Automatic drowsiness detection using convolution neural network

Driver rest is one of the main sources of mishaps and passing for some individuals and the economy. As of late, specialists have started to perceive that lack of sleep is a positive, industry-driving reaction. In this article, we suggest including CNN-based savvy glasses for eye acknowledgment and r...

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Main Authors: Jancy, S., Abhishek, Konagalla, Charan, Olla Yaswanth, Latha, S. Pushpa, Mary, A. Viji Amutha, Priyadarshini, E., Deepa, A.
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural networks
Rest
Sleep deprivation
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creator Jancy, S.
Abhishek, Konagalla
Charan, Olla Yaswanth
Latha, S. Pushpa
Mary, A. Viji Amutha
Priyadarshini, E.
Deepa, A.
description Driver rest is one of the main sources of mishaps and passing for some individuals and the economy. As of late, specialists have started to perceive that lack of sleep is a positive, industry-driving reaction. In this article, we suggest including CNN-based savvy glasses for eye acknowledgment and rest estimation. This new response is contrasted with the customary technique in view of the strategy for comprehension. Both execution, battery utilization, and impression are assessed for execution in incorporated glass. The outcomes show that CNN has over 7% of the precision found at the algorithmic level. Moreover, migraines in memory and battery duration are genuine and make CNN a potential answer for sleep deprivation.
doi_str_mv 10.1063/5.0208907
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source American Institute of Physics:Jisc Collections:Transitional Journals Agreement 2021-23 (Reading list)
subjects Artificial neural networks
Rest
Sleep deprivation
title Automatic drowsiness detection using convolution neural network
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