Autism spectrum disorder (ASD) is a brain disorder causing issues among many young children. For children suffering from ASD, their learning ability is typically slower when compared to normal children. Therefore, many technologies aiming to teach ASD children with optimized learning approaches have emerged. With this motivation, this study presents a smart autism spectrum disorder learning system based on remote edge healthcare clinics and the Internet of Medical Things, the objective of which is to offer an online education and healthcare environment for autistic children. Concave and convex optimization constraints, such as accuracy, learning score, total processing time with deadline, and resource failure, are considered in the proposed system, with a focus on different autism education learning applications (e.g., speaking, reading, writing, and listening), while respecting the system’s quality of service (QoS) requirements. All of the autism applications are executed on smartwatches, mobile devices, and edge healthcare nodes during their training and analysis in the system. This study presents the smartwatch autism spectrum data learning scheme (SM-ASDS), which consists of different offloading approaches, training analyses, and schemes. The SM-ASDS algorithm methodology includes partitioning offloading and deep convolutional neural network (DCNN)- and adaptive long short-term memory (ALSTM)-based schemes, which are used to train autism-related data on different nodes. The simulation results show that SM-ASDS improved the learning score by 30%, accuracy by 98%, and minimized the total processing time by 33%, when compared to baseline methods. Overall, this study presents an education learning system based on smartwatches for autistic patients, which facilitates educational training for autistic patients based on the use of artificial intelligence techniques.

Current usability evaluation models for e-books do not adequately address the needs of the visually impaired, as they require customisation to effectively evaluate these applications, considering the unique accessibility requirements of the visually impaired and the distinct features of e-books. This study presents a structured three-phase approach to developing a usability evaluation model specifically designed for mobile e-book applications used by the visually impaired. The first phase involved gathering requirements through a literature review and online survey. The second phase focused on verifying the proposed model using the Fuzzy Delphi Method. The final phase involved consolidating the verified measurements to establish the usability evaluation model. Five usability factors were identified: accessibility, satisfaction, effectiveness, learnability, and efficiency. The inclusion of accessibility ensures that the needs of the visually impaired are addressed. Besides, 31 usability metrics were validated through FDM. The findings confirm the applicability of the proposed model by demonstrating systematic expert agreement on the identified factors and metrics. The Fuzzy Delphi Method validates 31 usability metrics, thereby strengthening the reliability and applicability of HCI and accessibility research. This model promotes inclusive technology development, supporting the creation of usable and empowering e-book applications for users who are visually impaired.