Haijun Xia, Ricardo Jota, Benjamin McCanny, Zhe Yu, C. Forlines, Karan Singh, Daniel J. Wigdor
{"title":"Zero-latency tapping: using hover information to predict touch locations and eliminate touchdown latency","authors":"Haijun Xia, Ricardo Jota, Benjamin McCanny, Zhe Yu, C. Forlines, Karan Singh, Daniel J. Wigdor","doi":"10.1145/2642918.2647348","DOIUrl":null,"url":null,"abstract":"A method of reducing the perceived latency of touch input by employing a model to predict touch events before the finger reaches the touch surface is proposed. A corpus of 3D finger movement data was collected, and used to develop a model capable of three granularities at different phases of movement: initial direction, final touch location, time of touchdown. The model is validated for target distances >= 25.5cm, and demonstrated to have a mean accuracy of 1.05cm 128ms before the user touches the screen. Preference study of different levels of latency reveals a strong preference for unperceived latency touchdown feedback. A form of 'soft' feedback, as well as other uses for this prediction to improve performance, is proposed.","PeriodicalId":20543,"journal":{"name":"Proceedings of the 27th annual ACM symposium on User interface software and technology","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2014-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"35","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 27th annual ACM symposium on User interface software and technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2642918.2647348","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 35
Abstract
A method of reducing the perceived latency of touch input by employing a model to predict touch events before the finger reaches the touch surface is proposed. A corpus of 3D finger movement data was collected, and used to develop a model capable of three granularities at different phases of movement: initial direction, final touch location, time of touchdown. The model is validated for target distances >= 25.5cm, and demonstrated to have a mean accuracy of 1.05cm 128ms before the user touches the screen. Preference study of different levels of latency reveals a strong preference for unperceived latency touchdown feedback. A form of 'soft' feedback, as well as other uses for this prediction to improve performance, is proposed.