Incorporation of Physiological Features in Drowsiness Detection Using Deep Neural Network Approach

Mostafa Zaman, Sujay Saha, Nathan Puryear, Nasibeh Zohrabi, Sherif Abdelwahed

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

The rapid growth in the number of vehicles on the road has exacerbated traffic congestion and the likelihood of more road accidents. Implementing a smart accident prevention system in the subsequent years will be necessary since the number of fatalities increases exponentially. Drowsiness is a feeling that occurs just before falling asleep or the desire to sleep that is very strong for an unusually long period. Therefore, it is indispensable to assess the physical and psychological factors that may impact a driver's reflexes, resulting in decreased reaction times. One of the primary causes of vehicle accidents is driver fatigue and weariness. When operating a vehicle, driving a car, one must be focused and attentive and careful. This paper proposes a drowsiness detection method that integrates machine learning and physiological approaches such as heart rate and blood oxygen level. We have presented an efficient system to deal with real-time driver drowsiness detection using Convolutional Neural Network and other human biological features, including the blood oxygen level and cardiac rate.

Original languageEnglish (US)
Title of host publication2022 IEEE Transportation Electrification Conference and Expo, ITEC 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages219-224
Number of pages6
ISBN (Electronic)9781665405607
DOIs
StatePublished - 2022
Event2022 IEEE Transportation Electrification Conference and Expo, ITEC 2022 - Anaheim, United States
Duration: Jun 15 2022Jun 17 2022

Publication series

Name2022 IEEE Transportation Electrification Conference and Expo, ITEC 2022

Conference

Conference2022 IEEE Transportation Electrification Conference and Expo, ITEC 2022
Country/TerritoryUnited States
CityAnaheim
Period6/15/226/17/22

All Science Journal Classification (ASJC) codes

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Mechanical Engineering
  • Transportation

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