{"title":"收集android设备上的文本输入参数","authors":"Steven J. Castellucci, I. Mackenzie","doi":"10.1145/1979742.1979799","DOIUrl":null,"url":null,"abstract":"We developed an application to gather text entry speed and accuracy metrics on Android devices. This paper details the features of the application and describes a pilot study to demonstrate its utility. We evaluated and compared three mobile text entry methods: QWERTY typing, handwriting recognition, and shape writing recognition. Handwriting was the slowest and least accurate technique. QWERTY was faster than shape writing, but we found no significant difference in accuracy between the two techniques.","PeriodicalId":275462,"journal":{"name":"CHI '11 Extended Abstracts on Human Factors in Computing Systems","volume":"91 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"61","resultStr":"{\"title\":\"Gathering text entry metrics on android devices\",\"authors\":\"Steven J. Castellucci, I. Mackenzie\",\"doi\":\"10.1145/1979742.1979799\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We developed an application to gather text entry speed and accuracy metrics on Android devices. This paper details the features of the application and describes a pilot study to demonstrate its utility. We evaluated and compared three mobile text entry methods: QWERTY typing, handwriting recognition, and shape writing recognition. Handwriting was the slowest and least accurate technique. QWERTY was faster than shape writing, but we found no significant difference in accuracy between the two techniques.\",\"PeriodicalId\":275462,\"journal\":{\"name\":\"CHI '11 Extended Abstracts on Human Factors in Computing Systems\",\"volume\":\"91 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-05-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"61\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"CHI '11 Extended Abstracts on Human Factors in Computing Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/1979742.1979799\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"CHI '11 Extended Abstracts on Human Factors in Computing Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1979742.1979799","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
We developed an application to gather text entry speed and accuracy metrics on Android devices. This paper details the features of the application and describes a pilot study to demonstrate its utility. We evaluated and compared three mobile text entry methods: QWERTY typing, handwriting recognition, and shape writing recognition. Handwriting was the slowest and least accurate technique. QWERTY was faster than shape writing, but we found no significant difference in accuracy between the two techniques.
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