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Calculating Heart Rate Variability from ECG Data from Youth with Cerebral Palsy During Active Video Game Sessions

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Exploiting heart rate variability for driver drowsiness detection using wearable sensors and machine learning.

Zakwan AlArnaout¹, Chamseddine Zaki², Yehia Kotb³

¹College of Engineering and Technology, American University of the Middle East, 54200, Egaila, Kuwait. zakwan.alarnaout@aum.edu.kw.

Scientific Reports

|July 10, 2025

Summary

This summary is machine-generated.

Driver drowsiness detection using heart rate variability (HRV) shows promise for enhancing road safety. A system using wearable sensors and machine learning, particularly Random Forest, effectively predicts drowsiness levels to prevent accidents.

Keywords:

Drowsiness Detection ECG HRV IoT Pervasive Computing Supervised Machine Learning Time Series Segmentation

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Area of Science:

Transportation Safety
Biomedical Engineering
Machine Learning

Background:

Driver drowsiness is a major cause of traffic accidents, impairing crucial driving functions.
Existing methods for drowsiness detection have limitations, necessitating advanced solutions.
Heart Rate Variability (HRV) analysis offers a potential non-invasive method to assess physiological states related to drowsiness.

Purpose of the Study:

To investigate the feasibility of using HRV analysis for detecting driver drowsiness.
To develop and evaluate a system for real-time drowsiness detection using wearable sensors and machine learning.
To identify the most effective machine learning algorithm for accurate and timely drowsiness prediction.

Main Methods:

A system model integrating wearable photoplethysmography (PPG) sensors, smartphones, and cloud servers was proposed.
Two novel algorithms were developed for feature segmentation, labeling, and HRV-based drowsiness prediction.
Six supervised machine learning algorithms were applied to real-driving data for performance evaluation.

Main Results:

The Random Forest (RF) classifier demonstrated the highest performance with 86.05% accuracy, 87.16% precision, 93.61% recall, and 89.02% F1-score.
The Support Vector Machine with Radial Basis Function (SVM-RBF) also exhibited strong generalization, with an F1-score of 87.15%.
RF showed robustness with the smallest mean change between training and testing datasets (-4.30%).

Conclusions:

HRV-based drowsiness detection systems are feasible and can be integrated into Advanced Driver Assistance Systems (ADAS).
The proposed system, particularly with the RF classifier, offers a reliable method for timely driver drowsiness alerts.
Implementing such systems can significantly enhance road safety by reducing accidents caused by driver fatigue.