  Robots have great potential to facilitate future therapies for children on
the autism spectrum. However, existing robots lack the ability to automatically
perceive and respond to human affect, which is necessary for establishing and
maintaining engaging interactions. Moreover, their inference challenge is made
harder by the fact that many individuals with autism have atypical and
unusually diverse styles of expressing their affective-cognitive states. To
tackle the heterogeneity in behavioral cues of children with autism, we use the
latest advances in deep learning to formulate a personalized machine learning
(ML) framework for automatic perception of the childrens affective states and
engagement during robot-assisted autism therapy. The key to our approach is a
novel shift from the traditional ML paradigm - instead of using
'one-size-fits-all' ML models, our personalized ML framework is optimized for
each child by leveraging relevant contextual information (demographics and
behavioral assessment scores) and individual characteristics of each child. We
designed and evaluated this framework using a dataset of multi-modal audio,
video and autonomic physiology data of 35 children with autism (age 3-13) and
from 2 cultures (Asia and Europe), participating in a 25-minute child-robot
interaction (~500k datapoints). Our experiments confirm the feasibility of the
robot perception of affect and engagement, showing clear improvements due to
the model personalization. The proposed approach has potential to improve
existing therapies for autism by offering more efficient monitoring and
summarization of the therapy progress.
