Graduate Program in Health Technology, Pontifícia Universidade Católica do Paraná, Curitiba, Brazil; Graduate Program in Electrical and Computer Engineering, Universidade Tecnológica Federal do Paraná, Curitiba, Brazil.
Graduate Program in Health Technology, Pontifícia Universidade Católica do Paraná, Curitiba, Brazil.
Comput Methods Programs Biomed. 2020 Aug;192:105402. doi: 10.1016/j.cmpb.2020.105402. Epub 2020 Mar 3.
One in every 200 people worldwide cannot express orally because of cognitive, motor, neurological, or emotional problems. Assistive technologies can help people with impairments to use computers to perform their daily life activities independently and to communicate with others. This paper presents a Hidden Markov Model-based word prediction method that allows keyboard emulation software to predict words so that children with disabilities can type texts more quickly. The proposed system involved the development of a keyboard emulator, the construction and processing of a corpus, as well as a word prediction algorithm. Children with different cognitive profiles had to produce a text and type it twice: first with free typing, second using the virtual keyboard's word prediction. Results indicated the word prediction of the keyboard emulator software reduced typing efforts. However, the software initially increased the typing time when the corpus was not well adapted to users. The total amount of clicks with word prediction decreased by around 26.2%. Regarding execution time using prediction, 61% typed the text in less time. The tests performed with literate volunteers indicated a reduction in the number of clicks by up to 51.3%. This result surpasses the 15% achieved in the previous study by Free Virtual Keyboard with word prediction based on pure statistics. Moreover, all volunteers required fewer clicks to perform the task. People with impairments, especially children, could use the system and demonstrate their knowledge and abilities. The entire system is available on the Internet and users have unrestricted and free access to it.
全世界每 200 人中就有 1 人因认知、运动、神经或情绪问题无法进行口头表达。辅助技术可以帮助有障碍的人使用计算机独立完成日常生活活动并与他人交流。本文提出了一种基于隐马尔可夫模型的单词预测方法，允许键盘仿真软件预测单词，以便残疾儿童能够更快地输入文本。该系统涉及键盘仿真器的开发、语料库的构建和处理以及单词预测算法。具有不同认知特征的儿童必须生成一个文本并进行两次输入：首先是自由输入，其次是使用虚拟键盘的单词预测。结果表明，键盘仿真器软件的单词预测减少了输入工作量。然而，当语料库与用户不太适配时，软件最初会增加输入时间。使用预测时的总点击量减少了约 26.2%。关于使用预测的执行时间，61%的人在更短的时间内输入了文本。对有文化的志愿者进行的测试表明，点击次数最多减少了 51.3%。这一结果超过了基于纯统计的 Free Virtual Keyboard 之前的研究中预测达到的 15%。此外，所有志愿者都需要更少的点击次数来完成任务。有障碍的人，尤其是儿童，可以使用该系统并展示他们的知识和能力。整个系统都可以在互联网上使用，用户可以无限制地免费访问。
