical assessment of Multiple Sclerosis using Kinect
We present an early prototype of the ASSESS MS system, developed to help health professionals more accurately monitor the progression of Multiple Sclerosis (MS). Specifically, the system aims to detect and quantify changes in motor dysfunction more objectively and accurately than human assessors. We focus on key interaction design decisions needed to deploy a machine-learning based system in a real clinical context.