The design principles of edutainment system for autistic children with communication difficulties

The number of children with autism is increasing worldwide. Children with autism face three major problems; socializing, communicating, and behaviour. Approximately 50% of all individuals with autism have difficulties in developing functional language owing to communication deterioration. Mobile devices with installed educational games help these individuals feel more comfortable and relaxed doing such activities. Although numerous mobile applications are available for individuals with autism, they are difficult to use; particularly in terms of user-interface design. This study analysed the existing apps in order to determine the design principles applicable to the Edutainment App being studied. Five applications were involved in this analysis. As outlined in the objectives of this study, identifying these design principles is important in designing the app. The analysis identified fifteen suggestions for the design principles. These suggestions addressed, simple interfaces; image size; number of pictures; home page icon; colour; having images identical to real life objects; the use of caregivers; navigation; password-protection; audio appropriate to the images; the app language used; evaluating parameters to measure the child’s growth; option for photo loading; PECS-based communication; and sentence pronunciation function. These recommendations are offered by this study towards designing and developing a prototype app for autistic children. This study introduces an edutainment-system design principle formulated to help develop the communication skills of children with autism-spectrum disorders. This study also describes the design, implementation, and evaluation of the ICanTalk app-a mobile edutainment app that can be used to improve users’ understanding and communication skills and help them to connect with society and the surrounding environment particularly for autistic children with communication difficulties. This app allows caregivers to create personalized content using pictures and audio on their mobile devices (tablets). The evaluation of the app by teachers suggests that it is useful and easy-to-use. In conclusion, based on the evaluation results, it is found that the ICanTalk app is effective in helping autistic children with communication difficultie

ENGAGEMENT RECOGNITION WITHIN ROBOT-ASSISTED AUTISM THERAPY

Autism is a neurodevelopmental condition typically diagnosed in early childhood, which is characterized by challenges in using language and understanding abstract concepts, effective communication, and building social relationships. The utilization of social robots in autism therapy represents a significant area of research. An increasing number of studies explore the use of social robots as mediators between therapists and children diagnosed with autism. Assessing a child’s engagement can enhance the effectiveness of robot-assisted interventions while also providing an objective metric for later analysis. The thesis begins with a comprehensive multiple-session study involving 11 children diagnosed with autism and Attention Deficit Hyperactivity Disorder (ADHD). This study employs multi-purposeful robot activities designed to target various aspects of autism. The study yields both quantitative and qualitative findings based on four behavioural measures that were obtained from video recordings of the sessions. Statistical analysis reveals that adaptive therapy provides a longer engagement duration as compared to non-adaptive therapy sessions. Engagement is a key element in evaluating autism therapy sessions that are needed for acquiring knowledge and practising new skills necessary for social and cognitive development. With the aim to create an engagement recognition model, this research work also involves the manual labelling of collected videos to generate a QAMQOR dataset. This dataset comprises 194 therapy sessions, spanning over 48 hours of video recordings. Additionally, it includes demographic information for 34 children diagnosed with ASD. It is important to note that videos of 23 children with autism were collected from previous records. The QAMQOR dataset was evaluated using standard machine learning and deep learning approaches. However, the development of an accurate engagement recognition model remains challenging due to the unique personal characteristics of each individual with autism. In order to address this challenge and improve recognition accuracy, this PhD work also explores a data-driven model using transfer learning techniques. Our study contributes to addressing the challenges faced by machine learning in recognizing engagement among children with autism, such as diverse engagement activities, multimodal raw data, and the resources and time required for data collection. This research work contributes to the growing field of using social robots in autism therapy by illuminating an understanding of the importance of adaptive therapy and providing valuable insights into engagement recognition. The findings serve as a foundation for further advancements in personalized and effective robot-assisted interventions for individuals with autism