Abstract
Based on the Wi-Fi widely separated in the world, Wi-Fi-based wireless activity recognition has attracted more and more research efforts. Now, device-based activity awareness is being used for commercial purpose as the most important solution. Such devices based on various acceleration sensors and direction sensor are very mature at present. With more and more profound understanding of wireless signals, commercial wireless routers are used to obtain signal information of the physical layer: channel state information (CSI) more granular than the RSSI signal information provides a theoretical basis for wireless signal perception. Through research on activity recognition techniques based on CSI of wireless signal and deep learning, the authors proposed a system for learning classification using deep learning, mainly including a data preprocessing stage, an activity detection stage, a learning stage and a classification stage. During the activity detection model stage, a correlation-based model was used to detect the time of the activity occurrence and the activity time interval, thus solving the problem that the waveform changes due to variable environment at stable time. During the activity recognition stage, the network was studied by innovative deep learning to conduct training for activity learning. By replacing the fingerprint way, which is used broadly today, with learning the CSI signal information of activities, we classified the activities through trained network.
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References
Wang, Y., Liu, J., Chen, Y., et al.: E-eyes: device-free location-oriented activity recognition using fine-grained WiFi signatures. In: Proceedings of the 20th Annual International Conference on Mobile Computing and Networking, pp. 617–628. ACM (2014)
Pu, Q., Gupta, S., Gollakota, S., et al.: Whole-home gesture recognition using wireless signals. In: Proceedings of the 19th Annual International Conference on Mobile Computing & Networking, pp. 27–38. ACM (2013)
Zheng, X., Wang, J., Shangguan, L., et al.: Smokey: ubiquitous smoking detection with commercial WiFi infrastructures. In: IEEE INFOCOM 2016-The 35th Annual IEEE International Conference on Computer Communications, pp. 1–9. IEEE (2016)
Ren, Z., Meng, J., Yuan, J., et al.: Robust hand gesture recognition with kinect sensor. In: Proceedings of the 19th ACM International Conference on Multimedia, pp. 759–760. ACM (2011)
Weichert, F., Bachmann, D., Rudak, B., et al.: Analysis of the accuracy and robustness of the leap motion controller. Sensors 13(5), 6380–6393 (2013)
IEEE Std. 802.11n-2009: Enhancements for higher throughput (2009). http://www.ieee802.org
Silver, D., Veness, J.: Monte-Carlo planning in large POMDPs. In: Advances in Neural Information Processing Systems, pp. 2164–2172 (2010)
Adib, F., Kabelac, Z., Katabi, D., et al.: 3D tracking via body radio reflections. In: NSDI, vol. 14, pp. 317–329 (2014)
Gollakota, S., Hassanieh, H., Ransford, B., et al.: They can hear your heartbeats: noninvasive security for implantable medical devices. ACM SIGCOMM Comput. Commun. Rev. 41(4), 2–13 (2011)
Asadzadeh, P., Kulik, L., Tanin, E.: Gesture recognition using RFID technology. Pers. Ubiquit. Comput. 16(3), 225–234 (2012)
Tongrod, N., Lokavee, S., Kerdcharoen, T., et al.: Gestural system based on multifunctional sensors and ZigBee networks for squad communication. In: 2011 Defense Science Research Conference and Expo (DSR), pp. 1–4. IEEE, 2011
Wang, Y., Wu, K., Ni, L.M.: Wifall: device-free fall detection by wireless networks. IEEE Trans. Mob. Comput. 16, 581–594 (2016)
Molchanov, P., Gupta, S., Kim, K., et al.: Short-range FMCW monopulse radar for hand-gesture sensing. In: 2015 IEEE Radar Conference (RadarCon), pp. 1491–1496. IEEE (2015)
Wang, G., Zou, Y., Zhou, Z., et al.: We can hear you with Wi-Fi! IEEE Trans. Mob. Comput. 15(11), 2907–2920 (2016)
Adib, F., Kabelac, Z., Katabi, D.: Multi-person localization via RF body reflections. In: NSDI, pp. 279–292 (2015)
Xie, Y., Li, Z., Li, M.: Precise power delay profiling with commodity Wi-Fi. In: Proceedings of the 21st Annual International Conference on Mobile Computing and Networking, pp. 53–64. ACM (2015)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Kleisouris, K., Firner, B., Howard, R., Zhang, Y., Martin, R.P.: Detecting intra-room mobility with signal strength descriptors. In: ACM MobiHoc (2010)
Lei, J., Ren, X., Fox, D.: Fine-grained kitchen activity recognition using RGB-D. In: ACM UbiComp (2012)
Keally, M., et al.: PBN: towards practical activity recognition using smartphone based body sensor networks. In: ACM SenSys (2011)
Adib, F., Katabi, D.: See through walls with WiFi! In: ACM SIGCOMM (2013)
Yatani, K., Truong, K.N.: BodyScope: a wearable acoustic sensor for activity recognition. In: Proceedings of the ACM UbiComp (2012)
Halperin, D., et al.: Tool release: gathering 802.11n traces with channel state information. ACM SIGCOMM CCR 41(1), 1 (2011)
Xia, P., Zhou, S., Giannakis, G.B.: Adaptive MIMO-OFDM based on partial channel state information. IEEE Trans. Signal Process. 52(1), 202–213 (2004)
Hong, J., Ohtsuki, T.: Ambient intelligence sensing using array sensor: device-free radio based approach. In: CoSDEO Workshop (2013)
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© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Wang, Z. et al. (2020). Activity Recognition and Classification via Deep Neural Networks. In: Gao, H., Li, K., Yang, X., Yin, Y. (eds) Testbeds and Research Infrastructures for the Development of Networks and Communications. TridentCom 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 309. Springer, Cham. https://doi.org/10.1007/978-3-030-43215-7_15
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DOI: https://doi.org/10.1007/978-3-030-43215-7_15
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