Lifetime estimation for human motion with WiFi channel state information

doi:10.11772/j.issn.1001-9081.2018122431

Journal of Computer Applications ›› 2019, Vol. 39 ›› Issue (7): 2056-2060.DOI: 10.11772/j.issn.1001-9081.2018122431

• Network and communications • Previous Articles Next Articles

Lifetime estimation for human motion with WiFi channel state information

LIU Lishuang1,2, WEI Zhongcheng1,2, ZHANG Chunhua3, WANG Wei1,2, ZHAO Jijun1,2

1. School of Information and Electrical Engineering, Hebei University of Engineering, Handan Hebei 056038, China;
2. Hebei Key Laboratory of Security and Protection Information Sensing and Processing(Hebei University of Engineering), Handan Hebei 056038, China;
3. Department of Public Sports, Hebei University of Engineering, Handan Hebei 056038, China

Received:2018-12-10 Revised:2019-01-29 Online:2019-07-10 Published:2019-03-28
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61802107), the Natural Science Foundation of Hebei Province (F2018402251), the Handan Science and Technology Research and Development Program (1721203048), the Open Project of Hebei Internet of Things Data Acquisition and Processing Engineering Research Center (2016-2).

基于WiFi信道状态信息的人员活动持续时间估计

刘立双^1,2, 魏忠诚^1,2, 张春华³, 王巍^1,2, 赵继军^1,2

1. 河北工程大学信息与电气工程学院, 河北邯郸 056038;
2. 河北省安防信息感知与处理重点实验室(河北工程大学), 河北邯郸 056038;
3. 河北工程大学公共体育部, 河北邯郸 056038

通讯作者: 魏忠诚
作者简介:刘立双(1992-),女,河北邢台人,硕士研究生,CCF会员,主要研究方向:无线感知;魏忠诚(1987-),男,河南商丘人,讲师,博士,CCF会员,主要研究方向:物联网、公共安全;张春华(1970-),女,河北承德人,教授,硕士,主要研究方向:运动训练、姿态识别;王巍(1983-),男,河北邯郸人,副教授,博士,CCF会员,主要研究方向:物联网、公共安全;赵继军(1970-),男,河北邯郸人,教授,博士,CCF高级会员,主要研究方向:物联网。
基金资助:
国家自然科学基金资助项目（61802107）；河北省自然科学基金资助项目（F2018402251）；邯郸市科学技术研究与发展计划项目（1721203048）；河北省物联网数据采集与处理工程技术研究中心开放课题（2016-2）。

Abstract

Abstract:

Concerning the poor privacy and flexibility of traditional lifetime estimation for human motion, a lifetime estimation system for human motion was proposed, by analyzing the amplitude variation of WiFi Channel State Information (CSI). In this system, the continuous and complex lifetime estimation problem was transformed into a discrete and simple human motion detection problem. Firstly, the CSI was collected with filtering out the outliers and noise. Secondly, Principal Component Analysis (PCA) was used to reduce the dimension of subcarriers, obtaining the principal components and the corresponding eigenvectors. Thirdly, the variance of principal components and the mean of first difference of eigenvectors were calculated, and a Back Propagation Neural Network (BPNN) model was trained with the ratio of above two parameters as eigenvalue. Fourthly, human motion detection was achieved by the trained BP neural network model, and the CSI data were divided into some segments with equal width when the human motion was detected. Finally, after the human motion detection being performed on all CSI segments, the human motion lifetime was estimated according to the number of CSI segments with human motion detected. In real indoor environment, the average accuracy of human motion detection can reach 97% and the error rate of human motion lifetime is less than 10%. The experimental results show that the proposed system can effectively estimate the lifetime of human motion.

Key words: human motion lifetime, Channel State Information (CSI), Back Propagation Neural Network (BPNN), human motion detection, WiFi

摘要：

针对传统人员活动持续时间估计系统隐私性及灵活性较差的问题，分析信道状态信息（CSI）的幅度变化，提出了一个基于WiFi信道状态信息的人员活动持续时间估计系统。该系统重点将连续复杂的人员活动持续时间估计问题转化为离散简单的人员检测问题。首先，采集CSI信息并滤除异常值和噪声；其次，利用主成分分析法（PCA）进行子载波降维，获取主成分以及相应的特征向量；随后计算主成分方差和特征向量一阶差分均值，并将两者比值作为特征值训练反向传输神经网络（BPNN）模型；然后，利用训练好的BPNN模型进行人员检测，并当检测出有人员活动时，进一步对CSI数据进行等宽分割；最后，针对所有分割后的CSI数据实现人员检测，并依据符合人员检测结果的数据来估计人员活动的持续时间。在真实室内环境中对系统进行实验评估，人员检测平均准确率可达到97%，活动持续时间误差不超过10%。实验结果表明，该系统能够有效估计出人员活动的持续时间。

关键词: 人员活动持续时间, 信道状态信息, 反向传输神经网络, 人员活动检测, WiFi

CLC Number:

TP391.413

LIU Lishuang, WEI Zhongcheng, ZHANG Chunhua, WANG Wei, ZHAO Jijun. Lifetime estimation for human motion with WiFi channel state information[J]. Journal of Computer Applications, 2019, 39(7): 2056-2060.

刘立双, 魏忠诚, 张春华, 王巍, 赵继军. 基于WiFi信道状态信息的人员活动持续时间估计[J]. 计算机应用, 2019, 39(7): 2056-2060.

References

[1] BONOMI A G, GORIS A H, YIN B, et al. Detection of type, duration, and intensity of physical activity using an accelerometer[J]. Medicine and Science in Sports and Exercise, 2009, 41(9):1770-1777.
[2] LIN W, ZHANG Y, LU J, et al. Summarizing surveillance videos with local-patch-learning-based abnormality detection, blob sequence optimization, and type-based synopsis[J]. Neurocomputing, 2015, 155:84-98.
[3] 杨雪,吕绍和,张闽,等.基于无线信号的行为识别技术综述[J].软件学报,2015,26(Suppl.(1)):39-48.(YANG X, LYU S H, ZHANG M, et al. A survey on activity recognition using wireless signals[J]. Journal of Software, 2015, 26(Suppl.(1)):39-48.)
[4] HALPERIN D, HU W, SHETH A, et al. Tool release:gathering 802.11 n traces with channel state information[J]. ACM SIGCOMM Computer Communication Review, 2011, 41(1):53-53.
[5] 杨铮,刘云浩.Wi-Fi雷达:从RSSI到CSI[J].中国计算机学会通讯,2014,10(11):55-60.(YANG Z, LIU Y H. Wi-Fi radar:from RSSI to CSI[J]. Communications of CCF, 2014, 10(11):55-60.)
[6] ZHU H, XIAO F, SUN L, et al. R-TTWD:robust device-free through-the-wall detection of moving human with WiFi[J]. IEEE Journal on Selected Areas in Communications, 2017, 35(5):1090-1103.
[7] WANG W, LIU A X, SHAHZAD M, et al. Device-free human activity recognition using commercial WiFi devices[J]. IEEE Journal on Selected Areas in Communications, 2017, 35(5):1118-1131.
[8] GU Y, ZHAN J, JI Y, et al. MoSense:a RF-based motion detection system via off-the-shelf WiFi devices[J]. IEEE Internet of Things Journal, 2017, 4(6):2326-2341.
[9] YANG Z, ZHOU Z, LIU Y. From RSSI to CSI:indoor localization via channel response[J]. ACM Computing Surveys, 2013, 46(2):1-32.
[10] HAN C, WU K, WANG Y, et al. WiFall:device-free fall detection by wireless networks[C]//Proceedings of the 2014 International Conference on Computer Communications. Piscataway, NJ:IEEE, 2014:271-279.
[11] WANG Y, WU K, NI L M. WiFall:device-free fall detection by wireless networks[J]. IEEE Transactions on Mobile Computing, 2017, 16(2):581-594.
[12] ALI K, LIU A X, WANG W, et al. Recognizing keystrokes using WiFi devices[J]. IEEE Journal on Selected Areas in Communications, 2017, 35(5):1175-1190.
[13] ZHANG D, WANG H, WU D. Toward centimeter-scale human activity sensing with Wi-Fi signals[J]. Computer, 2017, 50(1):48-57.
[14] XU Y, YANG W, WANG J, et al. WiStep:device-free step counting with WiFi signals[C]//Proceedings of the 2018 ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies. New York:ACM, 2018, 1(4):Article No. 172.
[15] XIE Y, LI Z, LI M. Precise power delay profiling with commodity Wi-Fi[C]//Proceedings of the 2015 International Conference on Mobile Computing and Networking. New York:ACM, 2015:53-64.
[16] DAVIES L, GATHER U. The identification of multiple outliers[J]. Publications of the American Statistical Association, 1993, 88(423):782-792.
[17] XIN T, GUO B, WANG Z, et al. FreeSense:indoor human identification with Wi-Fi signals[C]//Proceedings of the 2016 IEEE Global Communications Conference. Piscataway, NJ:IEEE, 2016:1-7.

Lifetime estimation for human motion with WiFi channel state information

基于WiFi信道状态信息的人员活动持续时间估计

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