PhaseAnti: An Anti-Interference WiFi-Based Activity Recognition System Using Interference-Independent Phase Component
Authors: 
Jinyang Huang, Bin Liu, Chenglin Miao, Yan Lu, Qijia Zheng, Yu Wu, Jiancun Liu, Lu Su, Chang Wen ChenAuthors Info & Claims
Pages 2938 - 2954
Abstract
Driven by a wide range of essential applications, significant achievements have recently been made to explore WiFi-based Human Activity Recognition (HAR) techniques that utilize the information collected by commercial off-the-shelf (COTS) WiFi infrastructures to infer human activities without the need for the subject to carry any devices. Although existing WiFi-based HAR systems achieve satisfactory performance in some instances, they are faced with a severe challenge that the impacts of ubiquitous Co-channel Interference (CCI) on WiFi signals are inevitable. This downgrades the performance of these HAR systems significantly. To address this challenge, we propose PhaseAnti in this paper, a novel WiFi-based HAR system to exploit the CCI-independent phase component, Nonlinear Phase Error Variation (NLPEV), of WiFi Channel State Information (CSI) to cope with the negative effects of CCI. The stability of NLPEV data and the sensibility of this component to motions are rigorously analyzed. Furthermore, validated by extensive properly designed experiments, this phase component across subcarriers is invariant under various CCI scenarios while sufficiently distinct for different motions. Therefore, the NLPEV data can be used and processed effectively to perform HAR in CCI scenarios. Extensive experiments with various daily activities in different indoor rooms demonstrate the superior effectiveness and generalizability of the proposed PhaseAnti system under various CCI scenarios. Specifically, PhaseAnti achieves a <inline-formula><tex-math notation="LaTeX">$ 96.5\%$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>96</mml:mn><mml:mo>.</mml:mo><mml:mn>5</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="liu-ieq1-3127721.gif"/></alternatives></inline-formula> recognition accuracy rate (RAR) on average in different CCI scenarios, which can improve up to a <inline-formula><tex-math notation="LaTeX">$ 16.7\%$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>16</mml:mn><mml:mo>.</mml:mo><mml:mn>7</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="liu-ieq2-3127721.gif"/></alternatives></inline-formula> RAR compared with the amplitude component in the presence of CCI. Furthermore, the recognition speed is 10.3 × faster than the state-of-the-art solution.
References
[1]
O. D. Lara and M. A. Labrador, “A survey on human activity recognition using wearable sensors,” IEEE Commun. Surveys Tuts., vol. 15, no. 3, pp. 1192–1209, Third Quarter 2013.
[2]
W. Zhu, J. Hu, G. Sun, X. Cao, and Y. Qiao, “A key volume mining deep framework for action recognition,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2016, pp. 1991–1999.
[3]
G. Laput and C. Harrison, “Sensing fine-grained hand activity with smartwatches,” in Proc. CHI Conf. Hum. Factors Comput. Syst., 2019, pp. 1–13.
[4]
D. Halperin, W. Hu, A. Sheth, and D. Wetherall, “Tool release: Gathering 802.11 n traces with channel state information,” ACM SIGCOMM Comput. Commun. Rev., vol. 41, no. 1, pp. 53–53, 2011.
[5]
Y. Wang, K. Wu, and L. M. Ni, “WiFall: Device-free fall detection by wireless networks,” IEEE Trans. Mobile Comput., vol. 16, no. 2, pp. 581–594, Feb. 2017.
[6]
Y. Gu, F. Ren, and J. Li, “PAWS: Passive human activity recognition based on WiFi ambient signals,” IEEE Internet of Things J., vol. 3, no. 5, pp. 796–805, Oct. 2016.
[7]
H. Abdelnasser, M. Youssef, and K. A. Harras, “WiGest: A ubiquitous WiFi-based gesture recognition system,” in Proc. IEEE Conf. Comput. Commun., 2015, pp. 1472–1480.
[8]
IEEE Standard for Information Technology, IEEE Standard 802.11n-2009, pp. 1–565, Oct.2009.
[9]
J. Huanget al., “Towards anti-interference human activity recognition based on WiFi subcarrier correlation selection,” IEEE Trans. Veh. Technol., vol. 69, no. 6, pp. 6739–6754, Jun. 2020.
[10]
J. Huang, B. Liu, H. Jin, and Z. Liu, “WiAnti: An anti-interference activity recognition system based on WiFi CSI,” in Proc. IEEE Int. Conf. Internet of Things IEEE Green Comput. Commun. IEEE Cyber Phys. Soc. Comput. IEEE Smart Data, 2018, pp. 58–65.
[11]
K. Ali, A. X. Liu, W. Wang, and M. Shahzad, “Recognizing keystrokes using WiFi devices,” IEEE J. Sel. Areas Commun., vol. 35, no. 5, pp. 1175–1190, May 2017.
[12]
Z. Shi, J. A. Zhang, Y. D. R. Xu, and Q. Cheng, “Environment-robust device-free human activity recognition with channel-state-information enhancement and one-shot learning,” IEEE Trans. Mobile Comput., pp. 1–1, 2020.
[13]
N. Yu, W. Wang, A. X. Liu, and L. Kong, “QGesture: Quantifying gesture distance and direction with WiFi signals,” Proc. ACM Interactive Mobile Wearable Ubiquitous Technol., vol. 2, no. 1, 2018, Art. no.
[14]
Q. Pu, S. Gupta, S. Gollakota, and S. Patel, “Whole-home gesture recognition using wireless signals,” in Proc. 19th Annu. Int. Conf. Mobile Comput. Netw., 2013, pp. 27–38.
[15]
N. Xiao, P. Yang, Y. Yan, H. Zhou, X.-Y. Li, and H. Du, “Motion-Fi+: Recognizing and counting repetitive motions with wireless backscattering,” IEEE Trans. Mobile Comput., vol. 20, no. 5, pp. 1862–1876, May 2021.
[16]
X. Fan, W. Gong, and J. Liu, “TagFree activity identification with RFIDs,” Proc. ACM Interactive Mobile Wearable Ubiquitous Technol., vol. 2, no. 1, 2018, Art. no.
[17]
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 Proc. 20th Annu. Int. Conf. Mobile Comput. Netw., 2014, pp. 617–628.
[18]
W. Wang, A. X. Liu, M. Shahzad, K. Ling, and S. Lu, “Understanding and modeling of WiFi signal based human activity recognition,” in Proc. 21st Annu. Int. Conf. Mobile Comput. Netw., 2015, pp. 65–76.
[19]
D. Wuet al., “FingerDraw: Sub-wavelength level finger motion tracking with WiFi signals,” Proc. ACM Interactive Mobile Wearable Ubiquitous Technol., vol. 4, no. 1, pp. 1–27, 2020.
[20]
Y. Ma, G. Zhou, S. Wang, H. Zhao, and W. Jung, “SignFi: Sign language recognition using WiFi,” Proc. ACM Interactive Mobile Wearable Ubiquitous Technol., vol. 2, no. 1, 2018, Art. no.
[21]
Y. Zhenget al., “Zero-effort cross-domain gesture recognition with Wi-Fi,” in Proc. 17th Annu. Int. Conf. Mobile Syst. Appl. Services, 2019, pp. 313–325.
[22]
S. Palipana, D. Rojas, P. Agrawal, and D. Pesch, “FallDeFi: Ubiquitous fall detection using commodity Wi-Fi devices,” Proc. ACM Interactive Mobile Wearable Ubiquitous Technol., vol. 1, no. 4, pp. 1–25, 2018.
[23]
K. Qian, C. Wu, Z. Zhou, Y. Zheng, Z. Yang, and Y. Liu, “Inferring motion direction using commodity Wi-Fi for interactive exergames,” in Proc. CHI Conf. Hum. Factors Comput. Syst., 2017, pp. 1961–1972.
[24]
X. Wang, C. Yang, and S. Mao, “PhaseBeat: Exploiting CSI phase data for vital sign monitoring with commodity WiFi devices,” in Proc. IEEE 37th Int. Conf. Distrib. Comput. Syst., 2017, pp. 1230–1239.
[25]
Y. Zeng, D. Wu, R. Gao, T. Gu, and D. Zhang, “FullBreathe: Full human respiration detection exploiting complementarity of CSI phase and amplitude of WiFi signals,” Proc. ACM Interactive Mobile Wearable Ubiquitous Technol., vol. 2, no. 3, 2018, Art. no.
[26]
P. Liu, P. Yang, W.-Z. Song, Y. Yan, and X.-Y. Li, “Real-time identification of rogue WiFi connections using environment-independent physical features,” in Proc. IEEE Conf. Comput. Commun., 2019, pp. 190–198.
[27]
Y. Zhuo, H. Zhu, H. Xue, and S. Chang, “Perceiving accurate CSI phases with commodity WiFi devices,” in Proc. IEEE Conf. Comput. Commun., 2017, pp. 1–9.
[28]
J. Hua, H. Sun, Z. Shen, Z. Qian, and S. Zhong, “Accurate and efficient wireless device fingerprinting using channel state information,” in Proc. IEEE Conf. Comput. Commun., 2018, pp. 1700–1708.
[29]
J. S. Hunter, “The exponentially weighted moving average,” J. Qual. Technol., vol. 18, no. 4, pp. 203–210, 1986.
[30]
R. K. Pearson, “Outliers in process modeling and identification,” IEEE Trans. Control Syst. Technol., vol. 10, no. 1, pp. 55–63, Jan. 2002.
[31]
J.-S. R. Jang, “Machine learning toolbox,” 2014. [Online]. Available: mirlab.org/jang/matlab/toolbox/machineLearning, (Dec 1, 2014).
[32]
Y. Liu and S. Liao, “Preventing over-fitting of cross-validation with kernel stability,” in Proc. Joint Eur. Conf. Mach. Learn. Knowl. Discov. Databases, 2014, pp. 290–305.
[33]
Z. Guo, F. Xiao, B. Sheng, H. Fei, and S. Yu, “WiReader: Adaptive air handwriting recognition based on commercial WiFi signal,” IEEE Internet of Things J., vol. 7, no. 10, pp. 10 483–10 494, Oct. 2020.
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Published: 01 May 2023
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