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首页> 外文会议>International Conference on Human-Computer Interaction;International Conference on Universal Access in Human-Computer Interaction >An Evaluation of Eye-Foot Input for Target Acquisitions
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An Evaluation of Eye-Foot Input for Target Acquisitions

机译：目标捕获中眼-足输入的评估

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摘要

The multimodal interaction technique that combines eye and foot input not only provides a great opportunity for the user with busy hands or hand disabilities to interaction with computer, but can also overcome the drawbacks when using dwell time to solve the "Midas Problem" of gaze input. However, the user's capability of eye-foot coordination was still unclear. At the same time, the human performance in the basic task of target acquisitions by eye-foot input was also uncertain, and especially a proper performance model was lacking. Motivated by this situation, an eye pointing and foot tapping task experiment had been carried out to fill these gaps. A low-cost eye tracker and a USB foot pedal switcher were used as the input devices from different modalities. The experimental results indicated that the user was soon able to coordinate her/his foot with the eyes for target acquisitions, and that the user could respond fast to tap the foot pedal to finish a task trial in the level of 600 ms. The main performance measures of eye movement time (EMT) and eye pointing time (EPT) under the eye-foot multi-modal input condition were significantly increased with the increase of saccadic amplitude A and/or the decrease of target width (size) W, and vice versa. Regression analysis shown that the ID_(eye) model was more suitable than the standard Fitts' law to model the human performance in this multimodal interaction context.
机译：结合眼睛和脚输入的多模式交互技术不仅为双手繁忙或手部残疾的用户提供了与计算机交互的绝佳机会，而且还可以克服使用停留时间解决注视输入的“Midas问题”的缺点。然而，用户的眼-脚协调能力尚不清楚。同时，通过眼-足输入获取目标的基本任务中的人的绩效也不确定，尤其是缺乏合适的绩效模型。在这种情况下，一个眼睛指向和脚敲击的任务实验被用来填补这些空白。使用低成本的眼动跟踪器和USB脚踏开关作为不同模式的输入设备。实验结果表明，用户很快就可以将脚与眼睛协调起来进行目标捕获，在600毫秒的水平下，用户可以快速响应踩下踏板完成任务试验。眼动时间（EMT）和眼球指向时间（EPT）在眼-足多模态输入条件下的主要性能指标随着眼跳幅度a的增加和/或目标宽度（大小）W的减小而显著增加，反之亦然。回归分析表明，在这种多模态交互环境中，ID_uz（eye）模型比标准的Fitts定律更适合模拟人的绩效。

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