research-article

From Real to Complex: Enhancing Radio-based Activity Recognition Using Complex-Valued CSI

Authors:

Chun Tung ChouAuthors Info & Claims

ACM Transactions on Sensor Networks (TOSN), Volume 15, Issue 3

Article No.: 35, Pages 1 - 32

https://doi.org/10.1145/3338026

Published: 09 August 2019 Publication History

Abstract

Activity recognition is an important component of many pervasive computing applications. Radio-based activity recognition has the advantage that it does not have the privacy concern compared with camera-based solutions, and subjects do not have to carry a device on them. It has been shown 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 article, we investigate the impact of RFI on device-free CSI-based location-oriented activity recognition. 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 show that the performance may degrade significantly with RFI. We then propose a number of countermeasures to mitigate the impact of RFI and improve the performance. We are also the first to use complex-valued CSI along with the state-of-the-art Sparse Representation Classification method to enhance the performance in the environment with RFI.

References

[1]

Fadel Adib, Zach Kabelac, Dina Katabi, and Robert C. Miller. 2014. 3D tracking via body radio reflections. In Proceedings of the NSDI’14.

Digital Library

[2]

Fadel Adib and Dina Katabi. 2013. See through walls with WiFi&excl; In Proceedings of the SIGCOMM’13. ACM, New York, NY, 75--86.

Digital Library

[3]

Ling Bao and Stephen S. Intille. 2004. Activity recognition from user-annotated acceleration data. In Pervasive Computing. Springer, 1--17.

[4]

Isaac Cohen and Hongxia Li. 2003. Inference of human postures by classification of 3D human body shape. In Proceedings of the AMFG’03. IEEE, 74--81.

Digital Library

[5]

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

Digital Library

[6]

Tian Hao, Guoliang Xing, and Gang Zhou. 2013. iSleep: Unobtrusive sleep quality monitoring using smartphones. In Proceedings of the SenSys. ACM, 4:1--4:14.

Digital Library

[7]

Michael Harville and Dalong Li. 2004. Fast, integrated person tracking and activity recognition with plan-view templates from a single stereo camera. In Proceedings of the CVPR’04, Vol. 2. IEEE, II--398.

Digital Library

[8]

Jawbone. 2014. UP. Retrieved from: https://jawbone.com/up.

[9]

Ossi Kaltiokallio, Maurizio Bocca, and Neal Patwari. 2012. Enhancing the accuracy of radio tomographic imaging using channel diversity. In Proceedings of the IEEE MASS’12. IEEE, 254--262.

Digital Library

[10]

Matthew Keally, Gang Zhou, Guoliang Xing, Jianxin Wu, and Andrew Pyles. 2011. PBN: Towards practical activity recognition using smartphone-based body sensor networks. In Proceedings of the SenSys. ACM, 246--259.

Digital Library

[11]

Bryce Kellogg, Vamsi Talla, and Shyamnath Gollakota. 2014. Bringing gesture recognition to all devices. In Proceedings of the NSDI’14.

Digital Library

[12]

Jennifer R. Kwapisz, Gary M. Weiss, and Samuel A. Moore. 2011. Activity recognition using cell phone accelerometers. ACM SigKDD Explor. Newslett. 12, 2 (2011), 74--82.

Digital Library

[13]

Leap Motion Inc. 2014. Leap Motion. Retrieved from: https://www.leapmotion.com/.

[14]

Tianxing Li, Chuankai An, Zhao Tian, Andrew T. Campbell, and Xia Zhou. 2015. Human sensing using visible light communication. In Proceedings of the MobiCom. ACM, 331--344.

Digital Library

[15]

Yongsen Ma, Gang Zhou, Shuangquan Wang, Hongyang Zhao, and Woosub Jung. 2018. SignFi: Sign language recognition using WiFi. Ubicomp 2018 2, 1 (2018), 23.

Digital Library

[16]

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

Digital Library

[17]

Pedro Melgarejo, Xinyu Zhang, Parameswaran Ramanathan, and David Chu. 2014. Leveraging directional antenna capabilities for fine-grained gesture recognition. In Proceedings of the UbiComp’14. ACM, New York, NY, 541--551.

Digital Library

[18]

Microsoft. 2014. Kinect. Retrieved from: http://www.microsoft.com/en-us/kinectforwindows/.

[19]

Prasant Misra, Wen Hu, Mingrui Yang, and Sanjay Jha. 2012. Efficient cross-correlation via sparse representation in sensor networks. In Proceedings of the IPSN’12. ACM, New York, NY, 13--24.

Digital Library

[20]

Prasant Kumar Misra, Wen Hu, Yuzhe Jin, Jie Liu, Amanda Souza de Paula, Niklas Wirstrom, and Thiemo Voigt. 2014. Energy efficient GPS acquisition with sparse-GPS. In Proceedings of the IPSN’14. IEEE Press, 155--166.

Digital Library

[21]

Nuria Oliver, Eric Horvitz, and Ashutosh Garg. 2002. Layered representations for human activity recognition. In Proceedings of the ICMI’02. IEEE, 3--8.

Digital Library

[22]

Qifan Pu, Sidhant Gupta, Shyamnath Gollakota, and Shwetak Patel. 2013. Whole-home gesture recognition using wireless signals. In Proceedings of the MobiCom’13. 27--38.

Digital Library

[23]

Kun Qian, Chenshu Wu, Zimu Zhou, Yue Zheng, Zheng Yang, and Yunhao Liu. 2017. Inferring motion direction using commodity Wi-Fi for interactive exergames. In Proceedings of the CHI’17. ACM, 1961--1972.

Digital Library

[24]

Nishkam Ravi, Nikhil Dandekar, Preetham Mysore, and Michael L. Littman. 2005. Activity recognition from accelerometer data. In Proceedings of the AAAI, Vol. 5. 1541--1546.

Digital Library

[25]

Thuraiappah Sathyan, David Humphrey, and Mark Hedley. 2011. WASP: A system and algorithms for accurate radio localization using low-cost hardware. IEEE Trans. Syst., Man, Cyber., Part C: Appl. Rev. 41, 2 (2011), 211--222.

Digital Library

[26]

Souvik Sen, Jeongkeun Lee, Kyu-Han Kim, and Paul Congdon. 2013. Avoiding multipath to revive inbuilding WiFi localization. In Proceedings of the MobiSys’13. ACM, 249--262.

Digital Library

[27]

Souvik Sen, Božidar Radunovic, Romit Roy Choudhury, and Tom Minka. 2012. You are facing the Mona Lisa: Spot localization using PHY layer information. In Proceedings of the MobiSys’12. ACM, 183--196.

Digital Library

[28]

Yiran Shen, Wen Hu, Junbin Liu, Mingrui Yang, Bo Wei, and Chun Tung Chou. 2012. Efficient background subtraction for real-time tracking in embedded camera networks. In Proceedings of the SenSys’12. ACM, New York, NY, 295--308.

Digital Library

[29]

Yiran Shen, Wen Hu, Mingrui Yang, Junbin Liu, Bo Wei, Simon Lucey, and Chun Tung Chou. 2016. Real-time and robust compressive background subtraction for embedded camera networks. IEEE Trans. Mobile Comput. 15, 2 (2016), 406--418.

Digital Library

[30]

Yiran Shen, Wen Hu, Mingrui Yang, Bo Wei, Simon Lucey, and Chun Tung Chou. 2014. Face recognition on smartphones via optimised sparse representation classification. In Proceedings of the IPSN’14. IEEE Press, Piscataway, NJ, 237--248. Retrieved from: http://dl.acm.org/citation.cfm?id=2602339.2602366.

Digital Library

[31]

Yiran Shen, Mingrui Yang, Bo Wei, Chun Tung Chou, and Wen Hu. 2017. Learn to recognise: Exploring priors of sparse face recognition on smartphones. IEEE Trans. Mobile Comput. 16, 6 (2017), 1705--1717.

Digital Library

[32]

Stephan Sigg, Markus Scholz, Shuyu Shi, Yusheng Ji, and Michael Beigl. 2014. RF-sensing of activities from non-cooperative subjects in device-free recognition systems using ambient and local signals. IEEE TMC 13, 4 (2014), 907--920.

Digital Library

[33]

Jiancheng Sun, Chongxun Zheng, Xiaohe Li, and Yatong Zhou. 2010. Analysis of the distance between two classes for tuning SVM hyperparameters. IEEE Trans. Neural Netw. 21, 2 (2010), 305--318.

Digital Library

[34]

Ewout Van Den Berg and Michael P. Friedlander. 2008. Probing the Pareto frontier for basis pursuit solutions. SIAM J. Sci. Comput. 31, 2 (2008), 890--912.

Digital Library

[35]

Ewout Van den Berg and Michael P. Friedlander. 2011. Sparse optimization with least-squares constraints. SIAM J. Optim. 21, 4 (2011), 1201--1229.

[36]

Guanhua Wang, Yongpan Zou, Zimu Zhou, Kaishun Wu, and Lionel M. Ni. 2014. We can hear you with Wi-Fi&excl; In Proceedings of the MobiCom’14. ACM, 593--604.

Digital Library

[37]

Wei Wang, Alex X. Liu, and Muhammad Shahzad. 2016. Gait recognition using WiFi signals. In Proceedings of the UbiComp. ACM, 363--373.

Digital Library

[38]

Wei Wang, Alex X. Liu, Muhammad Shahzad, Kang Ling, and Sanglu Lu. 2017. Device-free human activity recognition using commercial WiFi devices. IEEE J. Select. Areas Commun. 35, 5 (2017), 1118--1131.

Digital Library

[39]

Xuyu Wang, Lingjun Gao, Shiwen Mao, and Santosh Pandey. 2017. CSI-based fingerprinting for indoor localization: A deep learning approach. IEEE TVT 66, 1 (2017), 763--776.

[40]

Xuyu Wang, Chao Yang, and Shiwen Mao. 2017. PhaseBeat: Exploiting CSI phase data for vital sign monitoring with commodity WiFi devices. In Proceedings of the ICDCS. IEEE, 1230--1239.

[41]

Yan Wang, Jian Liu, Yingying Chen, Marco Gruteser, Jie Yang, and Hongbo Liu. 2014. E-eyes: Device-free location-oriented activity identification using fine-grained WiFi signatures. In Proceedings of the MobiCom’14. ACM, 617--628.

Digital Library

[42]

Bo Wei, Wen Hu, Mingrui Yang, and Chun Tung Chou. 2015. Radio-based device-free activity recognition with radio frequency interference. In Proceedings of the IPSN. ACM, 154--165.

Digital Library

[43]

Bo Wei, Ambuj Varshney, Neal Patwari, Wen Hu, Thiemo Voigt, Chou, and Chun Tung. 2015. dRTI: Directional radio tomography. In Proceedings of the IPSN’15. ACM, 12.

Digital Library

[44]

Bo Wei, Mingrui Yang, Yiran Shen, Rajib Rana, Chun Tung Chou, and Wen Hu. 2013. Real-time classification via sparse representation in acoustic sensor networks. In Proceedings of the SenSys’13. ACM, 21.

Digital Library

[45]

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

Digital Library

[46]

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

Digital Library

[47]

Xiaopei Wu and Mingyan Liu. 2012. In-situ soil moisture sensing: Measurement scheduling and estimation using compressive sensing. In Proceedings of the IPSN’12. ACM, New York, NY, 1--12.

Digital Library

[48]

Xiaomi. 2014. Mi Band. Retrieved from: http://www.mi.com/shouhuan.

[49]

Chenren Xu, Bernhard Firner, Robert S. Moore, Yanyong Zhang, Wade Trappe, Richard Howard, Feixiong Zhang, and Ning An. 2013. SCPL: Indoor device-free multi-subject counting and localization using radio signal strength. In Proceedings of the IPSN’13. ACM, New York, NY, 79--90.

Digital Library

[50]

Chenren Xu, Bernhard Firner, Yanyong Zhang, Richard Howard, Jun Li, and Xiaodong Lin. 2012. Improving RF-based device-free passive localization in cluttered indoor environments through probabilistic classification methods. In Proceedings of the IPSN’12. ACM, New York, NY, 209--220.

Digital Library

[51]

Chenren Xu, Mingchen Gao, Bernhard Firner, Yanyong Zhang, Richard Howard, and Jun Li. 2012. Towards robust device-free passive localization through automatic camera-assisted recalibration. In Proceedings of the ACM SenSys.

Digital Library

[52]

Weitao Xu, Yiran Shen, Neil Bergmann, and Wen Hu. 2016. Sensor-assisted face recognition system on smart glass via multi-view sparse representation classification. In Proceedings of the IPSN. IEEE Press, 2.

Digital Library

[53]

Zheng Yang, Zimu Zhou, and Yunhao Liu. 2013. From RSSI to CSI: Indoor localization via channel response. ACM Comput. Surv. 46, 2, Article 25 (Dec. 2013), 32 pages.

Digital Library

[54]

Koji Yatani and Khai N. Truong. 2012. Bodyscope: A wearable acoustic sensor for activity recognition. In Proceedings of the Ubicomp’12. ACM, 341--350.

Digital Library

[55]

Moustafa Youssef, Matthew Mah, and Ashok Agrawala. 2007. Challenges: Device-free passive localization for wireless environments. In Proceedings of the MobiCom’07. ACM, 222--229.

Digital Library

[56]

Yunze Zeng, Parth H. Pathak, and Prasant Mohapatra. 2016. WiWho: WiFi-based person identification in smart spaces. In Proceedings of the IPSN. IEEE Press, 4.

Digital Library

[57]

Jin Zhang, Bo Wei, Wen Hu, and Salil S. Kanhere. 2016. WiFi-ID: Human identification using WiFi signal. In Proceedings of the DCOSS. IEEE, 75--82.

[58]

Mingmin Zhao, Fadel Adib, and Dina Katabi. 2016. Emotion recognition using wireless signals. In Proceedings of the MobiCom. ACM, 95--108.

Digital Library

[59]

Tao Zhao, Manoj Aggarwal, Rakesh Kumar, and Harpreet Sawhney. 2005. Real-time wide area multi-camera stereo tracking. In Proceedings of the CVPR’05, Vol. 1. IEEE, 976--983.

Digital Library

[60]

Yang Zhao and N. Patwari. 2011. Noise reduction for variance-based device-free localization and tracking. In Proceedings of the SECON’11. 179--187.

[61]

Yang Zhao, Neal Patwari, Jeff M. Phillips, and Suresh Venkatasubramanian. 2013. Radio tomographic imaging and tracking of stationary and moving people via kernel distance. In Proceedings of the IPSN’13. ACM, New York, NY, 229--240.

Digital Library

[62]

Xiaolong Zheng, Jiliang Wang, Longfei Shangguan, Zimu Zhou, and Yunhao Liu. 2017. Design and implementation of a CSI-based ubiquitous smoking detection system. IEEE/ACM Trans. Netw. 25, 6 (2017), 3781--3793.

Digital Library

[63]

Zimu Zhou, Zheng Yang, Chenshu Wu, Longfei Shangguan, and Yunhao Liu. 2013. Omnidirectional coverage for device-free passive human detection. IEEE Transactions on Parallel and Distributed Systems 25, 7 (2013), 1819--1829.

Digital Library

Cited By

Slapničar GWang WLuštrek M(2024)Feasibility of Remote Blood Pressure Estimation via Narrow-band Multi-wavelength Pulse Transit TimeACM Transactions on Sensor Networks10.1145/359730220:4(1-21)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3597302
Deng FJovanov ESong HShi WZhang YXu W(2024)WiLDAR: WiFi Signal-Based Lightweight Deep Learning Model for Human Activity RecognitionIEEE Internet of Things Journal10.1109/JIOT.2023.329400411:2(2899-2908)Online publication date: 15-Jan-2024
https://doi.org/10.1109/JIOT.2023.3294004
Thakur ABisoy SMishra PPratap Patra RRanjan R(2024)Speech Emotion Recognition Using Machine Learning and Deep Learning2024 1st International Conference on Cognitive, Green and Ubiquitous Computing (IC-CGU)10.1109/IC-CGU58078.2024.10530666(1-6)Online publication date: 1-Mar-2024
https://doi.org/10.1109/IC-CGU58078.2024.10530666
Show More Cited By

Index Terms

From Real to Complex: Enhancing Radio-based Activity Recognition Using Complex-Valued CSI
1. Computer systems organization
  1. Embedded and cyber-physical systems
    1. Sensor networks
2. Networks
  1. Network types
    1. Cyber-physical networks
      1. Sensor networks

Recommendations

Exact Error Rate Analysis of MIMO-MRC System under Cochannel Interference and Imperfect Channel State Information

In this paper, we derive closed-form solution for the bit error rate of multi-input multi-output (MIMO) system with maximum ratio combining. We consider binary PSK modulation suffers from cochannel interference (CCI) and imperfect channel state ...
Quadrature spatial modulation based multiuser MIMO transmission system

This study presents a multiuser (MU) multiple‐input multiple‐output (MIMO) downlink transmission scheme based on the quadrature spatial modulation (QSM) concept, which uses the indices of the non‐zero entries in its transmission vector to modulate and ...
WFID: Passive Device-free Human Identification Using WiFi Signal
MOBIQUITOUS 2016: Proceedings of the 13th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

We present WFID, a passive device-free indoor human identification system with one pair of WiFi signal transmitter and receiver. WFID design is motivated by the observation that PHY layer Channel State Information (CSI) is capable of capturing the ...

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Sensor Networks

ACM Transactions on Sensor Networks Volume 15, Issue 3

August 2019

324 pages

ISSN:1550-4859

EISSN:1550-4867

DOI:10.1145/3335317

Editor:
Yunhao Liu
Tsinghua University, China

Issue’s Table of Contents

Copyright © 2019 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]

Publisher

Association for Computing Machinery

New York, NY, United States

Journal Family

ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 09 August 2019

Accepted: 01 May 2019

Revised: 01 May 2019

Received: 01 July 2018

Published in TOSN Volume 15, Issue 3

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

23
Total Citations
View Citations
309
Total Downloads

Downloads (Last 12 months)31
Downloads (Last 6 weeks)1

Reflects downloads up to 30 Aug 2024

Other Metrics

View Author Metrics

Citations

Cited By

Slapničar GWang WLuštrek M(2024)Feasibility of Remote Blood Pressure Estimation via Narrow-band Multi-wavelength Pulse Transit TimeACM Transactions on Sensor Networks10.1145/359730220:4(1-21)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3597302
Deng FJovanov ESong HShi WZhang YXu W(2024)WiLDAR: WiFi Signal-Based Lightweight Deep Learning Model for Human Activity RecognitionIEEE Internet of Things Journal10.1109/JIOT.2023.329400411:2(2899-2908)Online publication date: 15-Jan-2024
https://doi.org/10.1109/JIOT.2023.3294004
Thakur ABisoy SMishra PPratap Patra RRanjan R(2024)Speech Emotion Recognition Using Machine Learning and Deep Learning2024 1st International Conference on Cognitive, Green and Ubiquitous Computing (IC-CGU)10.1109/IC-CGU58078.2024.10530666(1-6)Online publication date: 1-Mar-2024
https://doi.org/10.1109/IC-CGU58078.2024.10530666
He QFang S(2023)Phantom-CSI Attacks against Wireless Liveness DetectionProceedings of the 26th International Symposium on Research in Attacks, Intrusions and Defenses10.1145/3607199.3607245(440-454)Online publication date: 16-Oct-2023
https://dl.acm.org/doi/10.1145/3607199.3607245
Peng CGui LSheng BGuo ZXiao F(2023)RoSeFi: A Robust Sedentary Behavior Monitoring System With Commodity WiFi DevicesIEEE Transactions on Mobile Computing10.1109/TMC.2023.332130623:5(6470-6489)Online publication date: 2-Oct-2023
https://dl.acm.org/doi/10.1109/TMC.2023.3321306
Cominelli MGringoli FRestuccia F(2023)Exposing the CSI: A Systematic Investigation of CSI-based Wi-Fi Sensing Capabilities and Limitations2023 IEEE International Conference on Pervasive Computing and Communications (PerCom)10.1109/PERCOM56429.2023.10099368(81-90)Online publication date: 13-Mar-2023
https://doi.org/10.1109/PERCOM56429.2023.10099368
Khan MAhmad JBoulila WBroadbent MShah SKoubaa AAbbasi Q(2023)Contactless Human Activity Recognition using Deep Learning with Flexible and Scalable Software Define Radio2023 International Wireless Communications and Mobile Computing (IWCMC)10.1109/IWCMC58020.2023.10182652(126-131)Online publication date: 19-Jun-2023
https://doi.org/10.1109/IWCMC58020.2023.10182652
Pavithra ALedalla SDevi JDinesh GSingh MReddy G(2023)Deep Learning-based Speech Emotion Recognition: An Investigation into a sustainably Emotion-Speech RelationshipE3S Web of Conferences10.1051/e3sconf/202343001091430(01091)Online publication date: 6-Oct-2023
https://doi.org/10.1051/e3sconf/202343001091
Gupta Y(2023)Analysis on Speech Emotion RecognizerInternational Conference on Innovative Computing and Communications10.1007/978-981-99-3315-0_57(747-754)Online publication date: 23-Jul-2023
https://doi.org/10.1007/978-981-99-3315-0_57
Li CLiu MCao Z(2022)WiHF: Gesture and User Recognition With WiFiIEEE Transactions on Mobile Computing10.1109/TMC.2020.300956121:2(757-768)Online publication date: 1-Feb-2022
https://doi.org/10.1109/TMC.2020.3009561
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 Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View Issue’s Table of Contents