  We present and discuss a runtime architecture that integrates sensorial data
and classifiers with a logic-based decision-making system in the context of an
e-Health system for the rehabilitation of children with neuromotor disorders.
In this application, children perform a rehabilitation task in the form of
games. The main aim of the system is to derive a set of parameters the child's
current level of cognitive and behavioral performance (e.g., engagement,
attention, task accuracy) from the available sensors and classifiers (e.g., eye
trackers, motion sensors, emotion recognition techniques) and take decisions
accordingly. These decisions are typically aimed at improving the child's
performance by triggering appropriate re-engagement stimuli when their
attention is low, by changing the game or making it more difficult when the
child is losing interest in the task as it is too easy. Alongside
state-of-the-art techniques for emotion recognition and head pose estimation,
we use a runtime variant of a probabilistic and epistemic logic programming
dialect of the Event Calculus, known as the Epistemic Probabilistic Event
Calculus. In particular, the probabilistic component of this symbolic framework
allows for a natural interface with the machine learning techniques. We
overview the architecture and its components, and show some of its
characteristics through a discussion of a running example and experiments.
Under consideration for publication in Theory and Practice of Logic Programming
(TPLP).
