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Intelligent and Converged Networks 2023, 4(1): 15-27 https://doi.org/10.23919/ICN.2023.0002
Open Access | Issue | Published: 20 March 2023

Research on recognition algorithm for gesture page turning based on wireless sensing

Show Author's Information Lin Tang1,  Sumin Wang1,2,  Meng Zhou1,  Yinfan Ding1,  Chao Wang1( ),  Shengbo Wang4,  Zhen Sun5,  Jie Wu3
Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, Shanghai Institute for Advanced Communication and Data Science, School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
School of Electronic Information Engineering, Gannan University of Science and Technology, Ganzhou 341000, China
Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122-6096, USA
Affiliated High School of Peking University, Beijing 100086, China
Peking University & National Engineering Laboratory for Big Data Analysis and Applications, Beijing 100871, China
Keywords:
wireless sensing, Channel State Information (CSI), Wi-Fi signal, human behavior recognition
Cite this article:
Tang L, Wang S, Zhou M, et al. Research on recognition algorithm for gesture page turning based on wireless sensing. Intelligent and Converged Networks, 2023, 4(1): 15-27. https://doi.org/10.23919/ICN.2023.0002
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Abstract Full text About this article

When a human body moves within the coverage range of Wi-Fi signals, the reflected Wi-Fi signals by the various parts of the human body change the propagation path, so analysis of the channel state data can achieve the perception of the human motion. By extracting the Channel State Information (CSI) related to human motion from the Wi-Fi signals and analyzing it with the introduced machine learning classification algorithm, the human motion in the spatial environment can be perceived. On the basis of this theory, this paper proposed an algorithm of human behavior recognition based on CSI wireless sensing to realize deviceless and over-the-air slide turning. This algorithm collects the environmental information containing upward or downward wave in a conference room scene, uses the local outlier factor detection algorithm to segment the actions, and then the time domain features are extracted to train Support Vector Machine (SVM) and eXtreme Gradient Boosting (XGBoost) classification modules. The experimental results show that the average accuracy of the XGBoost module sensing slide flipping can reach 94%, and the SVM module can reach 89%, so the module could be extended to the field of smart classroom and significantly improve speech efficiency.


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About this article

Research on recognition algorithm for gesture page turning based on wireless sensing

Show Author's information Lin Tang1, Sumin Wang1,2, Meng Zhou1, Yinfan Ding1, Chao Wang1( ), Shengbo Wang4, Zhen Sun5, Jie Wu3
Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, Shanghai Institute for Advanced Communication and Data Science, School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
School of Electronic Information Engineering, Gannan University of Science and Technology, Ganzhou 341000, China
Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122-6096, USA
Affiliated High School of Peking University, Beijing 100086, China
Peking University & National Engineering Laboratory for Big Data Analysis and Applications, Beijing 100871, China

Abstract

When a human body moves within the coverage range of Wi-Fi signals, the reflected Wi-Fi signals by the various parts of the human body change the propagation path, so analysis of the channel state data can achieve the perception of the human motion. By extracting the Channel State Information (CSI) related to human motion from the Wi-Fi signals and analyzing it with the introduced machine learning classification algorithm, the human motion in the spatial environment can be perceived. On the basis of this theory, this paper proposed an algorithm of human behavior recognition based on CSI wireless sensing to realize deviceless and over-the-air slide turning. This algorithm collects the environmental information containing upward or downward wave in a conference room scene, uses the local outlier factor detection algorithm to segment the actions, and then the time domain features are extracted to train Support Vector Machine (SVM) and eXtreme Gradient Boosting (XGBoost) classification modules. The experimental results show that the average accuracy of the XGBoost module sensing slide flipping can reach 94%, and the SVM module can reach 89%, so the module could be extended to the field of smart classroom and significantly improve speech efficiency.

Keywords: wireless sensing, Channel State Information (CSI), Wi-Fi signal, human behavior recognition

References(22)

[1]

C. Wang, S. Chen, Y. Yang, F. Hu, F. Liu, and J. Wu, Literature review on wireless sensing-Wi-Fi signal-based recognition of human activities, Tsinghua Science and Technollogy, vol. 23, no. 2, pp. 203–222, 2018.
DOI Google Scholar
[2]

S. Yue, Y. Yang, H. Wang, H. Rahul, and D. Katabi, BodyCompass: Monitoring sleep posture with wireless signals, Proc. ACM Interact. Mob. Wearable Ubiquit. Technol., vol. 4, no. 2, pp. 1–25, 2020.
DOI Google Scholar
[3]
L. Fan, T. Li, R. Fang, R. Hristov, Y. Yuan, and D. Katabi, Learning longterm representations for person re-identification using radio signals, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Virtual Event, 2020, pp. 10699−10709.
DOI
[4]
D. Katabi, Keynote: Monitoring people and their vital signs using radio signals and machine learning, in Proc. of 2021 IEEE International Conference on Pervasive Computing and Communications (PerCom), Kassel, Germany, 2021, p. 1.
DOI
[5]

K. Qian, C. Wu, Z. Yang, Y. Liu, F. He, and T. Xing, Enabling contactless detection of moving humans with dynamic speeds using CSI, ACM Trans. Embedd. Comput. Syst., vol. 17, no. 2, pp. 1–18, 2018.
DOI Google Scholar
[6]

T. Hang, Y. Zheng, K. Qian, C. Wu, Z. Yang, X. Zhou, Y. Liu, and G. Chen, WiSH: WiFi-based real-time human detection, Tsinghua Science and Technollogy, vol. 24, no. 5, pp. 615–629, 2019.
DOI Google Scholar
[7]
W. Wang, A. X. Liu, and M. Shahzad, Gait recognition using wifi signals, in Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Heidelberg, Germany, 2016, pp. 363−373.
DOI
[8]
J. Zhang, B. Wei, W. Hu, and S. S. Kanhere, Wifi-id: Human identification using wifi signal, in Proc. of 2016 International Conference on Distributed Computing in Sensor Systems (DCOSS), Washington, DC, USA, 2016, pp. 75−82.
DOI
[9]
T. Xin, B. Guo, Z. Wang, M. Li, Z. Yu, and X. Zhou, Freesense: Indoor human identification with Wi-Fi signals, in Proc. of 2016 IEEE Global Communications Conference (GLOBECOM), Washington, DC, USA, 2016, pp. 1−7.
DOI
[10]

L. Zhang, C. Wang, M. Ma, and D. Zhang, WiDIGR: Direction-independent gait recognition system using commercial Wi-Fi devices, IEEE Intern. Things J., vol. 7, no. 2, pp. 1178–1191, 2019.
DOI Google Scholar
[11]

B. Yu, Y. Wang, K. Niu, Y. Zeng, T. Gu, L. Wang, C. Guan, and D. Zhang, WiFi-Sleep: Sleep stage monitoring using commodity Wi-Fi devices, IEEE Intern. Things J., vol. 8, no. 18, pp. 13900–13913, 2021.
DOI Google Scholar
[12]
Y. Zeng, P. H. Pathak, and P. Mohapatra, WiWho: WiFi-based person identification in smart spaces, in Proc. of 2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN), Vienna, Austria, 2016, pp. 1−12.
DOI
[13]
IEEE, IEEE standard for information technology--local and metropolitan area networks--specific requirements--Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) specifications. Amendment 5: Enhancements for higher throughput, 802.11n-2009, https://standards.ieee.org/ieee/802.11n/3952/, 2009.
[14]
H. Abdel-Nasser, R. Samir, I. Sabek, and M. Youssef, MonoPHY: Mono-stream-based device-free WLAN localization via physical layer information, in Proc. of 2013 IEEE Wireless Communications and Networking Conference (WCNC), Shanghai, China, 2013, pp. 4546−4551.
DOI
[15]

H. Chen, Y. Zhang, W. Li, X. Tao, and P. Zhang, ConFi: Convolutional neural networks based indoor Wi-Fi localization using channel state information, IEEE Access, vol. 5, pp. 18066–18074, 2017.
DOI Google Scholar
[16]

J. Gu, J. Wang, L. Zhang, Z. Yu, X. Xin, and Y. Liu, Spotlight: Hot target discovery and localization with crowdsourced photos, Tsinghua Science and Technollogy, vol. 25, no. 1, pp. 68–80, 2019.
DOI Google Scholar
[17]

Z. Song, Z. Cao, Z. Li, J. Wang, and Y. Liu, Inertial motion tracking on mobile and wearable devices: Recent advancements and challenges, Tsinghua Science and Technollogy, vol. 26, no. 5, pp. 692–705, 2021.
DOI Google Scholar
[18]

Z. Zhang, X. Cong, W. Feng, H. Zhang, G. Fu, and J. Chen, WAEAS: An optimization scheme of EAS scheduler for wearable applications, Tsinghua Science and Technollogy, vol. 26, no. 1, pp. 72–84, 2020.
DOI Google Scholar
[19]

X. Zhang, C. Xiu, Y. Wang, and D. Yang, High-precision WiFi indoor localization algorithm based on CSI-XGBoost, J. Beijing Univ. Aeronaut. Astronaut., vol. 44, no. 12, pp. 2536–2544, 2018.
Google Scholar
[20]
M. M. Breunig, H. -P. Kriegel, R. T. Ng, and J. Sander, LOF: Identifying density-based local outliers, in Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, Dallas, Texas, USA, 2000, pp. 93−104.
DOI
[21]

C. C. Chang and C. J. Lin, LIBSVM: A library for support vector machines, ACM Trans. Intell. Syst. Technol., vol. 2, no. 3, pp. 1–27, 2011.
DOI Google Scholar
[22]
T. Chen and C. Guestrin, Xgboost: A scalable tree boosting system, in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 2016, pp. 785−794.
DOI
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Publication history

Received: 08 December 2022
Accepted: 17 January 2023
Published: 20 March 2023
Issue date: March 2023

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Acknowledgment

This work was supported by the Special Zone Project of National Defense Innovation.

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This work is available under the CC BY-NC-ND 3.0 IGO license:https://creativecommons.org/licenses/by-nc-nd/3.0/igo/

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