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  1. University of Yangon
  2. Department of Physics

MICROCONTROLLER-IMPLEMENTED ARTIFICIAL NEURAL NETWORK FOR ELECTROOCULOGRAPHY-BASED WEARABLE DROWSINESS DETECTION WITH ALERTSYSTEM

http://hdl.handle.net/20.500.12678/0000002433
http://hdl.handle.net/20.500.12678/0000002433
2c2a23d2-6950-4412-8834-7b4dc1443de1
4828c45c-de26-405e-852f-0d68a6a0fc03
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Microcontroller-Implemented Microcontroller-Implemented Artificial Neural Network for electrooculography-based wearable drowsiness detection with Alert System.pdf (1199 Kb)
Publication type
Other
Upload type
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Title
Title MICROCONTROLLER-IMPLEMENTED ARTIFICIAL NEURAL NETWORK FOR ELECTROOCULOGRAPHY-BASED WEARABLE DROWSINESS DETECTION WITH ALERTSYSTEM
Language en
Publication date 2015
Authors
Tabar, Keith Marlon R.
Caluyo, Felicito S.
Ibarra, Joseph Bryan G.
Description
Drowsiness has been one of the leading causes of work-related accidents. One reason that is pointed out by the Royal Society for the Prevention of Accidents (2001) is that, drowsiness tends to reduce the reaction time and attentiveness of a person resulting to poor performance on attention-based activities. This is due to the fact that the speed at which information is processed in the brain is also reduced by drowsiness (NCSDR/NHTSA 1998).
Different methods have been explored to develop an effective drowsiness detection system (DDS) to give drivers warning of impending drowsiness. However, no study implemented a wearable and standalone DDS. Most of the existing DDS require the use of a computer application or a separate processor for signal processing and drowsiness detection. The present study aimed to design a wearable electrooculography (EOG)-based DDS that doesn't require a computer to operate; to implement an artificial neural network (ANN) into a microcontroller; to determine the best electrode placement setup on the visor cap for optimal extraction of EOG signals; to test the accuracy, precision and sensitivity of the system in real-time; and to evaluate the system in terms of comfort and unobtrusiveness. Figure 1 below shows the methodology from which the development of the system was based upon.
Keywords
Artificial neural network
Identifier https://uyr.uy.edu.mm/handle/123456789/375
Journal articles
8th AUN/SEED-Net Regional Conference on Electrical and Electronics Engineering
Conference papaers
Books/reports/chapters
Thesis/dissertations
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