Advancing autism therapy: emotion analysis using rehabilitation robots and ai for children with ASD

Abstract

Emotion analysis is a key component in understanding the unique communication patterns and emotional states of children with autism spectrum disorder (ASD). These children often struggle with traditional forms of expressing emotions, which presents a challenge for themselves and their therapists. Facial expression analysis techniques, supported by modern technologies such as machine learning and artificial intelligence, enable more accurate identification of subtle signals that may go unnoticed by human observers. The introduction of rehabilitation robots and emotion analysis software based on the analysis of facial expressions and gestures opens up new possibilities for individualizing therapy, adapting it to the child's specific reactions and needs. In this way, the use of these tools not only increases the effectiveness of treatment but also helps build more trusting therapeutic relationships, which is the basis for adequate support for the development of children with ASD. Regular monitoring of progress and modifying therapeutic approaches, supported by automation and data analysis, is essential to more effective and empathetic care for children with developmental disorders. However, the journey does not end here. Further research is necessary to develop and improve emotion analysis techniques for use in rehabilitation robots and their impact on the effectiveness of therapy for young patients