This paper proposes a low-cost methodology for analyzing the dynamics of constriction of a human pupil while subjected to a light stimulus: this phoenomenon is commonly known as Pupillary Light Reflex (PLR) and is widely utilized in medical field to diagnose a variety of diseases. In particular, the analysis of the PLR in this paper is preparatory to the development of a Driver Drowsiness Detection System (DDDS), which reveals the driver’s sleepiness state by measuring the pupil’s constriction dynamics. The test protocol consists in applying a light stimulus to one eye of the subject and to capture the dynamics of constriction of both eyes through cameras; the proposed methodology extracts from the video sequences the time profile of the pupil diameter, from which dynamic and static features are obtained by fitting a simplified 1st-order model with delay. Finally, conclusions on the intraand inter-subject variability of such features are drawn and possible DDDS strategies are proposed based on the obtained results.
A Low-Cost System for Dynamic Analysis of Pupillary Light Response for a Driver Drowsiness Detection System
Alessandro Amodio;ERMIDORO, MICHELE;MAGGI, DAVIDE;Sergio Matteo Savaresi
2018-01-01
Abstract
This paper proposes a low-cost methodology for analyzing the dynamics of constriction of a human pupil while subjected to a light stimulus: this phoenomenon is commonly known as Pupillary Light Reflex (PLR) and is widely utilized in medical field to diagnose a variety of diseases. In particular, the analysis of the PLR in this paper is preparatory to the development of a Driver Drowsiness Detection System (DDDS), which reveals the driver’s sleepiness state by measuring the pupil’s constriction dynamics. The test protocol consists in applying a light stimulus to one eye of the subject and to capture the dynamics of constriction of both eyes through cameras; the proposed methodology extracts from the video sequences the time profile of the pupil diameter, from which dynamic and static features are obtained by fitting a simplified 1st-order model with delay. Finally, conclusions on the intraand inter-subject variability of such features are drawn and possible DDDS strategies are proposed based on the obtained results.File | Dimensione | Formato | |
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