WiDMove - um sensor de movimento direcional baseado em perturbações do sinal eletromagnético de interfaces 802.11
Abstract
The accurate detection of people in indoor environments requires high-cost devices, while low-cost devices, in addition to low accuracy, offers little information about the monitored events. The perturbations that can affect the electromagnetic signals used by 802.11 interfaces make this type of device a low-cost sensor, widely available, and enoughly accurate for several applications. In this work, we propose the WiDMove, a proposal to detect the entry and exit of people indoors using channel quality measurements (known as Channel State Information - CSI) offered by the IEEE 802.11n standard. Our proposal is based on signal processing and machine learning techniques, which allow us to extract and classify event signatures using the CSI. In lab tests with off-the-shelf 802.11 interfaces, we collected CSI samples that were affected by 8 different people. From this collected data we extracted the signature of the entry and exit events using some techniques such as Principal Component Analysis (PCA) and Short-Time Fourier Transform (STFT). We trained a Support Vector Machine (SVM) classifier and validated it with cross-validation, using the K-Fold and Leave-One-Out techniques. WiDMove presented that can reach an average accuracy above 85%.
References
Gjengset, J., Xiong, J., McPhillips, G., and Jamieson, K. (2014). Phaser: Enabling phased array signal processing on commodity wi access points. In Proceedings of the 20th annual international conference on Mobile computing and networking, pages 153–164. ACM.
Halperin, D., Hu, W., Sheth, A., and Wetherall, D. (2011). Tool release: Gathering 802.11n traces with channel state information. ACM SIGCOMM CCR, 41(1):53.
IEEE (1999). Ieee standard for telecommunications and information exchange between systems lan/man specic requirements part 11: Wireless medium access control (mac) and physical layer (phy) specications: High speed physical layer in the 5 ghz band. IEEE Std 802.11a-1999, pages 1–102.
IEEE (2009). Ieee standard for information technology– local and metropolitan area networks– specic requirements– part 11: Wireless lan medium access control (mac)and physical layer (phy) specications amendment 5: Enhancements for higher throughput. IEEE Std 802.11n-2009, pages 1–565.
Li, Y. and Zhu, T. (2016). Using wi- signals to characterize human gait for identication and activity monitoring. In Connected Health: Applications, Systems and Engineering Technologies (CHASE), 2016 IEEE First International Conference on, pages 238–247. IEEE.
Proakis, J. G., Salehi, M., Zhou, N., and Li, X. (1994). Communication systems engineering, volume 2. Prentice Hall New Jersey.
Van Dorp, P. and Groen, F. (2008). Feature-based human motion parameter estimation with radar. IET Radar, Sonar & Navigation, 2(2):135–145.
Wang, W., Liu, A. X., and Shahzad, M. (2016). Gait recognition using wi signals. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pages 363–373. ACM.
Wang, W., Liu, A. X., Shahzad, M., Ling, K., and Lu, S. (2015). Understanding and modeling of wi signal based human activity recognition. In Proceedings of the 21st annual international conference on mobile computing and networking, pages 65–76. ACM.
Xie, Y., Li, Z., and Li, M. (2015). Precise power delay proling with commodity wi. In Proceedings of the 21st Annual International Conference on Mobile Computing and Networking, MobiCom ’15, pages 53–64, New York, NY, USA. ACM.
Zhang, J., Wei, B., Hu, W., and Kanhere, S. S. (2016). Wi-id: Human identication using wi signal. In Distributed Computing in Sensor Systems (DCOSS), 2016 International Conference on, pages 75–82. IEEE.
