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.

A Cost-Effective Eye-Tracker for Early Detection of Mild Cognitive Impairment / D. Greco, F. Masulli, S. Rovetta, A. Cabri, D. Daffonchio (IEEE MEDITERRANEAN ELECTROTECHNICAL CONFERENCE). - In: 2022 IEEE 21st Mediterranean Electrotechnical Conference (MELECON)[s.l] : IEEE, 2022 Aug. - ISBN 978-1-6654-4281-7. - pp. 1141-1146 (( Intervento presentato al 21. convegno MELECON tenutosi a Palermo nel 2022 [10.1109/MELECON53508.2022.9843008].

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

A. Cabri
Penultimo
;
2022

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.
Eye-tracker; Alzheimer’s disease; Mild Cognitive Impairment; Early Detection; Raspberry; Webcam; Heart Rate Variability; Photoplethysmography; Python; Neural Networks
Settore INF/01 - Informatica
ago-2022
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/954135
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