Robotics technology has been increasingly used as an educational and intervention tool for children with autism spectrum disorder (ASD). However, there remain research issues and challenges that need to be addressed to fully realize the potential benefits of robot-assisted therapy. This systematic review categorizes and summarizes the literature related to robot educational/training interventions and provides a conceptual framework for collecting and classifying these articles. The challenges identified in this review are classified into four levels: robot-level, algorithm-level, experimental-research-level, and application-level challenges. The review highlights possible future research directions and offers crucial insights for researchers interested in using robots in therapy. The most relevant findings suggest that robot-assisted therapy has the potential to improve social interaction, communication, and emotional regulation skills in children with ASD. Addressing these challenges and seeking new research avenues will be critical to advancing the field of robot-assisted therapy and improving outcomes for children with ASD. This study serves as a roadmap for future research in this important area.

Background/Objectives: 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. Methods: 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. Results: We developed a deep learning approach based on the human action recognition algorithm for analyzing clapping imitation. Conclusions: Our findings suggest that integrating robotics into therapeutic practices can effectively enhance the imitation skills of children with ASD, offering valuable support to therapists.

Human action recognition (HAR) is a rapidly growing field with numerous applications in various domains. HAR involves the development of algorithms and techniques to automatically identify and classify human actions from video data. Accurate recognition of human actions has significant implications in fields such as surveillance and sports analysis and in the health care domain. This paper presents a study on the design and development of an imitation detection system using an HAR algorithm based on deep learning. This study explores the use of deep learning models, such as a single-frame convolutional neural network (CNN) and pretrained VGG-16, for the accurate classification of human actions. The proposed models were evaluated using a benchmark dataset, KTH. The performance of these models was compared with that of classical classifiers, including K-Nearest Neighbors, Support Vector Machine, and Random Forest. The results showed that the VGG-16 model achieved higher accuracy than the single-frame CNN, with a 98% accuracy rate.