An Investigation to Detect Driver Drowsiness from Eye blink Artifacts Using Deep Learning Models-Reference-Cited by-同舟云学术

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An Investigation to Detect Driver Drowsiness from Eye blink Artifacts Using Deep Learning Models

Published:2022-12-07 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES)
language:
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Author:

Egambaram Ashvaany¹,Badruddin Nasreen²

Affiliation:

1. Universiti Tunku Abdul Rahman,Faculty of Information and Communication Technology,Department of Computer Science,Perak,Malaysia

2. Institute of Health and Analytics, Universiti Teknologi PETRONAS,Department of Electrical and Electronic Engineering,Perak,Malaysia

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10079231/10079241/10079592.pdf?arnumber=10079592

Reference48 articles.

1. Driver Drowsiness Detection by Applying Deep Learning Techniques to Sequences of Images

2. Sustainable Development Goals;Sustainable Development,2021

3. Sleep Stage Classification Using EEG Signal Analysis: A Comprehensive Survey and New Investigation

4. Age- and Gender-Related Test Performance in Community-Dwelling Elderly People: Six-Minute Walk Test, Berg Balance Scale, Timed Up & Go Test, and Gait Speeds

5. Drowsiness Detection System Using Deep Learning

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Ocular Artefact Removal from Electroencephalogram Signals: A Review;2023 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS);2023-11-03

2. Eye-Blink Event Detection Using a Neural-Network-Trained Frame Segment for Woman Drivers in Saudi Arabia;Electronics;2023-06-16

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