research-article

Radio-based device-free activity recognition with radio frequency interference

Authors:

Chun Tung ChouAuthors Info & Claims

IPSN '15: Proceedings of the 14th International Conference on Information Processing in Sensor Networks

Pages 154 - 165

https://doi.org/10.1145/2737095.2737117

Published: 13 April 2015 Publication History

Abstract

Activity recognition is an important component of many pervasive computing applications. Device-free activity recognition has the advantage that it does not have the privacy concern of using cameras and the subjects do not have to carry a device on them. Recently, it has been shown that channel state information (CSI) can be used for activity recognition in a device-free setting. With the proliferation of wireless devices, it is important to understand how radio frequency interference (RFI) can impact on pervasive computing applications. In this paper, we investigate the impact of RFI on device-free CSI-based location-oriented activity recognition. We conduct experiments in environments without and with RFI. We present data to show that RFI can have a significant impact on the CSI vectors. In the absence of RFI, different activities give rise to different CSI vectors that can be differentiated visually. However, in the presence of RFI, the CSI vectors become much noisier and activity recognition also becomes harder. Our extensive experiments shows that the performance of state-of-the-art classification methods may degrade significantly with RFI. We then propose a number of counter measures to mitigate the impact of RFI and improve the location-oriented activity recognition performance. Our evaluation shows the proposed method can improve up to 10% true detection rate in the presence of RFI. We also study the impact of bandwidth on activity recognition performance. We show that with a channel bandwidth of 20 MHz (which is used by WiFi), it is possible to achieve a good activity recognition accuracy when RFI is present.

References

[1]

F. Adib, Z. Kabelac, D. Katabi, and R. C. Miller. 3D tracking via body radio reflections. In NSDI '14, Seattle, WA, 2014.

Digital Library

[2]

F. Adib and D. Katabi. See through walls with WiFi! In SIGCOMM '13, pages 75--86, New York, NY, USA, 2013. ACM.

Digital Library

[3]

L. Bao and S. S. Intille. Activity recognition from user-annotated acceleration data. In Pervasive computing, pages 1--17. Springer, 2004.

[4]

I. Cohen and H. Li. Inference of human postures by classification of 3D human body shape. In AMFG 2003., pages 74--81. IEEE, 2003.

Digital Library

[5]

D. Halperin, W. Hu, A. Sheth, and D. Wetherall. Tool release: Gathering 802.11n traces with channel state information. ACM SIGCOMM CCR, 41(1): 53, Jan. 2011.

Digital Library

[6]

T. Hao, G. Xing, and G. Zhou. isleep: unobtrusive sleep quality monitoring using smartphones. In SenSys, pages 4:1--4:14. ACM, 2013.

Digital Library

[7]

M. Harville and D. Li. Fast, integrated person tracking and activity recognition with plan-view templates from a single stereo camera. In CVPR 2004., volume 2, pages II--398. IEEE, 2004.

Digital Library

[8]

Jawbone. UP. https://jawbone.com/up, 2014. {Online; accessed 28-Agust-2014}.

[9]

O. Kaltiokallio, M. Bocca, and N. Patwari. Enhancing the accuracy of radio tomographic imaging using channel diversity. In IEEE MASS 2012, pages 254--262. IEEE, 2012.

Digital Library

[10]

M. Keally, G. Zhou, G. Xing, J. Wu, and A. Pyles. PBN: towards practical activity recognition using smartphone-based body sensor networks. In SenSys, pages 246--259. ACM, 2011.

Digital Library

[11]

B. Kellogg, V. Talla, and S. Gollakota. Bringing gesture recognition to all devices. In NSDI 14, Seattle, WA, 2014. USENIX.

Digital Library

[12]

J. R. Kwapisz, G. M. Weiss, and S. A. Moore. Activity recognition using cell phone accelerometers. ACM SigKDD Explorations Newsletter, 12(2): 74--82, 2011.

Digital Library

[13]

Leap Motion Inc. Leap motion. https://www.leapmotion.com/, 2014. {Online; accessed 15-September-2014}.

[14]

X. Mei and H. Ling. Robust visual tracking and vehicle classification via sparse representation. IEEE TPAMI, 33(11): 2259--2272, 2011.

Digital Library

[15]

P. Melgarejo, X. Zhang, P. Ramanathan, and D. Chu. Leveraging directional antenna capabilities for fine-grained gesture recognition. In UbiComp '14, pages 541--551, New York, NY, USA, 2014. ACM.

Digital Library

[16]

Microsoft. Kinect. http://www.microsoft.com/en-us/kinectforwindows/, 2014. {Online; accessed 28-Agust-2014}.

[17]

P. Misra, W. Hu, M. Yang, and S. Jha. Efficient cross-correlation via sparse representation in sensor networks. In IPSN 2012, pages 13--24, New York, NY, USA, 2012. ACM.

Digital Library

[18]

P. K. Misra, W. Hu, Y. Jin, J. Liu, A. Souza de Paula, N. Wirstrom, and T. Voigt. Energy efficient GPS acquisition with sparse-GPS. In IPSN 2014, pages 155--166. IEEE Press, 2014.

Digital Library

[19]

N. Oliver, E. Horvitz, and A. Garg. Layered representations for human activity recognition. In ICMI 2002, pages 3--8. IEEE, 2002.

Digital Library

[20]

Q. Pu, S. Gupta, S. Gollakota, and S. Patel. Whole-home gesture recognition using wireless signals. In MobiCom 2013, pages 27--38, Sept. 2013.

Digital Library

[21]

N. Ravi, N. Dandekar, P. Mysore, and M. L. Littman. Activity recognition from accelerometer data. In AAAI, volume 5, pages 1541--1546, 2005.

Digital Library

[22]

T. Sathyan, D. Humphrey, and M. Hedley. WASP: A system and algorithms for accurate radio localization using low-cost hardware. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 41(2): 211--222, 2011.

Digital Library

[23]

S. Sen, J. Lee, K.-H. Kim, and P. Congdon. Avoiding multipath to revive inbuilding wifi localization. In MobiSys 2013, pages 249--262. ACM, 2013.

Digital Library

[24]

S. Sen, B. Radunovic, R. R. Choudhury, and T. Minka. You are facing the Mona Lisa: spot localization using PHY layer information. In MobiSys 2012, pages 183--196. ACM, 2012.

Digital Library

[25]

Y. Shen, W. Hu, J. Liu, M. Yang, B. Wei, and C. T. Chou. Efficient background subtraction for real-time tracking in embedded camera networks. In SenSys '12, pages 295--308, New York, NY, USA, 2012. ACM.

Digital Library

[26]

Y. Shen, W. Hu, M. Yang, B. Wei, S. Lucey, and C. T. Chou. Face recognition on smartphones via optimised sparse representation classification. In IPSN '14, pages 237--248, Piscataway, NJ, USA, 2014. IEEE Press.

Digital Library

[27]

S. Sigg, M. Scholz, S. Shi, Y. Ji, and M. Beigl. RF-sensing of activities from non-cooperative subjects in device-free recognition systems using ambient and local signals. IEEE TMC, 13(4): 907--920, 2014.

Digital Library

[28]

G. Wang, Y. Zou, Z. Zhou, K. Wu, and L. M. Ni. We can hear you with Wi-Fi! In MobiCom 2014, pages 593--604. ACM, 2014.

Digital Library

[29]

Y. Wang, J. Liu, Y. Chen, M. Gruteser, J. Yang, and H. Liu. E-eyes: device-free location-oriented activity identification using fine-grained wifi signatures. In MobiCom 2014, pages 617--628. ACM, 2014.

Digital Library

[30]

B. Wei, A. Varshney, N. Patwari, W. Hu, T. Voigt, Chou, and C. Tung. drti: Directional radio tomography. In IPSN '15, Seattle, WA, USA, 2015. ACM.

Digital Library

[31]

B. Wei, M. Yang, Y. Shen, R. Rana, C. T. Chou, and W. Hu. Real-time classification via sparse representation in acoustic sensor networks. In SenSys 2013, page 21. ACM, 2013.

Digital Library

[32]

J. Wilson and N. Patwari. Radio tomographic imaging with wireless networks. IEEE TMC, 9(5): 621--632, 2010.

Digital Library

[33]

J. Wright, A. Yang, A. Ganesh, S. Sastry, and Y. Ma. face recognition via sparse representation. TPAMI, 31(2): 210--227, 2009.

Digital Library

[34]

X. Wu and M. Liu. In-situ soil moisture sensing: Measurement scheduling and estimation using compressive sensing. In IPSN '12, pages 1--12, New York, NY, USA, 2012. ACM.

Digital Library

[35]

Xiaomi. Mi Band. http://www.mi.com/shouhuan, 2014. {Online; accessed 28-August-2014}.

[36]

C. Xu, B. Firner, R. S. Moore, Y. Zhang, W. Trappe, R. Howard, F. Zhang, and N. An. Scpl: Indoor device-free multi-subject counting and localization using radio signal strength. In IPSN '13, pages 79--90, New York, NY, USA, 2013. ACM.

Digital Library

[37]

C. Xu, B. Firner, Y. Zhang, R. Howard, J. Li, and X. Lin. Improving rf-based device-free passive localization in cluttered indoor environments through probabilistic classification methods. In IPSN '12, pages 209--220, New York, NY, USA, 2012. ACM.

Digital Library

[38]

C. Xu, M. Gao, B. Firner, Y. Zhang, R. Howard, and J. Li. Towards robust device-free passive localization through automatic camera-assisted recalibration. In ACM SenSys, 2012.

Digital Library

[39]

Z. Yang, Z. Zhou, and Y. Liu. From RSSI to CSI: Indoor localization via channel response. ACM Comput. Surv., 46(2): 25:1--25:32, Dec. 2013.

Digital Library

[40]

K. Yatani and K. N. Truong. Bodyscope: a wearable acoustic sensor for activity recognition. In Ubicomp 2012, pages 341--350. ACM, 2012.

Digital Library

[41]

M. Youssef, M. Mah, and A. Agrawala. Challenges: device-free passive localization for wireless environments. In MobiCom 2007, pages 222--229. ACM, 2007.

Digital Library

[42]

T. Zhao, M. Aggarwal, R. Kumar, and H. Sawhney. Real-time wide area multi-camera stereo tracking. In CVPR 2005, volume 1, pages 976--983. IEEE, 2005.

Digital Library

[43]

Y. Zhao and N. Patwari. Noise reduction for variance-based device-free localization and tracking. In SECON 2011, pages 179--187, 2011.

[44]

Y. Zhao, N. Patwari, J. M. Phillips, and S. Venkatasubramanian. Radio tomographic imaging and tracking of stationary and moving people via kernel distance. In IPSN '13, pages 229--240, New York, NY, USA, 2013. ACM.

Digital Library

[45]

Z. Zhou, Z. Yang, C. Wu, L. Shangguan, and Y. Liu. Omnidirectional coverage for device-free passive human detection. IEEE TPDS, 2013.

Digital Library

Cited By

Wang JDu HNiyato DZhou MKang JVincent Poor H(2024)Acceleration Estimation of Signal Propagation Path Length Changes for Wireless SensingIEEE Transactions on Wireless Communications10.1109/TWC.2024.338242523:9(11476-11492)Online publication date: Sep-2024
https://doi.org/10.1109/TWC.2024.3382425
Shafiqul Islam MHumayun Kabir MAli Hasan MShin W(2024)Wi-MIR: A CSI Dataset for Wi-Fi Based Multi-Person Interaction RecognitionIEEE Access10.1109/ACCESS.2024.339517312(67256-67272)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3395173
Huang SLi KYou DChen YLin ALiu SLi XMcCann J(2024)WiMANS: A Benchmark Dataset for WiFi-Based Multi-user Activity SensingComputer Vision – ECCV 202410.1007/978-3-031-72946-1_5(72-91)Online publication date: 2-Oct-2024
https://doi.org/10.1007/978-3-031-72946-1_5
Show More Cited By

Index Terms

Radio-based device-free activity recognition with radio frequency interference

Recommendations

Blind signal-to-interference-plus-noise ratio estimation of OFDM/OQAM system in radio frequency interference environment
Abstract
Based on cyclostationarity properties of Orthogonal Frequency Division Multiplexing with Offset Quadrature Amplitude Modulation (OFDM/OQAM) signal, a novel blind signal-to-interference-plus-noise ratio (SINR) estimation method for OFDM/OQAM system ...
Performance analysis of mobile radio systems over composite fading/shadowing channels with co-located interference

This paper presents an analytical framework for performance evaluation of mobile radio systems operating in composite fading/shadowing channels in the presence of co-located co-channel interference. The desired user and the interferers are subject to ...
The Radio Frequency Interference Mitigation in Ionospheric Sounding
IMCCC '11: Proceedings of the 2011 First International Conference on Instrumentation, Measurement, Computer, Communication and Control

Operating in noisy radio frequency environments has been a very important issue in high-frequency sounding these years, especially the narrow-band interference. So it is urgent to find an effective method to eliminate the interference. The algorithm of ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

IPSN '15: Proceedings of the 14th International Conference on Information Processing in Sensor Networks

April 2015

430 pages

ISBN:9781450334754

DOI:10.1145/2737095

General Chair:
Suman Nath
Microsoft Research
,
Program Chairs:
Bhaskar Krishnamachari
University of Southern California
,
Anthony Rowe
Carnegie Mellon University

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

IEEE-SPS: Signal Processing Society
IEEE
IEEE-CSS: Control Systems Society
ACM: Association for Computing Machinery
SIGBED: ACM Special Interest Group on Embedded Systems
IEEE-CS IRTS: IEEE CS Internet, Real Time Systems

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 April 2015

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Research-article

Conference

IPSN '15

Sponsor:

IEEE-SPS
IEEE-CSS
ACM
SIGBED
IEEE-CS IRTS

IPSN '15: The 14th International Conference on Information Processing in Sensor Networks

April 13 - 16, 2015

Washington, Seattle

Acceptance Rates

Overall Acceptance Rate 143 of 593 submissions, 24%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

61
Total Citations
View Citations
861
Total Downloads

Downloads (Last 12 months)41
Downloads (Last 6 weeks)2

Reflects downloads up to 03 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Wang JDu HNiyato DZhou MKang JVincent Poor H(2024)Acceleration Estimation of Signal Propagation Path Length Changes for Wireless SensingIEEE Transactions on Wireless Communications10.1109/TWC.2024.338242523:9(11476-11492)Online publication date: Sep-2024
https://doi.org/10.1109/TWC.2024.3382425
Shafiqul Islam MHumayun Kabir MAli Hasan MShin W(2024)Wi-MIR: A CSI Dataset for Wi-Fi Based Multi-Person Interaction RecognitionIEEE Access10.1109/ACCESS.2024.339517312(67256-67272)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3395173
Huang SLi KYou DChen YLin ALiu SLi XMcCann J(2024)WiMANS: A Benchmark Dataset for WiFi-Based Multi-user Activity SensingComputer Vision – ECCV 202410.1007/978-3-031-72946-1_5(72-91)Online publication date: 2-Oct-2024
https://doi.org/10.1007/978-3-031-72946-1_5
Chen CZhou GLin Y(2023)Cross-Domain WiFi Sensing with Channel State Information: A SurveyACM Computing Surveys10.1145/357032555:11(1-37)Online publication date: 9-Feb-2023
https://dl.acm.org/doi/10.1145/3570325
Qi FZhao YBhuiyan MTao HYan WTang Z(2022)Artificial intelligence driven Wi‐Fi CSI data mining: Focusing on the intrusion detection applicationsInternational Journal of Communication Systems10.1002/dac.5338Online publication date: 7-Sep-2022
https://doi.org/10.1002/dac.5338
Li SChowdhury RShang JGupta RHong D(2021)UniTSProceedings of the 19th ACM Conference on Embedded Networked Sensor Systems10.1145/3485730.3485942(234-247)Online publication date: 15-Nov-2021
https://dl.acm.org/doi/10.1145/3485730.3485942
Wei BLi KLuo CXu WZhang JZhang K(2021)No Need of Data Pre-processingACM Transactions on Internet of Things10.1145/34679802:4(1-26)Online publication date: 16-Aug-2021
https://dl.acm.org/doi/10.1145/3467980
Ma YArshad SMuniraju STorkildson ERantala EDoppler KZhou G(2021)Location- and Person-Independent Activity Recognition with WiFi, Deep Neural Networks, and Reinforcement LearningACM Transactions on Internet of Things10.1145/34247392:1(1-25)Online publication date: 21-Jan-2021
https://dl.acm.org/doi/10.1145/3424739
Su STan WZhu XListon RAazhang B(2021)Data-Driven Mode and Group Selection for Downlink MU-MIMO With Implementation in Commodity 802.11ac NetworkIEEE Transactions on Communications10.1109/TCOMM.2021.304913069:3(1620-1634)Online publication date: Mar-2021
https://doi.org/10.1109/TCOMM.2021.3049130
Denis SBellekens BWeyn MBerkvens R(2021)Sensing Thousands of Visitors Using Radio FrequencyIEEE Systems Journal10.1109/JSYST.2020.301918915:4(5090-5093)Online publication date: Dec-2021
https://doi.org/10.1109/JSYST.2020.3019189
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents