TY - GEN
T1 - Low-power listen based driver drowsiness detection system using smartwatch
AU - Zhang, Shiyuan
AU - He, Hui
AU - Wang, Zhi
AU - Gao, Mingze
AU - Mao, Jinsong
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2018.
PY - 2018
Y1 - 2018
N2 - Drowsy driving is a major cause of car accidents, because drivers are unable to swiftly perceive, process, and respond to the varying road conditions. Existing detecting solutions includes checking eye-blink, monitoring heartbeat with EEG or ECG device support, and analyzing the way the driver steers the steering wheel. Though effective, these solutions require extra hardware which causes distraction and inconvenience to the driver. We design and implement an unobtrusive and energy-efficient driver drowsiness detection system using only a commercial smartwatch through monitoring the steering behavior and heart rate of the driver. The system comprises two major modules, a hand state monitor and a drowsiness detector. The system is built by following insights. First, when the hand wearing smartwatch is off the steering wheel, no validate steering data will be captured. Thus it’s necessary to detect whether the hand is on the steering wheel to ensure the validity of the steering motion data. Second, heart rate features can reflect the alert level of the driver, and it can work no matter whether the hand is on the steering wheel. Consequently, we adopt the heart rate sensor of the smartwatch as a supplementary indicator of driver’s drowsiness level. Meanwhile, power consumption is considered given the limited smartwatch battery power. We evaluate our drowsiness detection system using a driving simulator, and it achieves an accuracy of 94.39%.
AB - Drowsy driving is a major cause of car accidents, because drivers are unable to swiftly perceive, process, and respond to the varying road conditions. Existing detecting solutions includes checking eye-blink, monitoring heartbeat with EEG or ECG device support, and analyzing the way the driver steers the steering wheel. Though effective, these solutions require extra hardware which causes distraction and inconvenience to the driver. We design and implement an unobtrusive and energy-efficient driver drowsiness detection system using only a commercial smartwatch through monitoring the steering behavior and heart rate of the driver. The system comprises two major modules, a hand state monitor and a drowsiness detector. The system is built by following insights. First, when the hand wearing smartwatch is off the steering wheel, no validate steering data will be captured. Thus it’s necessary to detect whether the hand is on the steering wheel to ensure the validity of the steering motion data. Second, heart rate features can reflect the alert level of the driver, and it can work no matter whether the hand is on the steering wheel. Consequently, we adopt the heart rate sensor of the smartwatch as a supplementary indicator of driver’s drowsiness level. Meanwhile, power consumption is considered given the limited smartwatch battery power. We evaluate our drowsiness detection system using a driving simulator, and it achieves an accuracy of 94.39%.
KW - Drowsiness detection
KW - Hand on/off steering wheel detection
KW - Smartwatch built-in sensors
KW - SVM
UR - https://www.scopus.com/pages/publications/85054824652
U2 - 10.1007/978-3-030-00018-9_40
DO - 10.1007/978-3-030-00018-9_40
M3 - 会议稿件
AN - SCOPUS:85054824652
SN - 9783030000172
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 453
EP - 464
BT - Cloud Computing and Security - 4th International Conference, ICCCS 2018, Revised Selected Papers
A2 - Sun, Xingming
A2 - Pan, Zhaoqing
A2 - Bertino, Elisa
PB - Springer Verlag
T2 - 4th International Conference on Cloud Computing and Security, ICCCS 2018
Y2 - 8 June 2018 through 10 June 2018
ER -