In order to improve input efficiency in the cross-device interaction environment, and reduce cognitive workload during the process of switching visual attention among different devices, the eye tracking based atten-tive user interface for cross-device was proposed. First, extracted the edges of devices' screens, and then com-bined the geometry characters and the color histograms of the screens to detect different devices. The pupil center cornea reflection algorithm was used to calculate the gaze fixations' coordinates with supporting head movement. Detected areas of interest based on the gaze dwell time and collaborative recognition schemes across devices. Furthermore, proposed the task management model for distributed attentive user interface, and utilized this model to control task allocating, pausing, continuing and evaluating. Besides, applied production rules to change user interfaces, and then defined related design guidelines. Finally, built a cross-device English reading prototype sys-tem, including the functions such as notification of the last reading positions and adaptive annotations of word explanation. The user study results showed that the participants' visual attention detection accuracy was 94％, and the participants' reading comprehension, reading efficiency and subjective satisfaction were also improved.%为了提高用户在跨设备交互环境下的输入操作效率,减小视觉注意力在不同设备间切换时产生的认知负荷,提出基于眼动跟踪的跨设备注意力感知界面技术.首先提取设备屏幕边缘,结合屏幕几何特征和颜色直方图进行不同设备的识别,基于支持头部运动的瞳孔-反光点向量模型计算注视点坐标数据,并根据驻留时间和多设备协同机制识别目标感兴趣区;然后建立分布式感知用户界面的任务管理模型,控制任务的分发、中断与继续以及评估,并利用产生式规则驱动用户界面的转换,提出界面设计的指导原则;最后设计与开发了一个面向跨设备阅读的感知界面原型系统,包括最后阅读位置提示、单词词义与例句自动注释等功能.用户测试结果表明,原型系统感知用户注意力在不同设备之间切换的准确率达到94％,并有效地提高了用户阅读理解水平、阅读效率以及主观满意度.
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