machine Learning and Humanoid Robots for Measuring Engagement of Children with Autism (H2020-MSCA-IF-2015)

Engaging children with ASC (Autism Spectrum Conditions) in communication centred activities during educational therapy is one of the cardinal challenges by ASC and contributes to its poor outcome. To this end, therapists recently started using humanoid robots (e.g., NAO) as assistive tools. However, this technology lacks the ability to autonomously engage with children, which is the key for improving the therapy and, thus, learning opportunities. Existing approaches typically use machine learning algorithms to estimate the engagement of children with ASC from their head-pose or eye-gaze inferred from face-videos. These approaches are rather limited for modeling atypical behavioral displays of engagement of children with ASC, which can vary considerably across the children. The first objective of EngageME is to bring novel machine learning models that can for the first time effectively leverage multi-modal behavioural cues, including facial expressions, head pose, vocal and physiological cues, to realize fully automated context-sensitive estimation of engagement levels of children with ASC. These models build upon dynamic graph models for multi-modal ordinal data, based on state-of-the-art machine learning approaches to sequence classification and domain adaptation, which can personalize to each child, while still being able to generalize across children and cultures. EngageME is expected to bring novel technology/models for endowing assistive robots with ability to accurately ‘sense’ engagement levels of children with ASC during robot-assisted therapy, while providing the candidate with a set of skills needed to become one of the frontiers in the emerging field of affect-sensitive assistive technology.

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