A Probabilistic Model-Checking Framework for Cognitive Assessment and Training

TL;DR

This paper introduces a probabilistic model-checking framework using Markov chains to assess and train cognitive functions in patients with neurodegenerative diseases through serious games, supporting diagnosis and personalized training.

Contribution

It presents a novel theoretical framework for evaluating diagnostic confidence and a protocol for dynamically adjusting game difficulty based on patient behavior.

Findings

Framework effectively identifies discrepancies in gameplay behavior.

Dynamic difficulty adjustment improves patient engagement.

Model supports diagnosis and training in neurocognitive disorders.

Abstract

Serious games have proven to be effective tools for screening cognitive impairments and supporting diagnosis in patients with neurodegenerative diseases like Alzheimer's and Parkinson's. They also offer cognitive training benefits. According to the DSM-5 classification, cognitive disorders are categorized as Mild Neurocognitive Disorders (mild NCDs) and Major Neurocognitive Disorders (Major NCDs). In this study, we focus on three patient groups: healthy, mild NCD, and Major NCD. We employ Discrete Time Markov Chains to model the behavior exhibited by each group while interacting with serious games. By applying model-checking techniques, we can identify discrepancies between expected and actual gameplay behavior. The primary contribution of this work is a novel theoretical framework designed to assess how a practitioner's confidence level in diagnosing a patient's Alzheimer's stage evolves with each game session (diagnosis support). Additionally, we propose an experimental protocol where the difficulty of subsequent game sessions is dynamically adjusted based on the patient's observed behavior in previous sessions (training support).