Applying neural network analysis on heart rate variability data to assess driver fatigue
By M. Patel, Sara Lal, Diarmuid Kavanagh, Peter Rossiter. Expert systems with applications, 2011
409 citations recorded by the source corpus.
Subfields: Biomedical Engineering, Cardiology and Cardiovascular Medicine, Experimental and Cognitive Psychology
View Paper: https://www.semanticscholar.org/paper/410a2d97c0ae3fed77327c57fa1fb16f90f9b70b
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Biomedical Engineering
In Cardiology and Cardiovascular Medicine
In Experimental and Cognitive Psychology
- Arturas Kaklauskas
- Kenneth Sundaraj
- Murugappan Murugappan
- Arun Sahayadhas
- Gulbadan Sikander
- Shahzad Anwar
- Vytautas Bucinskas
- Andrius Dzedzickis
Citations in the corpus
- Driver Fatigue Detection Systems: A Review (2019, methodology): “ANN has been widely incorporated in fatigue detection systems [48], [49].”
- Human Emotion Recognition: Review of Sensors and Methods (2020, methodology): “Fatigue caused by driving for extended hours HRV Neural network The neural network gave an accuracy of 90% [186]”
- Detecting Driver Drowsiness Based on Sensors: A Review (2012, background): “[58] ECG Band Pass Filter Fast Fourier Transform (FFT) Neural Network 90%”