Thus, the development of an automated and intelligent system that accomplishes this task has been proven to be challenging so far. Indeed, engagement is a multi-faceted and multi-modal construct that requires high accuracy in the analysis and interpretation of contextual, verbal and non-verbal cues. Several factors contribute to engagement state inference, which include the interaction context and interactants' behaviours and identity. Therefore, to develop successful human-centered human-machine interaction applications, automatic engagement inference is one of the tasks required to achieve engaging interactions between humans and machines, and to make machines attuned to their users, hence enhancing user satisfaction and technology acceptance. Robot perception of affect and engagement in children with autism and have implications for the design of future autism therapies.Īn integral part of seamless human-human communication is engagement, the process by which two or more participants establish, maintain, and end their perceived connection. These results demonstrate the feasibility of We evaluated this framework on a multimodal (audio, video, and autonomic physiology) data set of 35 children (ages 3 to 13) with autism, from two cultures (Asia and Europe), and achieved an average agreement (intraclass correlation) of ~60% with human experts in the estimation of affect and engagement, also outperforming non-personalized ML solutions. We personalized our framework to each child using their contextual information (demographics and behavioral assessment scores) and individual characteristics. Instead of using the traditional one-size-fits-all ML approach, To tackle the heterogeneity in children with autism, we used the latest advances in deep learning to formulate a personalized machine learning (ML) framework for automatic perception of the children’s affective states andĮngagement during robot-assisted autism therapy. Their inference challenge is made even harder by the fact that many individuals with autism have atypical and unusually diverse styles of expressing their affective-cognitive states. However, existing robots are limited in their ability to automatically perceive and respond to human affect, which is necessary for establishing and maintaining engaging interactions. Robots have the potential to facilitate future therapies for children on the autism spectrum.
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