Driving Fatigue Identification Based on Multiple Non-intrusive Indicators
Author:
Affiliation:
1. Guangdong Key Laboratory of Intelligent Transportation Systems, School of Intelligent systems, Sun Yatsen University,Guangzhou,China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9798405/9798406/09798504.pdf?arnumber=9798504
Reference27 articles.
1. Drowsy driving detection using neural network with backpropagation algorithm implemented by FPGA;choi;Concurrency Computation Practice Experience,2019
2. Quantitative electro-oculography and electroencephalography as indices of alertness
3. Driver Drowsiness Classification Using Fuzzy Wavelet-Packet-Based Feature-Extraction Algorithm
4. Assessment of Human Fatigue during Physical Performance using Physiological Signals: A Review
5. Real Time Drowsiness Detection by a WSN based Wearable ECG Measurement System
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