Evaluating Head Pose Estimation for Assessing Visual Attention in Children with Special Needs During Robot-Assisted Therapy

This study investigates the application of head pose estimation (HPE) to assess visual attention in children with special needs (CwSN) during robot-assisted therapy sessions, focusing on its effectiveness and the attention patterns exhibited by these children. CwSN often faces unique challenges, such as sensory processing difficulties or delayed cognitive processing. Age and therapy duration also influenced attention levels, with younger children generally exhibiting shorter attention spans than older participants. Additionally, familiarity with technology, such as prior screen time at home, positively impacted engagement during robot-assisted therapy. An experimental study was conducted with 30 children aged 2 to 7 years, including those with autism spectrum disorder (ASD), speech delay (SD), and attention-deficit/hyperactivity disorder (ADHD). Using an integrated camera, head movements were tracked to analyse forward-facing head direction as an indicator of attention. The system achieved an overall accuracy of 82% and an average attention percentage of 65%, highlighting that visual attention varies significantly based on the type of disability, age, and therapy duration. The integration of the robot enhanced visual engagement across all groups, fostering improved interaction and attention. These findings emphasise the importance of tailoring robot-assisted therapy (RAT) to the specific needs and attention patterns of children with different disabilities, ages, and therapy histories, underscoring the potential of assistive robotics to optimise therapeutic outcomes in special education settings. This research highlights the potential of personalised RAT to improve social, cognitive, and motor skills. It offers evidence-based strategies for integrating assistive robotics into special education and therapeutic settings for CwSN.