Research Article | Open Access
DEVELOPMENT OF AN EFFICIENT DRIVER DROWSINESS DETECTION SYSTEM USING EEG AND HEART RATE SENSOR DATA FOR MEASURING ACCURACY AND PRECISION USING NEURAL NETWORK ALGORITHM IN COMPARISON WITH COMPUTER VISION FACIAL FEATURE TECHNIQUE
Jeevan C, Kirupa Ganapathy
Pages: 5415-5424
Abstract
Aim: The aim of this study is to detect drowsiness with a group of people's data and to improve the performance of detecting using Artificial Neural Networks (ANN) in comparison with facial feature technique.
Materials and Methods: The minimum power of the algorithm is fixed as 0.8 and acceptable error is fixed as 0.05 in G power tool. A total of 14980 samples for the analysis. Accuracy was computed for the dataset of 14,980 Electroencephalogram (EEG) data obtained from the UCI database repository
Keywords
Computer Vision Technique, Drowsiness Detection, Electroencephalogram (EEG), Facial Feature Technique, Machine Learning, Neural Network Classifier, Novel Artificial Neural Network Algorithm
