Smart Tutoring System for Arabic Sign Language Using Leap Motion Controller

Heba A. Fasihuddin, S. Alsolami, Seham Alzahrani, Rawan Alasiri, Afnan Sahloli
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引用次数: 6

Abstract

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.
基于Leap运动控制器的阿拉伯手语智能辅导系统
本文提出了一种智能阿拉伯手语教学系统。手语是听力障碍人士交流的主要方式之一。许多人愿意学习手语并支持这一社会群体;然而,学习这门语言需要一些努力和帮助。用于支持手语学习者,特别是ArSL的工具是有限和不足的。因此,有必要开发一种能够培训和评估ArSL学习者的工具。提出了一种基于跳跃运动手部跟踪技术的ArSL智能辅导方案。该系统的目的是帮助想要学习手语的非残疾学习者,如听力障碍专业的本科生,听力障碍儿童的父母或任何感兴趣的科目。该系统允许学习者进行不同层次的ArSL练习和自我评估。由于采用了最新的跳跃运动控制器技术,它可以检测和跟踪手和手指的运动,从而评估位置和运动的准确性。机器学习技术,特别是K近邻算法应用于分类和符号识别。开发了初步原型,并根据用户的接受程度进行了测试。结果显示出令人满意和有希望的结果。预计拟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一个重要部分。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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