Driver Drowsiness Detection Using ECG Signals and Machine Learning Models

Fatigue and drowsiness are responsible for a significant percentage of road traffic accidents. There are several approaches to monitor the driver's drowsiness, ranging from the driver's steering behavior to analysis of the driver, e.g. eye tracking, blinking, yawning or electrocardiogram (ECG). This paper describes the development of a low-cost ECG sensor to derive heart rate variability (HRV) data for the drowsiness detection. The work includes the hardware and the software design. The hardware has been implemented on an Arduino using ECG AD 8232 model attached to a Raspberry Pi device for processing purposes. The digital ECG signal is transferred to a Raspberry Pi embedded PC where the processing takes place, including QRS-complex, heart rate and HRV detection as well as visualization features. The compact resulting sensor provides good results in the extraction of the main ECG parameters. Different machine learning algorithms are implemented to classify the ECG signals into mainly two categories (Sleep and Awake). Support Vector Machine using the Radial Bias Function Kernel (RBF) achieved accuracy of 95% in inference stage. Another Decision Tree classifier has been also designed and also produced a high accuracy of 98% during the evaluation phase.