dWatch: A Reliable and Low-Power Drowsiness Detection System for Drivers Based on Mobile Devices

被引:7
作者
Xing, Tianzhang [1 ]
Wang, Qing [2 ]
Wu, Chase Q. [3 ]
Xi, Wei [4 ]
Chen, Xiaojiang [1 ]
机构
[1] Northwest Univ, Shaanxi Int Joint Res Ctr Battery Free Internet T, Sch Informat Sci & Technol, North Taibai Ave, Xian, Peoples R China
[2] Northwest Univ, Sch Informat Sci & Technol, North Taibai Ave, Xian, Peoples R China
[3] New Jersey Inst Technol, Dept Comp Sci, Newark, NJ 07102 USA
[4] Xi An Jiao Tong Univ, Dept Comp Sci & Technol, Xianning West Rd, Xian, Peoples R China
基金
中国博士后科学基金;
关键词
Mobile computing; sensors; drowsiness detection; heart rate variability; smart watches;
D O I
10.1145/3407899
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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%.
引用
收藏
页数:22
相关论文
共 33 条
  • [1] Abe E, 2014, ASIAPAC SIGN INFO PR
  • [2] Energy-Harvesting Wireless Sensor Networks (EH-WSNs): A Review
    Adu-Manu, Kofi Sarpong
    Adam, Nadir
    Tapparello, Cristiano
    Ayatollahi, Hoda
    Heinzelman, Wendi
    [J]. ACM TRANSACTIONS ON SENSOR NETWORKS, 2018, 14 (02)
  • [3] [Anonymous], 2014, IEEE SYS MAN CYBERN
  • [4] [Anonymous], 2001, INT S MEDICAL DATA A
  • [5] A Hybrid Approach to Detect Driver Drowsiness Utilizing Physiological Signals to Improve System Performance and Wearability
    Awais, Muhammad
    Badruddin, Nasreen
    Drieberg, Micheal
    [J]. SENSORS, 2017, 17 (09)
  • [6] FLUCTUATIONS IN AUTONOMIC NERVOUS ACTIVITY DURING SLEEP DISPLAYED BY POWER SPECTRUM ANALYSIS OF HEART-RATE-VARIABILITY
    BAHARAV, A
    KOTAGAL, S
    GIBBONS, V
    RUBIN, BK
    PRATT, G
    KARIN, J
    AKSELROD, S
    [J]. NEUROLOGY, 1995, 45 (06) : 1183 - 1187
  • [7] Chang YL, 2016, IEEE ENG MED BIO, P4849, DOI 10.1109/EMBC.2016.7591813
  • [8] Chongguang Bi, 2017, 2017 IEEE/ACM Second International Conference on Internet-of-Things Design and Implementation (IoTDI), P223, DOI 10.1145/3054977.3054979
  • [9] Corey Timothy P, 2011, Front Evol Neurosci, V3, P7, DOI 10.3389/fnevo.2011.00007
  • [10] Danisman T., 2010, Proceedings International Conference on Machine and Web Intelligence (ICMWI 2010), P230, DOI 10.1109/ICMWI.2010.5648121