Computer Science ›› 2019, Vol. 46 ›› Issue (12): 266-271.doi: 10.11896/jsjkx.190200349

• Graphics ,Image & Pattern Recognition • Previous Articles     Next Articles

Non-cooperative Human Behavior Recognition Method Based on CSI

LI Xiao-wei, YU Jiang, CHANG Jun, YANG Jin-peng, RAN Ya-xin   

  1. (School of Information Science and Engineering,Yunnan University,Kunming 650500,China)
  • Received:2019-02-23 Online:2019-12-15 Published:2019-12-17

Abstract: Currently,Wi-Fi-based wireless personnel perception technology is widely used in anti-intrusion security monitoring,human health care,gait recognition and other fields,regarding this,this paper proposed a non-cooperative human behavior recognition method.The channel state information (CSI) of Wi-Fi signals can be used to recognize five dynamic activities:walking,sitting-standing up,squatting,jumping and falling.The method uses a SIMO system to collect CSI data,and after performing pre-processing on the CSI amplitude and phase respectively,implements a three-step computational cost reduction mechanism:subcarrier fusion,rejection of bad data link based on mobile variance threshold,and data segmentation of dynamic time window based on wavelet transform.Then activity features are extracted and extended from the time domain to the frequency domain.By analyzing the characteristics of the Doppler power spectrum,the utilization of the CSI signal is improved.Experiment results show that the overall recognition rate increases with the use of feature dimensions.Optimized by two rounds of voting,the combined classifier weighted voting method is increasing the overall recognition rate of five dynamic activities to 90.3%.And compared to RSSI,the advantages of CSI in the field of human behavior recognition are more prominent.

Key words: CSI, Dopplerpower spectrum, Non-cooperative, Wavelet analysis

CLC Number: 

  • TP391.4
[1]WANG L.Research on human behavior recognition technology based on wearable sensor network[D].Nanjing:Nanjing University,2014.
[2]WU C S,YANG Z,ZHOU Z M,et al.Non-invasive detection of moving and stationary human with Wi-Fi[J].IEEE Journal on Selected Areas in Communications,2015,33(11):2329-2342.
[3]QIAN K,WU C,YANG Z,et al.Enabling Contactless Detection of Moving Humans with Dynamic Speeds Using CSI[J].ACM Transactions on Embedded Computing Systems,2018,17(2):1-18.
[4]ZEHUA D,FANGMIN L,JULANG Y,et al.Indoor Motion Detection Using Wi-Fi Channel State Information in Flat Floor Environments Versus in Staircase Environments[J].Sensors,2018,18(7):2177.
[5]LIU M G,ZHANG L,YANG P L,et al.Wi-Run:Device-free step estimation system with commodity Wi-Fi[J].Journal of Network and Computer Applications,2019,143(1):77-88.
[6]YOUSSEF M,MAH M.Challenges:Device-free Passive Localization for Wireless[C]//Acm International Conference on Mobile Computing & Networking.Montréal,Québec,Canada:ACM,2007.
[7]SIGG S,SCHOLZ M,SHI S,et al.RF-Sensing of Activities from Non-Cooperative Subjects in Device-Free Recognition Systems Using Ambient and Local Signals[J].IEEE Transactions on Mobile Computing,2014,13(4):907-920.
[8]YANG Z,ZHOU Z,LIU Y.From RSSI to CSI:Indoor Localization via Channel Response[J].ACM Computing Surveys,2013,46(2):1-32.
[9]YANG Z,LIU Y H.Wi-Fi Radar:From RSSI to CSI[J].Chinese Computer Society,2014,10(11):55-60.
[10]TIAN X H,ZHU S J,XIONG S J,et al.Performance Analysis of Wi-Fi Indoor Localization with Channel State Information [J].IEEE Transactions on Mobile Computing,2019,18(8):1870-1884.
[11]ZHANG Y,LI D P,WANG Y J.An Indoor Passive Positioning Method Using CSI Fingerprint Based on Adaboost [J].IEEE Sensors Journal,2019,19(14):5792-5800.
[12]XIN T,GUO B,WANG Z,et al.FreeSense:human-behavior understanding using Wi-Fi signals [J].Journal of Ambient Intelligence and Humanized Computing,2018,9(5):1611-1622.
[13]LI W D,TAN B,PIECHOCKI R J.Wi-Fi based passive sensing system for human presence and activity event classification [J].IET Wireless Sensor Systems,2018,8(6):276-283.
[14]LI L X.Research and Design of Personnel Perception and Intrusion Detection Technology Based on WLAN[D].Beijing:Beijing University of Posts and Telecommunications,2018.
[15]XIAO L,PAN H.Human motion recognition system based on Wi-Fi signal[J].Journal of Beijing University of Posts and Telecommunications,2018,41(3):119-124.
[16]WANG T,YANG D D,ZHANG S Q,et al.Wi-Alarm:Low-Cost Passive Intrusion DetectionUsing Wi-Fi[J].Sensors,2019,19(10):2335.
[17]WU K,XIAO J,YI Y,et al.FILA:Fine-grained indoor localization[C]//Proceedings of IEEE INFOCOM.2012:2210-2218.
[18]WANG W,LIU A X,SHAHZAD M,et al.Device-Free Human Activity Recognition Using Commercial Wi-Fi Devices[J].IEEE Journal on Selected Areas in Communications,2017,35(5):1118-1131.
[19]LI Y H,CHEN B.A Parameter-Independent Access Point Location Method Based on CSI[J].Computer Science,2017,44(12):74-77.
[20]GENG Y,CHEN J,FU R,et al.Enlighten Wearable Physiological Monitoring Systems:On-body RF Characteristics Based Human Motion Classification Using a Support Vector Machine[J].IEEE Transactions on Mobile Computing,2016,15(3):656-671.
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