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WiDriver: Driver Activity Recognition System Based on WiFi CSI

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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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References

  1. H. Veeraraghavan, S. Atev, N. Bird, et al., Driver activity monitoring through supervised and unsupervised learning. In Intelligent Transportation Systems, 2005, Proceedings. 2005 IEEE, 2005, pages 580–585. IEEE

  2. 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

  3. C. Braunagel, E. Kasneci, W. Stolzmann, et al., Driver-activity recognition in the context of conditionally autonomous driving. In Intelligent Transportation Systems (ITSC), 2015 IEEE 18th International Conference on. IEEE, pages 1652–1657, 2015.

  4. S. Park, M. Trivedi Driver activity analysis for intelligent vehicles: issues and development framework. In Intelligent Vehicles Symposium, 2005. Proceedings. IEEE, pages 644–649, 2005. IEEE

  5. Y. Geng, J. Chen, R. Fu, et al., Enlighten wearable physiological monitoring systems: on-body rf characteristics based human motion classification using a support vector machine, IEEE Transactions on Mobile Computing, Vol. 15, No. 3, pp. 656–671, 2016.

    Article  Google Scholar 

  6. F. Caba Heilbron, J. 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.

  7. J. He, Y. Geng, Y. Wan, et al., A cyber physical test-bed for virtualization of RF access environment for body sensor network, IEEE Sensors Journal, Vol. 13, No. 10, pp. 3826–3836, 2013.

    Article  Google Scholar 

  8. Q. Pu, S. Gupta, S. Gollakota, et al. Whole-home gesture recognition using wireless signals. In Proceedings of the 19th Annual International Conference on Mobile Computing and Networking, pages 27–38, 2013. ACM.

  9. F. Adib, Z. Kabelac, D. Katabi, and R. C. Miller, 3D tracking via body radio reflections. In Proceedings of the 11th USENIX Conference on Networked Systems Design and Implementation, Seattle, WA, USA, 2–4 April 2014; pages 317–329.

  10. D. Halperin, W. Hu, A. Sheth, et al., Tool release: gathering 802.11 n traces with channel state information, ACM SIGCOMM Computer Communication Review, Vol. 41, No. 1, pp. 53–53, 2011.

    Article  Google Scholar 

  11. 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

  12. C. Han, K. Wu, Y. Wang, and L. M. Ni, Wifall: Device-free fall detection by wireless networks. In Proceedings of the IEEE INFOCOM, Toronto, ON, Canada, 27 April–2 May 2014, pages 271–279.

  13. Y. Wang, J. Liu, Y. Chen, M. Gruteser, J. Yang, and H. Liu, E-eyes: Device-free location-oriented activity identification using fine-grained wifi signatures. In Proceedings of the 20th annual International Conference on Mobile Computing and Networking, Maui, HI, USA, 7–11 September 2014; pages 617–628, 2014.

  14. C. C Li, and S. H. Fang, Device-free human detection using WiFi signals. In Consumer Electronics, 2016 IEEE 5th Global Conference on, pages 1–3, 2016. IEEE

  15. W. Xi, J. Zhao, X. Y. Li, et al., Electronic frog eye: Counting crowd using wifi. In Infocom, 2014 proceedings IEEE, pages 361–369, 2014. ACM.

  16. G. Wang, Y. Zou, Z. Zhou, et al., We can hear you with wi-fi!, IEEE Transactions on Mobile Computing, Vol. 15, No. 11, pp. 2907–2920, 2016.

    Article  Google Scholar 

  17. 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

  18. H. Wang, D. Zhang, J. Ma, et al., Human respiration detection with commodity wifi devices: do user location and body orientation matter?. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pages 25–36, 2016. ACM

  19. X. Liu, J. Cao, S. Tang, et al., Contactless respiration monitoring via off-the-shelf Wifi devices, IEEE Transactions on Mobile Computing, Vol. 15, No. 10, pp. 2466–2479, 2016.

    Article  Google Scholar 

  20. C. Xu, J. He, X. Zhang, H. Cai, et.al., A Classification capability quantification method for human activity recognition. In 2017 IEEE Ubiquitous Intelligence and Computing (UIC 2017), Aug 4–8, San Francisco, USA, 2017

  21. E. Ohn-Bar, S. Martin, and A. Tawari A, Head, eye, and hand patterns for driver activity recognition. In Pattern Recognition (ICPR), 2014 22nd International Conference on, pages 660–665, 2014. IEEE.

  22. D. Zhang, H. Wang and D. Wu, Toward centimeter-scale human activity sensing with Wi-Fi signals, Computer, Vol. 50, No. 1, pp. 48–57, 2017.

    Article  Google Scholar 

  23. S. Palipana, P. Agrawal, and D. Pesch D, Channel state information based human presence detection using non-linear techniques. BuildSys@ SenSystem, pp. 177–186, 2016.

  24. Dataset of the WiDriver. http://pan.baidu.com/s/1geFSmlX.

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Acknowledgements

This work is supported by National Natural Science Foundation of China (NSFC) Project Nos. 61671056, 61302065, 61304257, 61402033, Beijing Natural Science Foundation Project No. 4152036 and the Fundamental Research Funds for the Central Universities No. FRF-TP-15-026A2.

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  1. School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China

    Shihong Duan, Tianqing Yu & Jie He

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  1. Shihong Duan
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  2. Tianqing Yu
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  3. Jie He
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Correspondence to Jie He.

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Duan, S., Yu, T. & He, J. WiDriver: Driver Activity Recognition System Based on WiFi CSI. Int J Wireless Inf Networks 25, 146–156 (2018). https://doi.org/10.1007/s10776-018-0389-0

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  • DOI: https://doi.org/10.1007/s10776-018-0389-0

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