Base on Long Short-term Memory Network for Fatigue Detection

Guo Wei Gao, Mei Yung Chen

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

Abstract

This paper focuses on a real-time fatigue detection flow. The system will be doing this all inside of python and building it up step by step to be able to detect a bunch of different poses and specifically signs of drowsiness. In order to do that we use a few key models and using media pipe holistic to be able to extract key points. This is going to allow us to extract key points from our face. The system uses tensorflow and keras and builds up a long short-term memory(LSTM) model to be able to predict the action which is being shown on the screen. We need to do is collect a bunch of data on all of our different key points, so we collect data on our face and save those as numpy arrays. The face detection method is based on a deep neural network using LSTM layers to go on ahead and predict that temporal component, which be able to predict action from a number of frames not just a single frame. Integrate using opencv and then proceed to make real-time predictions using the webcam.

Original languageEnglish
Title of host publicationICSSE 2022 - 2022 International Conference on System Science and Engineering
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages55-58
Number of pages4
ISBN (Electronic)9781665488525
DOIs
Publication statusPublished - 2022
Event2022 International Conference on System Science and Engineering, ICSSE 2022 - Virtual, Online, Taiwan
Duration: 2022 May 262022 May 29

Publication series

NameICSSE 2022 - 2022 International Conference on System Science and Engineering

Conference

Conference2022 International Conference on System Science and Engineering, ICSSE 2022
Country/TerritoryTaiwan
CityVirtual, Online
Period2022/05/262022/05/29

Keywords

  • fatigue detection
  • long short-term memory network
  • mediapipe
  • tensorflow

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Mechanical Engineering
  • Control and Optimization
  • Artificial Intelligence
  • Computer Science Applications
  • Information Systems

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