Comparative Study of Machine Learning Algorithms in Classifying HRV for the Driver’s Physiological Condition
عنوان مقاله: Comparative Study of Machine Learning Algorithms in Classifying HRV for the Driver’s Physiological Condition
شناسه ملی مقاله: JR_CEJ-9-9_013
منتشر شده در در سال 1402
شناسه ملی مقاله: JR_CEJ-9-9_013
منتشر شده در در سال 1402
مشخصات نویسندگان مقاله:
Siti Fatimah Abdul Razak
S. N. M. Sayed Ismail
Sumendra Yogarayan
Mohd Fikri Azli Abdullah
Noor Hisham Kamis
Azlan Abdul Aziz
خلاصه مقاله:
Siti Fatimah Abdul Razak
S. N. M. Sayed Ismail
Sumendra Yogarayan
Mohd Fikri Azli Abdullah
Noor Hisham Kamis
Azlan Abdul Aziz
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 ۹۲.۸۶%. 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 F۱ 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: ۱۰.۲۸۹۹۱/CEJ-۲۰۲۳-۰۹-۰۹-۰۱۳ Full Text: PDF
کلمات کلیدی: Driver Monitoring System; ADAS; Machine Learning; Vehicle Safety.
صفحه اختصاصی مقاله و دریافت فایل کامل: https://civilica.com/doc/1962988/