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Article

Using Wearable ECG/PPG Sensors for Driver Drowsiness Detection Based on Distinguishable Pattern of Recurrence Plots

Bio-Intelligence & Data Mining Laboratory, School of Electronics Engineering, Kyungpook National University, Daegu 41566, Korea
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Electronics 2019, 8(2), 192; https://doi.org/10.3390/electronics8020192
Received: 30 December 2018 / Revised: 27 January 2019 / Accepted: 1 February 2019 / Published: 7 February 2019
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. View Full-Text
Keywords: drowsiness detection; smart band; electrocardiogram (ECG); photoplethysmogram (PPG); recurrence plot (RP); convolutional neural network (CNN) drowsiness detection; smart band; electrocardiogram (ECG); photoplethysmogram (PPG); recurrence plot (RP); convolutional neural network (CNN)
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MDPI and ACS Style

Lee, H.; Lee, J.; Shin, M. Using Wearable ECG/PPG Sensors for Driver Drowsiness Detection Based on Distinguishable Pattern of Recurrence Plots. Electronics 2019, 8, 192. https://doi.org/10.3390/electronics8020192

AMA Style

Lee H, Lee J, Shin M. Using Wearable ECG/PPG Sensors for Driver Drowsiness Detection Based on Distinguishable Pattern of Recurrence Plots. Electronics. 2019; 8(2):192. https://doi.org/10.3390/electronics8020192

Chicago/Turabian Style

Lee, Hyeonjeong, Jaewon Lee, and Miyoung Shin. 2019. "Using Wearable ECG/PPG Sensors for Driver Drowsiness Detection Based on Distinguishable Pattern of Recurrence Plots" Electronics 8, no. 2: 192. https://doi.org/10.3390/electronics8020192

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