ReLU-RP based CNN models using wearable ECG or PPG sensors improved drowsy/awake classification accuracy by approximately 6–17% for ECG and 4–14% for PPG compared to conventional models.
Does a ReLU-RP based CNN model improve drowsy/awake classification accuracy using wearable ECG/PPG sensors compared to conventional models?
ReLU-RP based CNN models using wearable ECG or PPG sensors significantly improve the accuracy of driver drowsiness detection compared to conventional models.
Effect estimate: 6-17% better accuracy for ECG and 4-14% for PPG
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.
Lee et al. (Thu,) conducted a other in Driver drowsiness. ReLU-RP based CNN models using wearable ECG or PPG sensors vs. Other conventional models was evaluated on Drowsy/awake classification accuracy (6-17% better accuracy for ECG and 4-14% for PPG). ReLU-RP based CNN models using wearable ECG or PPG sensors improved drowsy/awake classification accuracy by approximately 6–17% for ECG and 4–14% for PPG compared to conventional models.