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Smart Tutoring System for Arabic Sign Language Using Leap Motion Controller

机译:使用Leap Motion Controller的阿拉伯语手语智能辅导系统

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This paper presents a smart tutoring system for Arabic Sign Language (ArSL). Sign language is one of the main approaches of communication for people with hearing impairment. Many people are willing to learn sign language and support this segment of the society; however, learning this language requires some effort and assistant. Tools that are used to support sign language learners and specifically ArSL are limited and insufficient. Hence, the development of a tool that is capable of training and assessing ArSL learners becomes a necessity. We proposed a smart tutoring for ArSL based on using the leap motion's hand tracking technology. The aim of this system is assisting non-disabled learners who want to learn the sign language, such as undergraduates specializing in hearing disabilities, parents of kids with hearing impairment or any interested subject. The system allows learners to practice ArSL in different levels and self-assess themselves. As it utilizes the recent technology of leap motion controller, it can detect and track hand and fingers movements and consequently assess the position and movement accuracy. Machine learning techniques, specifically the K- Nearest Neighbor algorithm was applied for classification and sign recognition. Preliminary prototype was developed and tested in terms of users' acceptance. The outcomes show satisfactory and promising results. It is expected that the proposed system will contribute in enriching the learning process of ArSL and consequently support an important segment of our community.
机译:本文介绍了阿拉伯语手语(ARSL)的智能辅导系统。手语是听力障碍人士的主要沟通方法之一。许多人愿意学习手语并支持社会的这一部门;但是,学习这种语言需要一些努力和助手。用于支持手语学习者和特定ARSL的工具有限且不足够。因此,能够培训和评估ARSL学习者的工具的开发成为必需品。我们提出了一个智能辅导为ArSL基于使用的飞跃运动的手跟踪技术。该系统的目的是协助希望学习手语的非残疾学习者,例如专门从事听证残疾的大学生,以及听证障碍或任何感兴趣的主题的孩子们的父母。该系统允许学习者在不同层面练习ARSL并自我评估。由于它利用近期的Leap Motion Controller技术,它可以检测和跟踪手部和手指移动,从而评估位置和移动精度。机器学习技术,特别是k-最近邻算法应用于分类和标志识别。在用户的接受方面开发和测试了初步原型。结果表明了令人满意和有前途的结果。预计建议的系统将有助于丰富ARSL的学习过程,从而支持我们社区的重要部分。

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