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使用NAO机器人和人类动作识别来提高自闭症谱系障碍儿童的模仿技能。

Improving Imitation Skills in Children with Autism Spectrum Disorder Using the NAO Robot and a Human Action Recognition.

作者信息

Alnafjan Abeer, Alghamdi Maha, Alhakbani Noura, Al-Ohali Yousef

机构信息

Computer Science Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia.

Information Technology Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.

出版信息

Diagnostics (Basel). 2024 Dec 29;15(1):60. doi: 10.3390/diagnostics15010060.

Abstract

Autism spectrum disorder (ASD) is a group of developmental disorders characterized by poor social skills, low motivation in activities, and a lack of interaction with others. Traditional intervention approaches typically require support under the direct supervision of well-trained professionals. However, teaching and training programs for children with ASD can also be enhanced by assistive technologies, artificial intelligence, and robotics. In this study, we examined whether robotics can improve the imitation skills of children with autism and support therapists during therapeutic sessions. We designed scenarios for training hand clapping imitation skills using the NAO robot and analyzed the interaction between children with autism and the robot. We developed a deep learning approach based on the human action recognition algorithm for analyzing clapping imitation. Our findings suggest that integrating robotics into therapeutic practices can effectively enhance the imitation skills of children with ASD, offering valuable support to therapists.

摘要

自闭症谱系障碍(ASD)是一组发育障碍,其特征是社交技能差、活动积极性低以及缺乏与他人的互动。传统的干预方法通常需要在训练有素的专业人员的直接监督下提供支持。然而,针对自闭症谱系障碍儿童的教学和培训项目也可以通过辅助技术、人工智能和机器人技术得到加强。在本研究中,我们研究了机器人技术是否可以提高自闭症儿童的模仿技能,并在治疗过程中为治疗师提供支持。我们设计了使用NAO机器人训练鼓掌模仿技能的场景,并分析了自闭症儿童与机器人之间的互动。我们基于人体动作识别算法开发了一种深度学习方法来分析鼓掌模仿。我们的研究结果表明,将机器人技术融入治疗实践可以有效地提高自闭症谱系障碍儿童的模仿技能,为治疗师提供有价值的支持。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7023/11719826/b35f53ce67e4/diagnostics-15-00060-g001.jpg

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