Key result
K-Nearest Neighbor and Decision Tree models using time-domain HRV achieve ~93% accuracy classifying driver physiological states.
Why the study?
Heart Rate Variability (HRV) may serve as a psychological marker to assess drivers' states from physiological signals, but machine learning approaches for classifying driver cardiac health based on HRV require evaluation.
Machine learning algorithms, specifically K-Nearest Neighbor and Decision Tree, can accurately classify driver physiological states using HRV features from ECG data.
Heart Rate Variability (HRV) may be used as a psychological marker to assess drivers’ states from physiological signals such as an electrocardiogram (ECG), electroencephalogram (EEG), and photoplethysmography (PPG). This paper reviews HRV acquisition methods from drivers and machine learning approaches for driver cardiac health based on HRV classification. The study examines four publicly available ECG datasets and analyzes their HRV features, including time domain, frequency domain, short-term measures, and a combination of time and frequency domains. Eight machine learning classifiers, namely K-Nearest Neighbor, Decision Tree, Naive Bayes, Linear Discriminant Analysis, Support Vector Machine, Random Forest, Gradient Boost, and Adaboost, were used to determine whether the driver's state is normal or abnormal. The results show that K-Nearest Neighbor and Decision Tree classifiers had the highest accuracy at 92.86%. The study concludes by assessing the performance of machine learning algorithms in classifying HRV for the driver's physiological condition using the Man-Whitney U test in terms of accuracy and F1 score. We have statistical evidence to support that the prediction quality is different when HRV analysis applies these three sets: (i) time domain measures or frequency domain measures; (ii) frequency domain measures or short-term measures; and (iii) combining time and frequency domains or only frequency domains. Doi: 10.28991/CEJ-2023-09-09-013 Full Text: PDF
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Razak et al. (2023) studied Driver physiological condition (stress, drowsiness, workload) (n=50). Machine learning algorithms (K-Nearest Neighbor and Decision Tree) using HRV features vs. Other machine learning classifiers (Naive Bayes, LDA, SVM, Random Forest, Gradient Boost, Adaboost) was evaluated on Classification accuracy of driver's physiological condition (normal vs abnormal). K-Nearest Neighbor and Decision Tree classifiers achieved the highest accuracy of 92.86% in classifying driver physiological states using time-domain HRV features.
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