Abstract
Driving fatigue is the cause of many traffic accidents and poses a serious threat to road safety. To address this issue, this paper aims to develop a system for the early detection of driver fatigue. The system leverages heart rate variability (HRV) features and embedded machine learning to estimate the driver’s fatigue level. The driver’s HRV is derived from electrocardiogram (ECG) signals captured by a wearable device for analysis. Time- and frequency-domain HRV features are then extracted and used as the input for a machine learning classifier. A dataset of HRV features is collected from a driving simulation experiment involving 18 participants. Four machine learning classifiers are evaluated, and a backpropagation neural network (BPNN) is selected for its superior performance, achieving up to 94.35% accuracy. The optimized classifier is successfully deployed on an embedded system, providing a cost-effective and portable solution for the early detection of driver fatigue. The results demonstrate the feasibility of using HRV-based machine learning models for the early detection of driver fatigue, contributing to enhanced road safety and a reduced accident risk.
Published Version
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