Real-Time Drowsiness Detection Using Wearable, Lightweight Brain Sensing Headbands

Details

• Personal Author:
  Elwarfalli I ; Kavi R ; Kecojevic V ; Kulathumani V ; Nimbarte A ; Rohit F
• Description:
  The feasibility of real-time drowsiness detection using commercially available, off-the-shelf, lightweight, wearable electroencephalogram (EEG) sensors is explored. While EEG signals are known to be reliable indicators of fatigue and drowsiness, they have not been used widely due to their size and form factor. However, the use of lightweight wearable EEGs alleviates this concern. Spectral analysis of EEG signals from these sensors using support vector machines (SVMs) is shown to classify drowsy states with high accuracy. The system is validated using data collected on 23 subjects in fresh and drowsy states. An accuracy of 81% is obtained at a per-subject level and 74% in cross-subject validation using SVM with radial basis kernel. Using a temporal aggregation strategy, the cross-subject validation accuracy is shown to improve to 87%. The EEG signals are also used to characterise the blink duration and frequency of subjects. However, classification of drowsy states using blink analysis is shown to have lower accuracy than that using spectral analysis. [Description provided by NIOSH]
• Subjects:
  Fatigue Occupational Health Safety Sleep Sleep Initiation And Maintenance Disorders
• Keywords:
  Brain Electrical Activity Electroencephalography Emerging Technology Sensors Sleep Deprivation Wearable Technologies
• ISSN:
  1751-956X
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  OSHA Region 3 ; West Virginia
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  255-263
• Volume:
  11
• Issue:
  5
• NIOSHTIC Number:
  nn:20063414
• Citation:
  IET Intel Transport Syst 2017 Jun; 11(5):255-263
• Contact Point Address:
  Vinod Kulathumani, Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV, 26506 USA
• Email:
  vinod.kulathumani@mail.wvu.edu
• Federal Fiscal Year:
  2017
• Performing Organization:
  West Virginia University
• Peer Reviewed:
  True
• Start Date:
  20050701
• Source Full Name:
  IET Intelligent Transport Systems
• End Date:
  20250630
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:15014d27411466cc4767b1788ad8bb1318484dfc1e5bc0e0edf2a68ae280abf927525239c9febfe8ea790ff89d44f497931601befea9981b0183d569edfc91bb
• Download URL:
  https://stacks.cdc.gov/view/cdc/226481/cdc_226481_DS1.pdf
• File Type:
  [PDF - 3.07 MB ]