Walking Recognition and Parameters Estimation Based on Wi-Fi Channel State Information
Author:
Affiliation:

Clc Number:

TP18

Fund Project:

National Key Research and Development Project of China (2016YFB1001200)

  • Article
  • | |
  • Metrics
  • |
  • Reference [43]
  • |
  • Related [20]
  • | | |
  • Comments
    Abstract:

    As one of the common daily behaviors, walking could reveal much important information, such as one's identity and health condition. Fine-grained walking information such as walking velocity, walking direction, the number of steps, and stride length could provide important references for indoor tracking, gait analysis, elder care, and other context-aware situation applications. Thus, the perception of human walking utilizing the environmental Wi-Fi signal has been widely concerned by researchers in recent years. In order to achieve the perception of human walking, current methods usually need to gather a lot of walking data and then extract signal feature from extensive data through empirical observation or off-line training. However, due to the lack of theoretical instruction, the extracted signal feature is indirect and often contains redundant information of environment and sensing target. Therefore, as long as there is a change of the environment or sensing target, these systems have to regather data and relearn the signal feature for new situation. This would cause difficulties when applied in real life with varied wireless environment. Different from these works, this study has achieved the walking recognization in daily continuous activities without any learning requirement. Moreover, the fine-grained parameters such as walking velocity, walking direction, the number of steps, and stride length have been estimated in order to provide crucial context for upper layer context-aware applications. Specially, by analyzing the relationship between channel state information (CSI) and Doppler effect introduced by human movement, a Doppler velocity model is firstly established revealing that the theoretical relationship between human movement and CSI variation. Then by utilizing the MUSIC algorithm, the Doppler velocity could be obtained from Wi-Fi CSI which serves as an effective signal feature in revealing human movement and unrelated to the environment and human target. Finally, by studying the relationship between Doppler velocity and real human walking velocity, walking behavior as well as estimating fine-grained walking parameters could be recognized. Through extensive experiments done by different volunteers in different environments, the results have demonstrated the accuracy and robustness of the system. The system achieves an accuracy of 95.5% in walking recognition, a relative median error of 12.2% in walking velocity estimation, a median error of 9° in walking direction estimation, an accuracy of 90% in step counting and a median error of 0.12m in stride length estimation.

    Reference
    [1] Hardegger M, Roggen D, Troster G. 3D ActionSLAM:Wearable person tracking in multi-floor environments. Personal and Ubiquitous Computing, 2015,19(1):123-141. http://doi.org/10.1007/s00779-014-0815-y
    [2] Yun X, Calusdian J, Bachmann ER, McGhee RB. Estimation of human foot motion during normal walking using inertial and magnetic sensor measurements. IEEE Tran. on Instrumentation & Measurement, 2012,61(7):2059-2072. http://doi.org/10.1109/TIM.2011.2179830
    [3] Hamilton JM, Joyce BS, Kasarda ME, Tarazaga PA. Characterization of human motion through floor vibration. In:Proc. of the 32nd IMAC Dynamics of Civil Structures. Cham:Springer-Verlag, 2014.63-170. http://doi.org/10.1007/978-3-319-04546-7__19
    [4] Schuller B, Pokorny F, Ladsatter S, Fellner M, Graf F, Paletta L. Acoustic geo-sensing:Recognising cyclists' route, route direction, and route progress from cell-phone audio. In:Proc. of the Int'l Conf. on Acoustics, Speech and Signal Processing. Vancouver:IEEE, 2013.453-457. http://doi.org/10.1109/ICASSP.2013.6637688
    [5] Eldib M, Deboeverie F, Philips W, Aghajan H. Behavior analysis for elderly care using a network of low-resolution visual sensors. Journal of Electronic Imaging, 2016,25(4):1-17. https://doi.org/10.1117/1.JEI.25.4.041003
    [6] Ohnishi K, Kanehira A, Kanezaki A, Harada T. Recognizing activities of daily living with a wrist-mounted camera. In:Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition. Las Vegas:IEEE, 2016.3103-3111. https://doi.org/10.1109/CVPR.2016.338
    [7] Wang Y, Liu J, Chen Y, Gruteser M, Yang J, Liu HB. E-Eyes:Device-free location-oriented activity identification using fine-grained WiFi signatures. In:Proc. of the 20th Annual Int'l Conf. on Mobile Computing and Networking. Hawaii:ACM, 2014.617-628. https://doi.org/10.1145/2639108.2639143
    [8] Wange W, Liu AX, Shahzad M, Ling K, Lu S. Understanding and modeling of Wi-Fi signal based human activity recognition. In:Proc. of the 21st Annual Int'l Conf. on Mobile Computing and Networking. Paris:ACM, 2015.65-76. https://doi.org/10.1145/2789168.2790093
    [9] Wang W, Liu AX, Shahzad M. Gait recognition using WiFi signals. In:Proc. of the 2016 ACM Int'l Joint Conf. on Pervasive and Ubiquitous Computing. Heidelberg:ACM, 2016.363-373. https://doi.org/10.1145/2971648.2971670
    [10] Zeng Y, Pathak PH, Mohapatra P. WiWho:WiFi-based person identification in smart spaces. In:Proc. of the 15th Int'l Conf. on Information Processing in Sensor Networks. Vienna:IEEE, 2016.1-12. https://doi.org/10.1109/IPSN.2016.7460727
    [11] Wu D, Zhang DQ, Xu CR, Wang YS, Wang H. WiDir:Walking direction estimation using wireless signals. In:Proc. of the 2016 ACM Int'l Joint Conf. on Pervasive and Ubiquitous Computing. Heidelberg:ACM, 2016.351-362. https://doi.org/10.1145/2971648.2971658
    [12] Xu Y, Yang W, Wang JX, Zhou X, Li H, H LS. WiStep:Device-free step counting with WiFi signals. In:Proc of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies. New York:ACM, 2018.1-23. https://doi.org/10.1145/3178157
    [13] Hsu CY, Liu YC, Kabelac Z, Hristov R, Katabi D, Liu C. Extracting gait velocity and stride length from surrounding radio signals. In:Proc.of the 2017 CHI Conf. on Human Factors in Computing Systems. Denver:ACM, 2017.2116-2126. https://doi.org/10.1145/3025453.3025937
    [14] Li SJ, Li X, Niu K, Wang H, Zhang Y, Zhang DQ. AR-alarm:An adaptive and robust intrusion detection system leveraging CSI from commodity Wi-Fi. In:Proc. of the 2017 Int'l Conf. on Smart Homes and Health Telematics. Cham:Springer-Verlag, 2017.211-223. https://doi.org/10.1007/978-3-319-66188-9_18
    [15] Xiao J, Wu KS, Yi YW, Wang L, Ni LM. FIMD:Fine-grained device-free motion detection. In:Proc. of the 18th Int'l Conf. on Parallel and Distributed Systems. Singapore:IEEE, 2012.229-235. https://doi.org/10.1109/ICPADS.2012.40
    [16] Qian K, Wu CS, Yang Z, Liu YH, Zhou ZM. PADS:Passive detection of moving targets with dynamic speed using PHY layer information. In:Proc. of the 18th Int'l Conf. on Parallel and Distributed Systems. IEEE, 2014.1-8. https://doi.org/10.1109/PADSW.2014.7097784
    [17] Wu CS, Yang Z, Zhou ZM, Liu XF, Liu YH, Cao JN. Non-invasive detection of moving and stationary human with Wi-Fi. IEEE Journal on Selected Areas in Communications, 2015,33(11):2329-2342. https://doi.org/10.1109/JSAC.2015.2430294
    [18] Wang J, Jiang HB, Xiong J, Jamieson K, Chen XJ, Fang DY, Xie BB. LiFS:Low human-effort, device-free localization with fine-grained subcarrier information. In:Proc. of the 22nd Annual Int'l Conf. on Mobile Computing and Networking. New York:ACM, 2016.243-256. https://doi.org/10.1145/2973750.2973776
    [19] Li X, Li SJ, Zhang DQ, Xiong J, Wang YS, Mei H. Dynamic-MUSIC:Accurate device-free indoor localization. In:Proc. of the 2016 ACM Int'l Joint Conf. on Pervasive and Ubiquitous Computing. New York:ACM, 2016.196-207. https://doi.org/10.1145/2971648.2971665
    [20] Li X, Zhang DQ, Lv Q, Xiong J, Li SJ, Zhang Y, Mei H. IndoTrack:Device-free indoor human tracking with commodity Wi-Fi. In:Proc. of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies. New York:ACM, 2017.1-22. https://doi.org/10.1145/3130940
    [21] Qian K, Wu CS, Yang Z, Liu YH, Jamieson K. WiDar:Decimeter-level passive tracking via velocity monitoring with commodity Wi-Fi. In:Proc. of the 18th ACM Int'l Symp. on Mobile Ad Hoc Networking and Computing. Chennai, 2017.1-10. https://doi.org/10.1145/3084041.3084067
    [22] Qian K, Wu CS, Zhang Y, Zhang Y, Zhang GD, Yang Z, Liu YH. Widar2.0:Passive human tracking with a single Wi-Fi link. In:Proc. of 16th ACM Int'l Conf. on Mobile Systems, Applications, and Services. Munich:ACM, 2018.350-361. https://doi.org/10.1145/3210240.3210314
    [23] Ali K, Liu AX, Wang W, Shahzad M. Keystroke recognition using Wi-Fi signals. In:Proc. of the 21st Annual Int'l Conf. on Mobile Computing and Networking. Paris:ACM, 2015.90-102. https://doi.org/10.1145/2789168.2790109
    [24] Li H, Yang W, Wang JX, Xu Y, Huang LS. WiFinger:Talk to your smart devices with finger-grained gesture. In:Proc. of the 2016 ACM Int'l Joint Conf. on Pervasive and Ubiquitous Computing. Heidelberg:ACM, 2016.250-261. https://doi.org/10.1145/2971648
    [25] Wang GH, Zou YP, Zhou ZM, Wu KS, Ni LM. We can hear you with Wi-Fi! IEEE Trans. on Mobile Computing, 2016,15(11):2907-2920. https://doi.org/10.1145/2639108.2639112
    [26] Zhang DQ, Wang H, Wu D. Toward centimeter-scale human activity sensing with Wi-Fi signals. IEEE Computer Magazine, 2017, 50(1):48-57. https://doi.org/10.1109/MC.2017.7
    [27] Wang H, Zhang DQ, Ma JY, Wang YS, Wang YX, Wu D, Gu T, Xie B. Human respiration detection with commodity Wi-Fi devices:Do user location and body orientation matter? In:Proc. of the 2016 ACM Int'l Joint Conf. on Pervasive and Ubiquitous Computing. Heidelberg:ACM, 2016.25-36. https://doi.org/10.1145/2971648
    [28] Zhang FS, Zhang DQ, Xiong J, Wang H, Niu K, Jin BH, Wang YX. From Fresnel diffraction model to fine-grained human respiration sensing with commodity Wi-Fi devices. In:Proc. of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies. Singapore:ACM, 2018.1-23. https://doi.org/0000001.0000001
    [29] Wu D, Zhang DQ, Xu CR, Wang H, Li X. Device-free Wi-Fi human sensing:From pattern-based to model-based approaches. IEEE Communications Magazine, 2017,55(10):91-97. https://doi.org/10.1109/MCOM.2017.1700143
    [30] Liu J, Wang Y, Chen YY, Yang J, Chen X, Chen J. Tracking vital signs during sleep leveraging off-the-shelf Wi-Fi. In:Proc. of the 16th ACM Int'l Symp. on Mobile Ad Hoc Networking and Computing. New York:ACM, 2015.267-276. https://doi.org/10.1145/274628
    [31] Han CM, Wu KS, Wang YX, Ni LM. WiFall:Device-free fall detection by wireless networks. In:Proc. of the 2014 IEEE Conf. on Computer Communications. Toronto:IEEE, 2014.271-279. https://doi.org/10.1109/INFOCOM.2014.6847948
    [32] Wang H, Zhang DQ, Wang YS, Ma JY, Wang YX, Li SJ. RT-fall:A real-time and contactless fall detection system with commodity WiFi devices. IEEE Trans. on Mobile Computing, 2017,16(2):511-526. https://doi.org/10.1109/TMC.2016.2557795
    [33] Li SJ, Li X, Lv Q, Tian GY, Zhang DQ. Wi-Fit:Ubiquitous bodyweight exercise monitoring with commodity Wi-Fi devices. In:Proc. of the 15th IEEE Int'l Conf. on Ubiquitous Intelligence and Computing. Guangzhou:IEEE, 2018.530-537. https://doi.org/10.1109/SmartWorld.2018.00114
    [34] Wang YX, Li SJ, Wang H, Ma JY, Wang YS, Zhang DQ. Survey on Wi-Fi based contactless activity recognition. Jounal of Zhejiang University (Engineering Science), 2017,51(4):648-654(in Chinese with English abstract).
    [35] Li X. Wi-Fi-based passive sensing based on the target reflected signal's parameters estimation[Ph.D. Thesis]. Beijing:Peking University, 2018(in Chinese with English abstract).
    [36] Zhang DQ, Wang H, Wu D. Toward militimeter-scale contactless sensing with Wi-Fi signals:From pattern to model. Communications of China Computer Federation, 2018,14(1):18-26(in Chinese with English abstract).
    [37] Halperin D, Hu WJ, Sheth A, Wetherall D. Tool release:Gathering 802.11N traces with channel state information. SIGCOMM Computer Communator, 2011,41(1):53-53. https://doi.org/10.1145/1925861.1925870
    [38] Fritz SL, Lusardi M. White paper:Walking speed:The sixth vital sign. Journal of Geriatric Physical Therapy, 2009,32(2):46.
    [39] Bohannon WR. Comfortable and maximum walking speed of adults aged 20~79 years:Reference values and determinants. Age and Ageing, 1997,26(1):15. https://doi.org/10.1093/ageing/26.1.15
    附中文参考文献:
    [34] 王钰翔,李晟洁,王皓,马钧轶,王亚沙,张大庆.基于Wi-Fi的非接触式行为识别研究综述.浙江大学学报(工学版),2017,5(4):648-654.
    [35] 李翔.基于目标反射信号参数估计的Wi-Fi非接触式感知技术[博士学位论文].北京:北京大学,2018.
    [36] 张大庆,王皓,吴丹.毫米级的Wi-Fi无接触感知:从模式到模型.中国计算机学会通讯,2018,14(1):18-26.
    Cited by
    Comments
    Comments
    分享到微博
    Submit
Get Citation

李晟洁,李翔,张越,王亚沙,张大庆.基于Wi-Fi信道状态信息的行走识别与行走参数估计.软件学报,2021,32(10):3122-3138

Copy
Share
Article Metrics
  • Abstract:2011
  • PDF: 4806
  • HTML: 2289
  • Cited by: 0
History
  • Received:February 14,2019
  • Revised:January 31,2020
  • Online: June 08,2020
  • Published: October 06,2021
You are the first2034066Visitors
Copyright: Institute of Software, Chinese Academy of Sciences Beijing ICP No. 05046678-4
Address:4# South Fourth Street, Zhong Guan Cun, Beijing 100190,Postal Code:100190
Phone:010-62562563 Fax:010-62562533 Email:jos@iscas.ac.cn
Technical Support:Beijing Qinyun Technology Development Co., Ltd.

Beijing Public Network Security No. 11040202500063