Implementation of a Novel Intelligent Drowsiness Detection Warning System

A new intelligent drowsiness detection warning system is proposed in this paper. Electrocardiogram (ECG) signals are input from sensors. Through Bluetooth connection, the raw data is received on the remote end. In order to obtain accurate R-R interval data, this study uses the R waveform of the ECG signal to be detected and uses the enhanced So and Chan method to obtain the waveform signal. Perform preprocessing and FFT based on the captured signal, and calculate power spectral density (PSD) data. Finally, the proposed novel drowsiness judgement and analysis are performed. The entire system is implemented on Zedboard. Graphical user interface is developed using the Linux operating system. It can immediately display the ECG waveform, PSD analysis of heart rate variability, sympathetic/parasympathetic nervous balance index, and smile levels, which can effectively indicate the diver’s physiological state.