Utilizing Support Vector Machine to Help with Social Robot Therapy for Autism Spectrum Disorder

Children with autism spectrum disorder (ASD) require long-term care, support, and empathy from experienced therapists. However, there is a shortage num-ber of highly experienced therapists available to provide consistent and high-quality care for all ASD children and teach them effectively. The Robotic-based assessment and treatment offer promising advantages for ASD children. However, many robotic and virtual therapies rely on pre-programmed that may not consider individual needs and cultural differences. This study aims to develop and test the hypothesis that robots can effectively assist in conducting therapy sessions for children with ASD. A Support Vector Machine (SVM) classifier has been used to predict out-comes from sckeleton and gaze basaed features of data generated during both robot-child and therapist-child in-teractions. A comprehensive evaluation across five distinct scenarios, incorporating various combinations of Robot-Enhanced Therapy (RET), Standard Human Treatment (SHT), and their concatenation have been conducted. The article results for both skeleton-based features and gaze-based data reveal a significant performance advantage over existing state-of-the-art methodologies.