TY - JOUR
T1 - Dwatch
T2 - A reliable and low-power drowsiness detection system for drivers based on mobile devices
AU - Xing, Tianzhang
AU - Wang, Qing
AU - Wu, Chase Q.
AU - Xi, Wei
AU - Chen, Xiaojiang
N1 - Publisher Copyright:
© 2020 Association for Computing Machinery.
PY - 2020/10
Y1 - 2020/10
N2 - Drowsiness detection is critical to driver safety, considering thousands of deaths caused by drowsy driving annually. Professional equipment is capable of providing high detection accuracy, but the high cost limits their applications in practice. The use of mobile devices such as smart watches and smart phones holds the promise of providing a more convenient, practical, non-invasive method for drowsiness detection. In this article, we propose a real-time driver drowsiness detection system based on mobile devices, referred to as dWatch, which combines physiological measurements with motion states of a driver to achieve high detection accuracy and low power consumption. Specifically, based on heart rate measurements, we design different methods for calculating heart rate variability (HRV) and sensing yawn actions, respectively, which are combined with steering wheel motion features extracted from motion sensors for drowsiness detection. We also design a driving posture detection algorithm to control the operation of the heart rate sensor to reduce system power consumption. Extensive experimental results show that the proposed system achieves a detection accuracy up to 97.1% and reduces energy consumption by 33%.
AB - Drowsiness detection is critical to driver safety, considering thousands of deaths caused by drowsy driving annually. Professional equipment is capable of providing high detection accuracy, but the high cost limits their applications in practice. The use of mobile devices such as smart watches and smart phones holds the promise of providing a more convenient, practical, non-invasive method for drowsiness detection. In this article, we propose a real-time driver drowsiness detection system based on mobile devices, referred to as dWatch, which combines physiological measurements with motion states of a driver to achieve high detection accuracy and low power consumption. Specifically, based on heart rate measurements, we design different methods for calculating heart rate variability (HRV) and sensing yawn actions, respectively, which are combined with steering wheel motion features extracted from motion sensors for drowsiness detection. We also design a driving posture detection algorithm to control the operation of the heart rate sensor to reduce system power consumption. Extensive experimental results show that the proposed system achieves a detection accuracy up to 97.1% and reduces energy consumption by 33%.
KW - Drowsiness detection
KW - Heart rate variability
KW - Mobile computing
KW - Sensors
KW - Smart watches
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U2 - 10.1145/3407899
DO - 10.1145/3407899
M3 - Article
AN - SCOPUS:85092784966
SN - 1550-4859
VL - 16
JO - ACM Transactions on Sensor Networks
JF - ACM Transactions on Sensor Networks
IS - 4
M1 - 37
ER -