使用人形机器人辅助书写障碍儿童的书写治疗和习得

Soukaina Gouraguine, Mohammed Qbadou, K. Mansouri
{"title":"使用人形机器人辅助书写障碍儿童的书写治疗和习得","authors":"Soukaina Gouraguine, Mohammed Qbadou, K. Mansouri","doi":"10.1109/EDUCON52537.2022.9766701","DOIUrl":null,"url":null,"abstract":"Handwriting is one of the basic skills and an important means of communicating with others and expressing oneself, as it is important in all aspects of life. Acquiring handwriting is also a complex skill that takes years of training to be mastered. Advances in technology have allowed robots to accomplish a diversity of human activities, provoking interest from educators, researchers, and practitioners in discovering the potential advantages of employing robots as an intervention for children with dysgraphia who have difficulty automating their handwriting. Dysgraphia can be treated very well thanks to a writing rehabilitation adapted to the needs and the importance of the child’s problem. The purpose of this article is to explore the potential benefits of integrating a social humanoid robot in interventions for the treatment of dysgraphia in children. In this context, our research focuses on introducing a social humanoid robot assistant in an educative context to assist dysgraphic children to acquire handwriting. This work is done by applying a new approach based on a deep learning classification algorithm using convolutional neural networks (CNN) to determine the presence of dysgraphia from the handwriting of elementary school children. In this study, we realized a humanoid robot that will assist a teacher by equipping the NAO robot: (1) to moderate a session of learning to handwrite an alphabet character, (2) to detect whether a child is dysgraphic or not using a convolutional neural network, and (3) to assist and monitor dysgraphic children by performing tasks and suggesting rehabilitation sessions. The results indicate that it can distinguish dysgraphic children from non-dysgraphic children with an accuracy of 75%, a specificity of 75%, and a precision of 60%. The results reveal that the robot was able to classify learners so that the human tutor could assign the students to the appropriate rehabilitation program based on their specific needs.","PeriodicalId":416694,"journal":{"name":"2022 IEEE Global Engineering Education Conference (EDUCON)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Handwriting treatment and acquisition in dysgraphic children using a humanoid robot-assistant\",\"authors\":\"Soukaina Gouraguine, Mohammed Qbadou, K. Mansouri\",\"doi\":\"10.1109/EDUCON52537.2022.9766701\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Handwriting is one of the basic skills and an important means of communicating with others and expressing oneself, as it is important in all aspects of life. Acquiring handwriting is also a complex skill that takes years of training to be mastered. Advances in technology have allowed robots to accomplish a diversity of human activities, provoking interest from educators, researchers, and practitioners in discovering the potential advantages of employing robots as an intervention for children with dysgraphia who have difficulty automating their handwriting. Dysgraphia can be treated very well thanks to a writing rehabilitation adapted to the needs and the importance of the child’s problem. The purpose of this article is to explore the potential benefits of integrating a social humanoid robot in interventions for the treatment of dysgraphia in children. In this context, our research focuses on introducing a social humanoid robot assistant in an educative context to assist dysgraphic children to acquire handwriting. This work is done by applying a new approach based on a deep learning classification algorithm using convolutional neural networks (CNN) to determine the presence of dysgraphia from the handwriting of elementary school children. In this study, we realized a humanoid robot that will assist a teacher by equipping the NAO robot: (1) to moderate a session of learning to handwrite an alphabet character, (2) to detect whether a child is dysgraphic or not using a convolutional neural network, and (3) to assist and monitor dysgraphic children by performing tasks and suggesting rehabilitation sessions. The results indicate that it can distinguish dysgraphic children from non-dysgraphic children with an accuracy of 75%, a specificity of 75%, and a precision of 60%. The results reveal that the robot was able to classify learners so that the human tutor could assign the students to the appropriate rehabilitation program based on their specific needs.\",\"PeriodicalId\":416694,\"journal\":{\"name\":\"2022 IEEE Global Engineering Education Conference (EDUCON)\",\"volume\":\"25 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-03-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE Global Engineering Education Conference (EDUCON)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/EDUCON52537.2022.9766701\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE Global Engineering Education Conference (EDUCON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EDUCON52537.2022.9766701","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

书写是一项基本技能,也是与他人交流和表达自己的重要手段,因为它在生活的各个方面都很重要。掌握手写也是一项复杂的技能,需要多年的训练才能掌握。技术的进步使机器人能够完成各种各样的人类活动,这引起了教育工作者、研究人员和实践者的兴趣,他们正在探索利用机器人来干预有书写困难的儿童的潜在优势,这些儿童难以自动书写。书写困难症可以很好地治疗,这要归功于适应儿童问题的需要和重要性的写作康复。本文的目的是探讨整合社交类人机器人干预儿童书写障碍治疗的潜在益处。在此背景下,我们的研究重点是在教育背景下引入一种社交类人机器人助手,以帮助书写困难的儿童获得书写。这项工作是通过一种基于卷积神经网络(CNN)的深度学习分类算法的新方法来完成的,该方法可以从小学生的笔迹中确定是否存在书写障碍。在这项研究中,我们实现了一个人形机器人,它将通过装备NAO机器人来协助教师:(1)调节学习手写字母的过程,(2)使用卷积神经网络来检测儿童是否有读写困难,(3)通过执行任务和建议康复课程来协助和监测读写困难儿童。结果表明,该方法能够以75%的准确率、75%的特异性和60%的准确率区分有读写困难的儿童和无读写困难的儿童。结果表明,机器人能够对学习者进行分类,这样人类导师就可以根据学生的具体需求为他们分配适当的康复计划。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Handwriting treatment and acquisition in dysgraphic children using a humanoid robot-assistant
Handwriting is one of the basic skills and an important means of communicating with others and expressing oneself, as it is important in all aspects of life. Acquiring handwriting is also a complex skill that takes years of training to be mastered. Advances in technology have allowed robots to accomplish a diversity of human activities, provoking interest from educators, researchers, and practitioners in discovering the potential advantages of employing robots as an intervention for children with dysgraphia who have difficulty automating their handwriting. Dysgraphia can be treated very well thanks to a writing rehabilitation adapted to the needs and the importance of the child’s problem. The purpose of this article is to explore the potential benefits of integrating a social humanoid robot in interventions for the treatment of dysgraphia in children. In this context, our research focuses on introducing a social humanoid robot assistant in an educative context to assist dysgraphic children to acquire handwriting. This work is done by applying a new approach based on a deep learning classification algorithm using convolutional neural networks (CNN) to determine the presence of dysgraphia from the handwriting of elementary school children. In this study, we realized a humanoid robot that will assist a teacher by equipping the NAO robot: (1) to moderate a session of learning to handwrite an alphabet character, (2) to detect whether a child is dysgraphic or not using a convolutional neural network, and (3) to assist and monitor dysgraphic children by performing tasks and suggesting rehabilitation sessions. The results indicate that it can distinguish dysgraphic children from non-dysgraphic children with an accuracy of 75%, a specificity of 75%, and a precision of 60%. The results reveal that the robot was able to classify learners so that the human tutor could assign the students to the appropriate rehabilitation program based on their specific needs.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Virtual Laboratory Workshop in Physics Hands-on Physics Experiments for K-6 Teachers at CERN Analysis of dual and non-dual student learning outcomes and student dropout data A Proposed Machine Learning Based Approach to Support Students with Learning Difficulties in The Post-Pandemic Norm Fab Lab-based learning: an environment to promote Women and Leadership in Engineering Education
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1