A Cost-Effective Eye-Tracker for Early Detection of Mild Cognitive Impairment

TL;DR

This paper introduces a low-cost, machine learning-based eye-tracker using standard webcams for early detection of Mild Cognitive Impairment, integrating stress measurement for comprehensive assessment.

Contribution

It presents a novel, affordable eye-tracking system combining webcams and machine learning for early cognitive impairment detection.

Findings

Effective detection of Mild Cognitive Impairment using the system

Integration of stress estimation enhances assessment accuracy

Cost reduction makes early screening more accessible

Abstract

This paper presents a low-cost eye-tracker aimed at carrying out tests based on a Visual Paired Comparison protocol for the early detection of Mild Cognitive Impairment. The proposed eye-tracking system is based on machine learning algorithms, a standard webcam, and two personal computers that constitute, respectively, the "Measurement Sub-System" performing the test on the patients and the "Test Management Sub-System" used by medical staff for configuring the test protocol, recording the patient data, monitoring the test and storing the test results. The system also integrates an stress estimator based on the measurement of heart rate variability obtained with photoplethysmography.