WiDriver: Driver Activity Recognition System Based on WiFi CSI
Driver is the most active factor in people–vehicle–road system, so the driver activity monitoring has become increasingly important to support the driver assistant system application. The possibility of using device-free sensing technology for driver activity recognition in a simulated driving envir...
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| Published in | International journal of wireless information networks Vol. 25; no. 2; pp. 146 - 156 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.06.2018
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1068-9605 1572-8129 |
| DOI | 10.1007/s10776-018-0389-0 |
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| Abstract | Driver is the most active factor in people–vehicle–road system, so the driver activity monitoring has become increasingly important to support the driver assistant system application. The possibility of using device-free sensing technology for driver activity recognition in a simulated driving environment is investigated in this paper. We present WiDriver, among the first efforts to employ channel state information (CSI) amplitude variation data to intelligently estimate driving actions with commodity WiFi devices. The WiDriver proposes the scheme of screening sensitive input data from original CSI matrix of WiFi signals based on BP neural network algorithm; and the continuous driving activities classification algorithm by introducing the posture sequence, driving context finite automate model. Our experimental driving study in carriage with 5 subjects shows that the sensitive input selection scheme can achieve high accuracy of 96.8% in posture recognition and the continuous action classification algorithm can reach 90.76% maneuver operation detection rate. |
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| AbstractList | Driver is the most active factor in people–vehicle–road system, so the driver activity monitoring has become increasingly important to support the driver assistant system application. The possibility of using device-free sensing technology for driver activity recognition in a simulated driving environment is investigated in this paper. We present WiDriver, among the first efforts to employ channel state information (CSI) amplitude variation data to intelligently estimate driving actions with commodity WiFi devices. The WiDriver proposes the scheme of screening sensitive input data from original CSI matrix of WiFi signals based on BP neural network algorithm; and the continuous driving activities classification algorithm by introducing the posture sequence, driving context finite automate model. Our experimental driving study in carriage with 5 subjects shows that the sensitive input selection scheme can achieve high accuracy of 96.8% in posture recognition and the continuous action classification algorithm can reach 90.76% maneuver operation detection rate. |
| Author | He, Jie Yu, Tianqing Duan, Shihong |
| Author_xml | – sequence: 1 givenname: Shihong surname: Duan fullname: Duan, Shihong organization: School of Computer and Communication Engineering, University of Science and Technology Beijing – sequence: 2 givenname: Tianqing surname: Yu fullname: Yu, Tianqing organization: School of Computer and Communication Engineering, University of Science and Technology Beijing – sequence: 3 givenname: Jie surname: He fullname: He, Jie email: hejie@ustb.edu.cn organization: School of Computer and Communication Engineering, University of Science and Technology Beijing |
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| Cites_doi | 10.1145/2993422.2993579 10.1145/2789168.2790124 10.1109/CVPR.2016.211 10.1109/INFOCOM.2014.6847958 10.1145/1925861.1925870 10.1109/INFOCOM.2014.6847948 10.1145/2971648.2971744 10.1109/ITSC.2015.268 10.1109/GCCE.2016.7800363 10.1109/IVS.2014.6856610 10.1109/IVS.2005.1505176 10.1145/2639108.2639143 10.1109/TMC.2016.2517630 10.1109/UIC-ATC.2017.8397502 10.1145/2500423.2500436 10.1109/ICPR.2014.124 10.1109/MC.2017.7 10.1109/TMC.2015.2416186 10.1109/TMC.2015.2504935 10.1109/ITSC.2005.1520169 10.1109/JSEN.2013.2271721 10.1145/2789168.2790109 |
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Carlos Niebles, and B. Ghanem, Fast temporal activity proposals for efficient detection of human actions in untrimmed videos. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1914–1923, 2016. – reference: K. Ali, A. X. Liu, and W. Wang, et al. Keystroke recognition using wifi signals. In Proceedings of the 21st Annual International Conference on Mobile Computing and Networking, pages 90–102, 2015. ACM – reference: Y. Xie, Z. Li, and M. Li, Precise power delay profiling with commodity wifi. In Proceedings of the 21st Annual International Conference on Mobile Computing and Networking, pages 53–64, 2015. ACM – reference: Martin S, Ohn-Bar E, Tawari A, et al., Understanding head and hand activities and coordination in naturalistic driving videos. In Intelligent Vehicles Symposium Proceedings, 2014 IEEE, pages 884–889, 2014. IEEE – reference: Y. Wang, J. Liu, Y. Chen, M. Gruteser, J. Yang, and H. 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Trivedi Driver activity analysis for intelligent vehicles: issues and development framework. In Intelligent Vehicles Symposium, 2005. Proceedings. IEEE, pages 644–649, 2005. 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| Title | WiDriver: Driver Activity Recognition System Based on WiFi CSI |
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