  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.
