Caregiver Assessment Using Smart Gaming Technology: A Preliminary Approach

TL;DR

This paper introduces CAST, a mobile gaming app that non-invasively assesses caregiver performance for dementia patients using personalized, Fuzzy Inference System-based difficulty adjustments, aiming to improve care quality.

Contribution

Development of CAST, a novel personalized gaming tool utilizing Fuzzy Inference System and Genetic Algorithm for caregiver assessment in dementia care.

Findings

Preliminary results show effective task difficulty calibration.

Personalized assessments correlate with caregiver performance.

The approach offers a non-invasive evaluation method.

Abstract

As pre-diagnostic technologies are becoming increasingly accessible, using them to improve the quality of care available to dementia patients and their caregivers is of increasing interest. Specifically, we aim to develop a tool for non-invasively assessing task performance in a simple gaming application. To address this, we have developed Caregiver Assessment using Smart Gaming Technology (CAST), a mobile application that personalizes a traditional word scramble game. Its core functionality uses a Fuzzy Inference System (FIS) optimized via a Genetic Algorithm (GA) to provide customized performance measures for each user of the system. With CAST, we match the relative level of difficulty of play using the individual's ability to solve the word scramble tasks. We provide an analysis of the preliminary results for determining task difficulty, with respect to our current participant cohort.