Next Article in Journal
Social-Aware Peer Selection for Device-to-Device Communications in Dense Small-Cell Networks
Next Article in Special Issue
Anxiety Level Recognition for Virtual Reality Therapy System Using Physiological Signals
Previous Article in Journal
Efficient QC-LDPC Encoder for 5G New Radio
Open AccessArticle

Utilizing HRV-Derived Respiration Measures for Driver Drowsiness Detection

Bio-Intelligence & Data Mining Lab, School of Electronics Engineering, Kyungpook National University, Daegu 41566, Korea
*
Author to whom correspondence should be addressed.
Electronics 2019, 8(6), 669; https://doi.org/10.3390/electronics8060669
Received: 13 May 2019 / Revised: 10 June 2019 / Accepted: 12 June 2019 / Published: 13 June 2019
This study aims to utilize heart rate variability (HRV) signals obtained with a wearable sensor for driver drowsiness detection. To this end, we investigated respiration characteristics derived from HRV signals based on the known fact that respiratory activity can be estimated from the high frequency (HF) band of HRV signals. For drowsiness detection, many earlier works commonly used dominant respiration (DR) characteristics. However, in some situations where emphasized power in a power spectrum of HRV occurs at multi sub-frequency, the DR measures may possibly fail to capture overall respiration characteristics. To handle this problem, we propose two spectral indices, the weighted mean (WM) and the weighted standard deviation (WSD) of the HF band in the power spectrum. These indices are used to properly capture the overall shape of the respiratory activity shown through the HF band of the HRV power spectrum as an alternative to the DR measures. For experiments, we collected HRV data with an electrocardiogram device worn on the body under a virtual driving environment. The proposed indices somewhat clearly showed the tendency that respiratory frequency decreases and respiration regularity increases in drowsy states of all subjects, while existing DR measures hardly showed this. In addition, when the proposed indices are used alone or together with conventional HRV-related measures as input features for classification models, they showed the best performance in distinguishing drowsiness from wakefulness. View Full-Text
Keywords: driver drowsiness detection; wearable ECG device; respiration characteristics driver drowsiness detection; wearable ECG device; respiration characteristics
Show Figures

Figure 1

MDPI and ACS Style

Kim, J.; Shin, M. Utilizing HRV-Derived Respiration Measures for Driver Drowsiness Detection. Electronics 2019, 8, 669.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop