Drowsiness is one of the first casualty factors of car accidents. A large number of studies have been conducted to reduce the risk of car accidents and, many of them, are based on the detection of biological signals to determine driver drowsiness. In this way, several prototypes have been proposed but all of them are efficient in specific scenarios only. Photoplethysmography (PPG) is a non-invasive tool that allows monitoring heart activity, it is also used to evaluate driver drowsiness. This paper introduces a prototype based on PPG signals able to improve current systems in terms of evaluation time and results clearness. We performed a measurement campaign to compare experimental data with literature. The goal is to validate the prototype.
Amidei, A., Fallica, P.G., Conoci, S., Pavan, P. (2021). Validating Photoplethysmography (PPG) data for driver drowsiness detection. 345 E 47TH ST, NEW YORK, NY 10017 USA : IEEE [10.1109/MetroAutomotive50197.2021.9502865].
Validating Photoplethysmography (PPG) data for driver drowsiness detection
Amidei, A
;Conoci, S;
2021
Abstract
Drowsiness is one of the first casualty factors of car accidents. A large number of studies have been conducted to reduce the risk of car accidents and, many of them, are based on the detection of biological signals to determine driver drowsiness. In this way, several prototypes have been proposed but all of them are efficient in specific scenarios only. Photoplethysmography (PPG) is a non-invasive tool that allows monitoring heart activity, it is also used to evaluate driver drowsiness. This paper introduces a prototype based on PPG signals able to improve current systems in terms of evaluation time and results clearness. We performed a measurement campaign to compare experimental data with literature. The goal is to validate the prototype.File | Dimensione | Formato | |
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