Automated Driver Health Monitoring System in Automobile Industry Using WOA-DBN Using ECG Waveform

Abstract

Reducing the amount of car accidents and the deaths that result from them requires close monitoring of drivers’ health and alertness. Identifying driver weariness has been a major practical concern and problem in recent years. A number of machine learning algorithms have been used for monitoring the driver’s health system, even though accurate and early identification is more challenging. In order to overcome this issues, vehicle driver health is monitored using wearable ECG based on an optimized Deep Belief Network (DBN) is proposed. The collected ECG raw signal is pre-processed using a notch filter and high pass filter and an adaptive sliding window to improve the signal quality. After that, Wavelet Packet Decomposition (WPD) and the Short Time Fourier Transform (SIFT) are used to extract features from the pre-processed signal. It enables for the extraction of both time and frequency domain data. In order to classify whether a driver is fit to drive, is under stress, or has a heart condition, the extracted statistical features are sent for further classification using an optimized Deep Belief Neural Network (DBN). The walrus optimization technique is utilized to set the learning rate of the DBN classifier in an optimal manner. To prevent collisions between vehicles, the driver will be alerted via a buzzer system in the event of stress or heart problems. According to the results of the experimental research, the proposed technique achieves 95.1% accuracy, 92.5% precision, 96.5% specificity, 93% of recall, and 92.7% of the f1-score. Thus, the driver health monitoring system can be accurately detected using this automated model.