Zhang Dan, Seiple William H, Li Zhi, Ramakrishnan I V, Ashok Vikas, Bi Xiaojun
Department of Computer Science, Stony Brook University New York, USA.
Lighthouse Guild New York, USA.
Proc ACM Symp User Interface Softw Tech. 2024;2024. doi: 10.1145/3654777.3676447. Epub 2024 Oct 11.
While gesture typing is widely adopted on touchscreen keyboards, its support for low vision users is limited. We have designed and implemented two keyboard prototypes, layout-magnified and key-magnified keyboards, to enable gesture typing for people with low vision. Both keyboards facilitate uninterrupted access to all keys while the screen magnifier is active, allowing people with low vision to input text with one continuous stroke. Furthermore, we have created a kinematics-based decoding algorithm to accommodate the typing behavior of people with low vision. This algorithm can decode the gesture input even if the gesture trace deviates from a pre-defined word template, and the starting position of the gesture is far from the starting letter of the target word. Our user study showed that the key-magnified keyboard achieved 5.28 words per minute, 27.5% faster than a conventional gesture typing keyboard with voice feedback.
虽然手势输入在触摸屏键盘上被广泛采用，但其对视力低下用户的支持有限。我们设计并实现了两种键盘原型，即布局放大键盘和按键放大键盘，以使视力低下的人能够进行手势输入。在屏幕放大镜处于活动状态时，这两种键盘都便于用户不间断地访问所有按键，从而使视力低下的人能够通过一次连续笔划来输入文本。此外，我们创建了一种基于运动学的解码算法，以适应视力低下者的打字行为。即使手势轨迹偏离预定义的单词模板，并且手势的起始位置与目标单词的起始字母相距甚远，该算法也能对手势输入进行解码。我们的用户研究表明，按键放大键盘的输入速度达到每分钟5.28个单词，比具有语音反馈的传统手势输入键盘快27.5%。
