智能手表交互的预测手指触控级模型

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Multimodal Technologies and Interaction Pub Date : 2018-07-02 DOI:10.3390/MTI2030038
Shiroq Al-Megren
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引用次数: 11

摘要

在使用交互式系统时,通常使用击键级别模型(KLM)来预测专家用户在没有错误的情况下完成任务所需的时间。KLM最初旨在预测传统设置中的交互,即鼠标和键盘的交互。然而,它已经被用于预测与智能手机、车载信息系统和自然用户界面的交互。KLM及其扩展的简单性,以及它们节省资源和时间的功能,推动了它们的采用。近年来,智能手表越来越受欢迎,由于小型触摸屏和涉及的手动交互,引入了新的设计挑战,这使得目前对KLM的扩展不适合建模智能手表。因此,有必要对这些接口和交互进行研究。本文报告了三项研究,以修改原始KLM及其扩展的智能手表交互。首先,进行了一项观察性研究,以表征智能手表的互动。其次,观察到的相互作用的单位时间是通过另一项研究得出的,其中测量了执行相关物理动作所需的时间。最后,进行了第三项研究,以验证该模型与Apple Watch和Samsung Gear S3的交互。结果表明,新模型能够准确预测智能手表用户的行为,误差百分比为12.07%;低于原KLM规定的可接受百分比~21%的值。
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A Predictive Fingerstroke-Level Model for Smartwatch Interaction
The keystroke-level model (KLM) is commonly used to predict the time it will take an expert user to accomplish a task without errors when using an interactive system. The KLM was initially intended to predict interactions in conventional set-ups, i.e., mouse and keyboard interactions. However, it has since been adapted to predict interactions with smartphones, in-vehicle information systems, and natural user interfaces. The simplicity of the KLM and its extensions, along with their resource- and time-saving capabilities, has driven their adoption. In recent years, the popularity of smartwatches has grown, introducing new design challenges due to the small touch screens and bimanual interactions involved, which make current extensions to the KLM unsuitable for modelling smartwatches. Therefore, it is necessary to study these interfaces and interactions. This paper reports on three studies performed to modify the original KLM and its extensions for smartwatch interaction. First, an observational study was conducted to characterise smartwatch interactions. Second, the unit times for the observed interactions were derived through another study, in which the times required to perform the relevant physical actions were measured. Finally, a third study was carried out to validate the model for interactions with the Apple Watch and Samsung Gear S3. The results show that the new model can accurately predict the performance of smartwatch users with a percentage error of 12.07%; a value that falls below the acceptable percentage dictated by the original KLM ~21%.
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来源期刊
Multimodal Technologies and Interaction
Multimodal Technologies and Interaction Computer Science-Computer Science Applications
CiteScore
4.90
自引率
8.00%
发文量
94
审稿时长
4 weeks
期刊最新文献
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