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Introduction: Autism spectrum disorder (ASD) is a group of complex lifelong neurodevelopmental disorders, characterized by difficulties in social communication and stereotyped behaviours. Due to the increasing number of children with ASD, it is important to continue developing interventions as well as invent new ones. Human-robot interaction can contribute to better outcomes for these children. There are several robots such as Nao, Kaspar, ZENO, Probo, ZECA, etc. which are used in autism interventions. Many mobile and web applications are in constant growth, too. They target skills such as collaboration, social skills, language skills, social competence, and communication. Aim: To explore the usability of the humanoid robot Kaspar and a complementary app in interventions of children with ASD. Sample: 20 children with ASD, aged between 23 and 76 months old. Method: As an added intervention for this group of children, we used the robot Kaspar and its complementary app. Kaspar is a child-sized humanoid robot that uses bodily expressions, facial expressions, gestures, and pre-recorded speech to interact with a human. Results: This intervention achieved certain positive shifts in eight of the eleven measured developmental domains, such as communication functions and means, turn taking, imitation, language skills, play, attention and daily life skills. The three categories that had inconsiderable improvement are vocalization and speech, cause and effect and coping skills. Conclusion: Based on the measurements before and after the use of Kaspar and its complementary app, there is improvement, primarily in the domains of language, imitation and communication skills and attention. [ABSTRACT FROM AUTHOR]

The automatic emotion recognition domain brings new methods and technologies that might be used to enhance therapy of children with autism. The paper aims at the exploration of methods and tools used to recognize emotions in children. It presents a literature review study that was performed using a systematic approach and PRISMA methodology for reporting quantitative and qualitative results. Diverse observation channels and modalities are used in the analyzed studies, including facial expressions, prosody of speech, and physiological signals. Regarding representation models, the basic emotions are the most frequently recognized, especially happiness, fear, and sadness. Both single-channel and multichannel approaches are applied, with a preference for the first one. For multimodal recognition, early fusion was the most frequently applied. SVM and neural networks were the most popular for building classifiers. Qualitative analysis revealed important clues on participant group construction and the most common combinations of modalities and methods. All channels are reported to be prone to some disturbance, and as a result, information on a specific symptoms of emotions might be temporarily or permanently unavailable. The challenges of proper stimuli, labelling methods, and the creation of open datasets were also identified. [ABSTRACT FROM AUTHOR]