An Application of a Runtime Epistemic Probabilistic Event Calculus to Decision-making in e-Health Systems

TL;DR

This paper introduces a runtime architecture combining sensor data, classifiers, and a probabilistic logic-based decision system for e-Health rehabilitation, enabling adaptive interventions based on real-time cognitive and behavioral assessments.

Contribution

It presents a novel integration of a probabilistic epistemic Event Calculus with machine learning techniques for real-time decision-making in e-Health systems.

Findings

Effective real-time assessment of child's engagement and attention.

Adaptive decision-making improves rehabilitation task engagement.

Integration of probabilistic logic with sensors enhances system robustness.

Abstract

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).