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Facial expression analysis in children with autism spectrum disorder using a refined Human-Robot-Game platform for active learning

Abstract:

The use of serious video games and robotics in children's education and therapy is rapidly increasing. Innovative Human-Robot-Game (HRG) platforms are particularly promising for assessing learning and behaviour in children with autism spectrum disorder (ASD), who often face challenges with attention, communication, and socialisation. Research shows that when children with ASD engage with social robots connected to interactive educational games, their learning, attention, and communication skills improve. This study aims to monitor the psychosocial and cognitive progress of children with ASD using an HRG platform, refining it as a tool for active learning. The article focuses on enhancing the facial recognition metrics of the HRG platform, which is identified as LOLY-MIDI and designed to measure attention and emotions in children while playing serious video games with the assistance of a social robot. Using a mixed-methods experimental approach strategy, reviewing previous studies, and employing tools like OpenFace and the Facial Action Coding System (FACS) for facial recognition, the research achieved greater precision in designing these metrics, providing accurate measurements of attention and emotional responses. The findings offer valuable insights for psychology and education professionals in assessing the socio-educational progress of children with ASD.