Recognizing Human Internal States: A Conceptor-Based Approach
This addresses the need for robots to provide quantitative diagnostic information in ASD therapies, though it appears incremental as it builds on existing conceptor methods.
The paper tackles the problem of recognizing human internal states like confusion or engagement in social robots for Autism Spectrum Disorder interventions, proposing a conceptor-based classifier and reporting initial results from a proof-of-concept study.
The past few decades has seen increased interest in the application of social robots to interventions for Autism Spectrum Disorder as behavioural coaches [4]. We consider that robots embedded in therapies could also provide quantitative diagnostic information by observing patient behaviours. The social nature of ASD symptoms means that, to achieve this, robots need to be able to recognize the internal states their human interaction partners are experiencing, e.g. states of confusion, engagement etc. Approaching this problem can be broken down into two questions: (1) what information, accessible to robots, can be used to recognize internal states, and (2) how can a system classify internal states such that it allows for sufficiently detailed diagnostic information? In this paper we discuss these two questions in depth and propose a novel, conceptor-based classifier. We report the initial results of this system in a proof-of-concept study and outline plans for future work.