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.

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

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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.

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Published: 13 April 2015

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

April 13 - 16, 2015

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Cited By

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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
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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
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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
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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
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