Ayşe Tuna

The potential of humanoid robots to facilitate symbolic play has been explored in intervention research involving children with autism spectrum disorder. Accordingly, this study employed a multiple-baseline design across participants to examine the effects of a humanoid robot-assisted least-to-most prompting procedure on the symbolic play skills of three children diagnosed with autism spectrum disorder. The findings demonstrated improvements in the children's symbolic play performance following the intervention. Furthermore, the children maintained the acquired target skills at 100% accuracy over time and generalised them across people and settings. Additionally, the participants' mothers and teachers reported positive perceptions of the intervention.

Research indicates therapeutic benefits of robots for children with autism; however, the potential of similar agents, such as puppets, needs to be investigated. Because puppets have been proven to capture and hold the attention of children with autism spectrum disorder, indicating the potential for alternative therapies that improve social engagement and aid learning. According to research, when a puppet speaks, children with autism spectrum disorder's attention patterns are similar to those of typically developing children and both groups exhibit a pronounced preference for the talking puppet over the listening person. In this study, we highlight the attentional and emotional benefits of puppets, which ideally can be used. In particular, in our study, Karagöz-Hacivat performance has been organised to help with therapeutic efforts. Our findings indicate that shadow play characters could be an effective tool for assisting children with autism spectrum disorder in improving their social engagement, which is quite exciting.

One of the most visible symptoms of autism spectrum disorder is difficulty in speech and language. Difficulties in speech and language are generally very different for each child with autism spectrum disorder. Although some children with autism spectrum disorder can speak fluently, others will not be able to speak normally or will be even nonverbal. In all cases, parents try to communicate with, and understand their children’s needs, desires, and emotions. If a child with autism spectrum disorder cannot speak out loud, it is harder to communicate with him/her but there are other non-vocal methods for communication. In this paper, the benefits of teaching American sign language to children with autism spectrum disorder, the difficulties that families and children will experience while doing this, and technological solutions to these difficulties are presented. In parallel with advancements in technology, novel solutions to understand and use sign language have been proposed and these solutions are supposed to help parents who cannot understand sign language. Such solutions typically rely on image processing methods and classification algorithms to recognise sign language. Therefore, in this paper, the performance of various classification algorithms used to classify American Sign Language phonemes is compared. As the results show, when combined with image processing methods, classification algorithms can be used in various technological solutions aiming at helping to identify sign language phonemes.

The main goal of applied behaviour analysis is to increase behaviours that are positive and helpful and decrease behaviours that affect learning negatively or are harmful. Behaviour analysis has been studied and used for decades and it has been shown that its methods can help many kinds of learners gain different skills. Therapists have been using applied behaviour analysis to help children with autism spectrum disorder and other related developmental disorders for a long time. Applied behaviour analysis can be adapted to meet the needs of each child and also be provided in different environments like school, home, and community. It can involve one-to-one teaching or group instruction and is effective at teaching skills useful in everyday life. In this paper, firstly approaches and strategies used to implement applied behaviour analysis on children with autism spectrum disorder are presented. Then, challenges related to these and emerging opportunities and software applications serving for applied behaviour analysis are reviewed. Finally, future research directions are presented.

Recognising emotions, facial expressions and tone of voice and body language, expressing and managing their own emotions, and understanding and responding to other people’s emotions are often difficult for children with autism spectrum disorder. Since the emotional codes of individuals with autism spectrum disorder are different, those people will possibly be awkward in expressing some throughout their lives. Although it might seem that children with autism spectrum disorder do not respond emotionally, the ability to understand their facial expressions could lead to an improvement in their social interaction difficulties. In addition, since autistic expressions might be unique to each child, recognising their emotions is important when delivering a personalised intervention to a child with autism spectrum disorder. In recent decades, researchers have become increasingly interested in the role of emotions in learning and teaching a foreign language beyond heavily investigated topics such as foreign language anxiety and motivation and attitudes towards the foreign language. In this paper, how emotions impact the motivation and success of children with autism spectrum disorder while they are learning a foreign language is presented. Challenges, opportunities and future research directions in this domain are given.

Usage of complex words causes significant problems not only in reading but in writing as well and eventually leads to poor academic achievement of students, poorer particularly for hearing impaired children. The dual diagnosis of Autism Spectrum Disorder (ASD) and hearing impairment pose additional challenges mainly due to the difficulties that come with making accurate decisions. Hence, parents must be provided with the information about the signs and symptoms of ASD and deafness or partial hearing loss, as well as appropriate intervention strategies. Although different learning activities can be used to enlarge such children’s vocabulary, if the presented words are difficult to learn, it will be very hard to realize this. Identifying difficult words and replacing them with simple ones both make the readability of a text easier and help such children enhance their vocabulary knowledge in a shorter period of time. Therefore, in this study we propose a classification approach that identifies difficult words among a given set of words in English. The lexical and semantic features of the words in the dataset were extracted based on the language rules specific to hearing impaired children. In the classification approach, five popular classification algorithms were used and the algorithms' performance in identifying difficult words was evaluated using various performance metrics. As the results show, the K-Nearest Neighbors algorithm is the most suitable algorithm for identifying difficult words in English for the target group.

Individuals with Autism Spectrum Disorder often struggle to establish and keep positive relationships with peers and adults because of communication and social difficulties. It has been shown that humanoid robots and virtual agents can enable interventionists to maximize engagement during instruction and program for generalization. Humanoid robots have also been employed as a mediator and therapeutic support tool for children with Autistic Spectrum Disorder so that inclusive education for young children with Autism Spectrum Disorder can be achieved. Similarly, the findings obtained in many research studies carried out recently support the use of technology-aided interventions and instruction with interactive virtual agents on children with Autism Spectrum Disorder. Therefore, in this study, the use of humanoid robots and virtual agents for inclusive education is reviewed and a pilot study is presented. In addition, challenges, opportunities and future research directions in this domain are provided.

In recent years, robots have started to become more commonplace in our lives, from factory floors to museums, festivals and shows. They have started to change how we work and play. With an increase in the population of the elderly, they have also been started to be used for caregiving services, and hence many countries have been investing in the robot development. The advancements in robotics and wireless communications has led to the emergence of autonomous caregiving robot teams which cooperate to accomplish a set of tasks assigned by human operators. Although wireless communications and devices are flexible and convenient, they are vulnerable to many risks compared to traditional wired networks. Since robots with wireless communication capability transmit all data types, including sensory, coordination, and control, through radio frequencies, they are open to intruders and attackers unless protected and their openness may lead to many security issues such as data theft, passive listening, and service interruption. In this paper, a secure web-based communication framework is proposed to address potential security threats due to wireless communication in robot-robot and human-robot interaction. The proposed framework is simple and practical, and can be used by caregiving robot teams in the exchange of sensory data as well as coordination and control data.