Abstract
This paper aims to investigate the robust and distinguishable pattern of heart rate variability (HRV) signals, acquired from wearable electrocardiogram (ECG) or photoplethysmogram (PPG) sensors, for driver drowsiness detection. As wearable sensors are so vulnerable to slight movement, they often produce more noise in signals. Thus, from noisy HRV signals, we need to find good traits that differentiate well between drowsy and awake states. To this end, we explored three types of recurrence plots (RPs) generated from the R–R intervals (RRIs) of heartbeats: Bin-RP, Cont-RP, and ReLU-RP. Here Bin-RP is a binary recurrence plot, Cont-RP is a continuous recurrence plot, and ReLU-RP is a thresholded recurrence plot obtained by filtering Cont-RP with a modified rectified linear unit (ReLU) function. By utilizing each of these RPs as input features to a convolutional neural network (CNN), we examined their usefulness for drowsy/awake classification. For experiments, we collected RRIs at drowsy and awake conditions with an ECG sensor of the Polar H7 strap and a PPG sensor of the Microsoft (MS) band 2 in a virtual driving environment. The results showed that ReLU-RP is the most distinct and reliable pattern for drowsiness detection, regardless of sensor types (i.e., ECG or PPG). In particular, the ReLU-RP based CNN models showed their superiority to other conventional models, providing approximately 6–17% better accuracy for ECG and 4–14% for PPG in drowsy/awake classification.
Highlights
Driver drowsiness or fatigue is one of main causal factors to many road accidents
To evaluate the usefulness of each type of recurrence plots (RPs) for drowsiness detection, we developed three different convolutional neural network (CNN) models, which employed Bin-RP, Cont-RP, and rectified linear unit (ReLU)-RP as input features
For comparison, we developed four classification models (i.e., logistic regression (LR), K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF)) using six significant
Summary
Driver drowsiness or fatigue is one of main causal factors to many road accidents. as a car safety technology to reduce such accidents, the driver drowsiness detection problem is widely examined [1,2,3], in which various measures are obtainable. The vehicle-based measures contain wheel position, handle movement, velocity, acceleration, etc These measures have the advantage of being non-invasive and relatively accurate, but are highly dependent on driver’s driving skills, road conditions, and vehicle characteristics. They have some potential risks of taking time in detecting the motion of a vehicle to avoid accidents in real driving situations [4,5,6]. The behavioral measures include driver’s eye state, eye blinking rate, yawning, head movement, and so on These measures were widely used with deep learning technology [6,7,8]. These measures are non-invasive and easy to use, but there are some drawbacks that they are sensitive to camera movement, lighting conditions, and the surrounding environment [1,6,9]
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