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EAR: Exploiting Uncontrollable Ambient RF Signals in Heterogeneous Networks for Gesture Recognition

Authors:

Zhichuan Huang,

Ting ZhuAuthors Info & Claims

SenSys '18: Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems

Pages 237 - 249

https://doi.org/10.1145/3274783.3274847

Published: 04 November 2018 Publication History

Abstract

The exponentially increasing number of Internet-of-Thing (IoT) devices introduces a spectrum crisis in the shared ISM band. However, it also introduces opportunities for conducting radio frequency (RF) sensing using pervasively available signals generated by heterogeneous IoT devices. In this paper, we explore how to leverage the ambient wireless traffic that i) generated by uncontrollable IoT devices and ii sensed by ambient noise floor measurements (a widely available metric in IoT devices) for human gesture recognition. Specifically, we introduce our system EAR, which can conduct fine-grained human gesture recognition using coarse-grained measurements (i.e., noise floor) of ambient RF signals generated from uncontrollable signal sources. We conducted extensive evaluations in both residential and academic buildings. Experimental results show that although EAR uses coarse-grained noise floor measurements to sense the uncontrollable signal sources, the signal sources can be distinguished with an accuracy up to 99.76%. Moreover, EAR can recognize fine-grained human gestures with high accuracy even under extremely low traffic rate (i.e., 4%) from uncontrollable ambient signal sources.

References

[1]

{n. d.}. http://tinyos.stanford.edu/tinyos-wiki/index.php/TelosB.

[2]

Heba Abdelnasser, Khaled A. Harras, and Moustafa Youssef. 2015. UbiBreathe: A Ubiquitous non-Invasive WiFi-based Breathing Estimator. In MobiHoc.

Digital Library

[3]

Heba Abdelnasser, Moustafa Youssef, and Khaled A Harras. 2015. Wigest: A ubiquitous wifi-based gesture recognition system. In INFOCOM.

[4]

Fadel Adib, Zachary Kabelac, Dina Katabi, and Robert C. Miller. 2014. 3D Tracking via Body Radio Reflections. In NSDI.

Digital Library

[5]

Fadel Adib, Hongzi Mao, Zachary Kabelac, Dina Katabi, and Robert C. Miller. 2015. Smart Homes That Monitor Breathing and Heart Rate. In CHI.

Digital Library

[6]

J. K. Aggarwal and M. S. Ryoo. 2011. Human Activity Analysis: A Review. In ACM Comput. Surveys.

Digital Library

[7]

Kamran Ali, Alex X. Liu, Wei Wang, and Muhammad Shahzad. 2015. Keystroke Recognition Using WiFi Signals. In MobiCom.

Digital Library

[8]

Z. Chi, Z. Huang, Y. Yao, T. Xie, H. Sun, and T. Zhu. 2017. EMF: Embedding multiple flows of information in existing traffic for concurrent communication among heterogeneous IoT devices. In IEEE INFOCOM 2017 - IEEE Conference on Computer Communications. 1--9.

[9]

Zicheng Chi, Yan Li, Hongyu Sun, Yao Yao, Zheng Lu, and Ting Zhu. 2016. B2W2: N-Way Concurrent Communication for IoT Devices. In Proceedings of the 14th ACM Conference on Embedded Network Sensor Systems CD-ROM (SenSys '16). ACM, New York, NY, USA, 245--258.

Digital Library

[10]

Z. Chi, Y. Li, Y. Yao, and T. Zhu. 2017. PMC: Parallel multi-protocol communication to heterogeneous IoT radios within a single WiFi channel. In 2017 IEEE 25th International Conference on Network Protocols (ICNP). 1--10.

[11]

Zicheng Chi, Yao Yao, Tiantian Xie, Zhichuan Huang, Michael Hammond, and Ting Zhu. 2016. Harmony: Exploiting coarse-grained received signal strength from IoT devices for human activity recognition. In ICNP.

[12]

Saurabh Ganeriwal, Ram Kumar, and Mani B. Srivastava. 2003. Timing-sync Protocol for Sensor Networks. In SenSys.

Digital Library

[13]

T. Hao, R. Zhou, G. Xing, and M. Mutka. 2011. WizSync: Exploiting Wi-Fi Infrastructure for Clock Synchronization in Wireless Sensor Networks. In 2011 IEEE 32nd Real-Time Systems Symposium. 149--158.

Digital Library

[14]

Mohamed Abudulaziz Ali Haseeb and Ramviyas Parasuraman. 2017. Wisture: RNN-based Learning of Wireless Signals for Gesture Recognition in Unmodified Smartphones. CoRR abs/1707.08569 (2017). arXiv:1707.08569 http://arxiv.org/abs/1707.08569

[15]

Enamul Hoque and John Stankovic. 2012. AALO: Activity recognition in smart homes using Active Learning in the presence of Overlapped activities. In PervasiveHealth.

[16]

Ossi Johannes Kaltiokallio, Hüseyin Yigitler, Riku Jäntti, and Neal Patwari. 2014. Non-invasive Respiration Rate Monitoring Using a Single COTS TX-RX Pair. In IPSN.

Digital Library

[17]

B. Li, M. Ayazoglu, T. Mao, O. I. Camps, and M. Sznaier. 2011. Activity recognition using dynamic subspace angles. In CVPR 2011. 3193--3200.

Digital Library

[18]

S. Li, A. Ashok, Y. Zhang, C. Xu, J. Lindqvist, and M. Gruteser. 2016. Whose move is it anyway? Authenticating smart wearable devices using unique head movement patterns. In 2016 IEEE International Conference on Pervasive Computing and Communications (PerCom). 1--9.

[19]

Yan Li, Zicheng Chi, Xin Liu, and Ting Zhu. 2018. Chiron: Concurrent High Throughput Communication for IoT Devices. In Proceedings of the 16th Annual International Conference on Mobile Systems, Applications, and Services (MobiSys '18). ACM, New York, NY, USA, 204--216.

Digital Library

[20]

Yan Li and Ting Zhu. 2016. Gait-Based Wi-Fi Signatures for Privacy-Preserving. In Proceedings of the 11th ACM on Asia Conference on Computer and Communications Security (ASIA CCS '16). ACM, New York, NY, USA, 571--582.

Digital Library

[21]

Y. Li and T. Zhu. 2016. Using Wi-Fi Signals to Characterize Human Gait for Identification and Activity Monitoring. In 2016 IEEE First International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE). 238--247.

[22]

A. A. Liu, N. Xu, W. Z. Nie, Y. T. Su, Y. Wong, and M. Kankanhalli. 2017. Benchmarking a Multimodal and Multiview and Interactive Dataset for Human Action Recognition. IEEE Transactions on Cybernetics 47, 7 (July 2017), 1781--1794.

[23]

R Lovelace, J Sutton, and E Salpeter. 1969. Digital Search Methods for Pulsars. In Nature.

[24]

Miklós Maróti, Branislav Kusy, Gyula Simon, and Ákos Lédeczi. 2004. The Flooding Time Synchronization Protocol. In SenSys.

[25]

Matthai Philipose Michael Buettner, Richa Prasad and David Wetherall. 2009. Recognizing Daily Activities with RFID-Based Sensors. In UbiComp.

[26]

Homin Park, Jongjun Park, Hyunhak Kim, Jongarm Jun, Sang Hyuk Son, Taejoon Park, and JeongGil Ko. 2015. ReLiSCE: utilizing resource-limited sensors for office activity context extraction. In IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[27]

Nam Pham and Tarek Abdelzaher. 2008. Robust Dynamic Human Activity Recognition Based on Relative Energy Allocation. In Distributed Computing in Sensor Systems, Sotiris E. Nikoletseas, Bogdan S. Chlebus, David B. Johnson, and Bhaskar Krishnamachari (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 525--530.

Digital Library

[28]

Zhou Shangguan, L. and Z. and Jamieson K. 2017. Enabling Gesture-based Interactions with Objects. In MobiSys.

Digital Library

[29]

S. Sigg, U. Blanke, and G. Tröster. 2014. The telepathic phone: Frictionless activity recognition from WiFi-RSSI. In PerCom.

[30]

Li Sun, Souvik Sen, Dimitrios Koutsonikolas, and Kyu-Han Kim. 2015. WiDraw: Enabling Hands-free Drawing in the Air on Commodity WiFi Devices. In MobiCom 2015.

Digital Library

[31]

Cisco Systems. 2014--2019. Cisco Global Cloud Index: Forecast and Methodology, 2014-2019 White Paper.

[32]

Guanhua Wang, Yongpan Zou, Zimu Zhou, Kaishun Wu, and Lionel M. Ni. 2014. We Can Hear You with Wi-Fi!. In MobiCom.

Digital Library

[33]

Wei Wang, Alex X. Liu, Muhammad Shahzad, Kang Ling, and Sanglu Lu. 2015. Understanding and Modeling of WiFi Signal Based Human Activity Recognition. In MobiCom.

Digital Library

[34]

W. Wang, T. Xie, X. Liu, and T. Zhu. 2018. ECT: Exploiting Cross-Technology Concurrent Transmission for Reducing Packet Delivery Delay in IoT Networks. In IEEE INFOCOM 2018 - IEEE Conference on Computer Communications.

[35]

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 MobiCom.

Digital Library

[36]

Y. Xiong, R. Zhou, M. Li, G. Xing, L. Sun, and J. Ma. 2014. ZiFi: Exploiting Cross-Technology Interference Signatures for Wireless LAN Discovery. IEEE Transactions on Mobile Computing 13, 11 (Nov 2014), 2675--2688.

[37]

Y. Yao, Y. Li, X. Liu, Z. Chi, W. Wang, T. Xie, and T. Zhu. 2018. Aegis: An Interference-Negligible RF Sensing Shield. In IEEE INFOCOM 2018 - IEEE Conference on Computer Communications.

[38]

Ouyang Zhang and Kannan Srinivasan. 2016. Mudra: User-friendly Fine-grained Gesture Recognition Using WiFi Signals. In CoNext.

Digital Library

Cited By

Zhang XHu QXiao ZSun TZhang JZhang JLi Z(2025)Few-Shot Adaptation to Unseen Conditions for Wireless-Based Human Activity Recognition Without Fine-TuningIEEE Transactions on Mobile Computing10.1109/TMC.2024.346246624:2(585-599)Online publication date: Feb-2025
https://doi.org/10.1109/TMC.2024.3462466
Ghosian Moghaddam MAsghar Nazari Shirehjini AShirmohammadi S(2025)Device-Free Human Activity Recognition: A Systematic Literature ReviewIEEE Open Journal of Instrumentation and Measurement10.1109/OJIM.2024.35028854(1-34)Online publication date: 2025
https://doi.org/10.1109/OJIM.2024.3502885
Srivastava TWinters RGable TWang YLaScala TTashev I(2024)Whispering Wearables: Multimodal Approach to Silent Speech Recognition with Head-Worn DevicesProceedings of the 26th International Conference on Multimodal Interaction10.1145/3678957.3685720(214-223)Online publication date: 4-Nov-2024
https://dl.acm.org/doi/10.1145/3678957.3685720
Show More Cited By

Index Terms

EAR: Exploiting Uncontrollable Ambient RF Signals in Heterogeneous Networks for Gesture Recognition
1. Computer systems organization
  1. Embedded and cyber-physical systems
    1. Sensor networks
2. Human-centered computing
  1. Interaction design
    1. Interaction design process and methods
      1. User interface design

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Published In

cover image ACM Conferences

SenSys '18: Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems

November 2018

449 pages

ISBN:9781450359528

DOI:10.1145/3274783

Editors:
Gowri Sankar Ramachandran
University of Southern California, Los Angeles
,
Bhaskar Krishnamachari
University of Southern California, Los Angeles

Copyright © 2018 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]

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Published: 04 November 2018

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National Science Foundation

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SenSys '18

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SenSys '18: The 16th ACM Conference on Embedded Networked Sensor Systems

November 4 - 7, 2018

Shenzhen, China

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Cited By

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https://doi.org/10.1109/TMC.2024.3462466
Ghosian Moghaddam MAsghar Nazari Shirehjini AShirmohammadi S(2025)Device-Free Human Activity Recognition: A Systematic Literature ReviewIEEE Open Journal of Instrumentation and Measurement10.1109/OJIM.2024.35028854(1-34)Online publication date: 2025
https://doi.org/10.1109/OJIM.2024.3502885
Srivastava TWinters RGable TWang YLaScala TTashev I(2024)Whispering Wearables: Multimodal Approach to Silent Speech Recognition with Head-Worn DevicesProceedings of the 26th International Conference on Multimodal Interaction10.1145/3678957.3685720(214-223)Online publication date: 4-Nov-2024
https://dl.acm.org/doi/10.1145/3678957.3685720
Weng YWu GZheng TYang YLuo JShu YLiu JTan RHe YChen J(2024)Large Model for Small Data: Foundation Model for Cross-Modal RF Human Activity RecognitionProceedings of the 22nd ACM Conference on Embedded Networked Sensor Systems10.1145/3666025.3699349(436-449)Online publication date: 4-Nov-2024
https://dl.acm.org/doi/10.1145/3666025.3699349
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Wang WLiu XYao YChi ZRay SZhu TZhang Y(2023)Simultaneous Data Dissemination Among WiFi and ZigBee DevicesIEEE/ACM Transactions on Networking10.1109/TNET.2023.324307031:6(2545-2558)Online publication date: Dec-2023
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Islam MYu LAbuella HO'Hara JCrick CEkin S(2023)Hand Gesture Recognition through Reflected Infrared Light Wave Signals2023 10th International Conference on Electrical and Electronics Engineering (ICEEE)10.1109/ICEEE59925.2023.00008(1-5)Online publication date: 8-May-2023
https://doi.org/10.1109/ICEEE59925.2023.00008
Yang DKumar ARay SWang WTourani R(2023)IoT Sentinel: Correlation-based Attack Detection, Localization, and Authentication in IoT Networks2023 32nd International Conference on Computer Communications and Networks (ICCCN)10.1109/ICCCN58024.2023.10230209(1-10)Online publication date: Jul-2023
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Xiao JLi HWu MJin HDeen MCao J(2022)A Survey on Wireless Device-free Human Sensing: Application Scenarios, Current Solutions, and Open IssuesACM Computing Surveys10.1145/353068255:5(1-35)Online publication date: 3-Dec-2022
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