Characterization of Pupillary Light Response through Low-Cost Pupillometry and Machine Learning Techniques

Eng Pub Date : 2024-06-07 DOI:10.3390/eng5020059
D. Gutierrez-Hernandez, Miguel S. Gómez-Díaz, Francisco J. Casillas-Rodríguez, Emmanuel Ovalle-Magallanes
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引用次数: 0

Abstract

This article employed pupillometry as a non-invasive technique to analyze pupillary light reflex (PLR) using LED flash stimuli. Particularly, for the experiments, only the red LED with a wavelength of 600 nm served as the light stimulation source. To stabilize the initial pupil size, a pre-stimulus (PRE) period of 3 s was implemented, followed by a 1 s stimulation period (ON) and a 4 s post-stimulus period (POST). Moreover, an experimental, low-cost pupillometer prototype was designed to capture pupillary images of 13 participants. The prototype consists of a 2-megapixel web camera and a lighting system comprising infrared and RGB LEDs for image capture in low-light conditions and stimulus induction, respectively. The study reveals several characteristic features for classifying the phenomenon, notably the mobility of Hjórth parameters, achieving classification percentages ranging from 97% to 99%, and offering novel insights into pattern recognition in pupillary activity. Moreover, the proposed device successfully captured the PLR from all the participants with zero reported incidents or health affectations.
通过低成本瞳孔测量和机器学习技术表征瞳孔光反应
本文采用瞳孔测量法作为一种非侵入性技术,利用 LED 闪光刺激来分析瞳孔光反射(PLR)。在实验中,仅使用波长为 600 nm 的红色 LED 作为光刺激源。为了稳定初始瞳孔大小,实验中设置了 3 秒钟的刺激前(PRE)时间,然后是 1 秒钟的刺激时间(ON)和 4 秒钟的刺激后(POST)时间。此外,还设计了一个低成本的瞳孔仪实验原型,用于捕捉 13 名参与者的瞳孔图像。原型包括一个 200 万像素的网络摄像头和一个由红外线和 RGB LED 组成的照明系统,分别用于在弱光条件下捕捉图像和刺激诱导。这项研究揭示了对瞳孔现象进行分类的几个特征,特别是 Hjórth 参数的流动性,分类率达到 97% 到 99%,并为瞳孔活动的模式识别提供了新的见解。此外,所提议的设备还成功捕获了所有参与者的瞳孔活动,且无任何事故或健康影响报告。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Eng
Eng
CiteScore
2.10
自引率
0.00%
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