Abstract
Monitoring school violence is critical for the prevention of juvenile delinquency and promotion of social harmony. Pioneering approaches employ always-on-body sensors or cameras with limited surveillance area, which cannot provide ubiquitous violence monitoring. In this paper, we present Wi-Dog, a non-invasive physical violence monitoring scheme based on commodity WiFi infrastructures. The key intuition is that violence-induced WiFi signals convey informative characteristics of intensity, irregularity and continuity. To identify school violence from violence-alike actions (e.g., jump, lie down and run), we develop a precise noise reduction method by selecting sensitive antenna pair and subcarriers. Moreover, a wavelet-entropy-based segmentation method is proposed to detect movement transitions in the distance, and the complete local-global analysis is further adopted to improve overall performance. We implemented Wi-Dog using commercial WiFi devices and evaluated it in real indoor environments. Experimental results demonstrate the effectiveness of Wi-Dog with average detection accuracy of 0.9.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2(3), 27 (2011)
Datta, A., Shah, M., Lobo, N.D.V.: Person-on-person violence detection in video data. In: Proceedings of 16th International Conference on Pattern Recognition, vol. 1, pp. 433–438. IEEE (2002)
Deniz, O., Serrano, I., Bueno, G., Kim, T.K.: Fast violence detection in video. In: 2014 International Conference on Computer Vision Theory and Applications (VISAPP), vol. 2, pp. 478–485. IEEE (2014)
Ding, H., Shangguan, L., Yang, Z., Han, J., Zhou, Z., Yang, P., Xi, W., Zhao, J.: Femo: a platform for free-weight exercise monitoring with rfids. In: Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems, pp. 141–154. ACM (2015)
Evans, C.B., Fraser, M.W., Cotter, K.L.: The effectiveness of school-based bullying prevention programs: a systematic review. Aggress. Violent Behav. 19(5), 532–544 (2014)
Halperin, D., Hu, W., Sheth, A., Wetherall, D.: Tool release: gathering 802.11 n traces with channel state information. ACM SIGCOMM Comput. Commun. Rev. 41(1), 53–53 (2011)
Mateo, A., Roberto, H., Daniel, A., Daniel, A.: Interpretation of the lempel-ziv complexity measure in the context of biomedical signal analysis. IEEE Trans. Bio-med. Eng. 53(11), 2282–2288 (2006)
Nelson, A., Schmandt, J., Shyamkumar, P., Wilkins, W., Lachut, D., Banerjee, N., Rollins, S., Parkerson, J., Varadan, V.: Wearable multi-sensor gesture recognition for paralysis patients. In: 2013 IEEE SENSORS, pp. 1–4. IEEE (2013)
Qian, K., Wu, C., Yang, Z., Yang, C., Liu, Y.: Decimeter level passive tracking with wifi. In: Proceedings of the 3rd Workshop on Hot Topics in Wireless, pp. 44–48. ACM (2016)
Sun, Z., Tang, S., Huang, H., Huang, L., Zhu, Z., Guo, H., Sun, Y.: iProtect: detecting physical assault using smartphone. In: Xu, K., Zhu, H. (eds.) WASA 2015. LNCS, vol. 9204, pp. 477–486. Springer, Cham (2015). doi:10.1007/978-3-319-21837-3_47
Tan, S., Yang, J.: WiFinger: leveraging commodity WiFi for fine-grained finger gesture recognition. In: Proceedings of the 17th ACM International Symposium on Mobile Ad Hoc Networking and Computing, pp. 201–210. ACM (2016)
Wang, H., Zhang, D., Ma, J., Wang, Y., Wang, Y., Wu, D., Gu, T., Xie, B.: 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, pp. 25–36. ACM (2016)
Wang, H., Zhang, D., Wang, Y., Ma, J., Wang, Y., Li, S.: RT-Fall: a real-time and contactless fall detection system with commodity wifi devices. IEEE Trans. Mob. Comput. 16(2), 511–526 (2016)
Wang, J., Jiang, H., Xiong, J., Jamieson, K., Chen, X., Fang, D., Xie, B.: LIFS: low human effort, device-free localization with fine-grained subcarrier information. In: Proceedings of the 22nd Annual International Conference on Mobile Computing and Networking, pp. 243–256. ACM (2016)
Wang, W., Liu, A.X., Shahzad, M.: Gait recognition using wifi signals. In: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 363–373. ACM (2016)
Wang, W., Liu, A.X., Shahzad, M., Ling, K., Lu, S.: Understanding and modeling of wifi signal based human activity recognition. In: Proceedings of the 21st Annual International Conference on Mobile Computing and Networking, pp. 65–76. ACM (2015)
Wang, Y., Li, W., Zhou, J., Li, X., Pu, Y.: Identification of the normal and abnormal heart sounds using wavelet-time entropy features based on OMS-WPD. Future Gen. Comput. Syst. 37, 488–495 (2014)
Wang, Y., Yu, X., Zhang, Y., Lv, H., Jiao, T., Lu, G., Li, Z., Li, S., Jing, X., Wang, J.: Detecting and monitoring the micro-motions of trapped people hidden by obstacles based on wavelet entropy with low centre-frequency UWB radar. Int. J. Remote Sens. 36(5), 1349–1366 (2015)
Wu, F., Zhao, H., Zhao, Y., Zhong, H.: Development of a wearable-sensor-based fall detection system. Int. J. Telemed. Appl. 2015, 2 (2015)
Zhang, T., Jia, W., Yang, B., Yang, J., He, X., Zheng, Z.: Mowld: a robust motion image descriptor for violence detection. Multimedia Tools Appl. 76(1), 1419–1438 (2017)
Zhou, Z., Yang, Z., Qian, K., Wu, C., Shangguan, L., Xu, H., et al.: Tracking synchronous gestures with wifi. In: The 25th International Conference on Computer Communication and Networks, Waikoloa, Hawaii, USA (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Zhou, Q., Wu, C., Xing, J., Li, J., Yang, Z., Yang, Q. (2017). Wi-Dog: Monitoring School Violence with Commodity WiFi Devices. In: Ma, L., Khreishah, A., Zhang, Y., Yan, M. (eds) Wireless Algorithms, Systems, and Applications. WASA 2017. Lecture Notes in Computer Science(), vol 10251. Springer, Cham. https://doi.org/10.1007/978-3-319-60033-8_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-60033-8_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-60032-1
Online ISBN: 978-3-319-60033-8
eBook Packages: Computer ScienceComputer Science (R0)