Skip to main content
SpringerLink
Log in

WiPD: A Robust Framework for Phase Difference-based Activity Recognition

  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

Abstract

Using Wi-Fi signals to sense target activity is a promising study field, accounting for convenience concerns. However, it remains challenging to recognize target activity as a way of high-precision and stability due to the multi-path effect in Wi-Fi signals. In this paper, we propose a robust framework named WiPD, for accurate activity recognition based on Wi-Fi phase difference data. Firstly, a novel feature representation mechanism named visualized spectrum matrix (VSM) for Wi-Fi activity recognition is proposed. VSM is generated by performing a Short Time Fourier Transform operation on Wi-Fi phase difference data. Then, we design a neural network with the input type of VSM, namely, WiPD-Net, in which the activity features are extracted by both four convolutional neural network submodules and two WiPD-Block submodules. Experiment results show that our proposed WiPD-Net outperforms the existing baselines on our dataset and one public dataset. In particular, WiPD-Net can reach up to an accuracy of 99.80%, and achieve a good migration performance among five Wi-Fi environments.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

References

  1. Wang Z, She Q, Smolic A (2021) Action-net: Multipath excitation for action recognition. In: Proc IEEE CVPR, pp 13,214–13,223. https://doi.org/10.1109/CVPR46437.2021.01301

  2. Munro J, Damen D (2020) Multi-modal domain adaptation for fine-grained action recognition. In: Proc IEEE CVPR, pp 119–129. https://doi.org/10.1109/CVPR42600.2020.00020

  3. Sheng T, Huber M (2020) Weakly supervised multi-task representation learning for human activity analysis using wearables. Proc ACM Interact Mob Wearable Ubiquitous Technol 4(2):57:1–57:18. https://doi.org/10.1145/3397330

    Article  Google Scholar 

  4. Ma C, Li W, Gravina R, et al. (2017) Activity level assessment using a smart cushion for people with a sedentary lifestyle. Sensors 17(10):2269. https://doi.org/10.3390/s17102269

    Article  Google Scholar 

  5. Yang Y, Cao J, Liu X (2019) Er-rhythm: Coupling exercise and respiration rhythm using lightweight COTS RFID. Proc ACM Interact Mob Wearable Ubiquitous Technol 3(4):158:1–158:24. https://doi.org/10.1145/3369808

    Article  Google Scholar 

  6. Mbarek B, Ge M, Pitner T (2020) An efficient mutual authentication scheme for internet of things. IEEE Internet Things J 9:100,160. https://doi.org/10.1016/j.iot.2020.100160

    Article  Google Scholar 

  7. Gu Y, Wang Y, Liu Z, et al. (2020) Sleepguardian: an rf-based healthcare system guarding your sleep from afar. IEEE Netw 34(2):164–171. https://doi.org/10.1109/MNET.001.1900235

    Article  Google Scholar 

  8. Zhao M, Li T, Alsheikh MA et al (2018) Through-wall human pose estimation using radio signals. In: Proc. IEEE CVPR, pp 7356–7365. https://doi.org/10.1109/CVPR.2018.00768

  9. Index CVN (2017) Cisco visual networking index: Global mobile data traffic forecast update, 2016–2021 white paper

  10. Bahl P, Padmanabhan VN (2000) RADAR: an in-building rf-based user location and tracking system. In: Proc IEEE INFOCOM, pp 775–784. https://doi.org/10.1109/INFCOM.2000.832252

  11. Cao Y, Wang F, Lu X, et al. (2020) Contactless body movement recognition during sleep via wifi signals. IEEE Internet Things J 7(3):2028–2037. https://doi.org/10.1109/JIOT.2019.2960823

    Article  Google Scholar 

  12. Yousefi S, Narui H, Dayal S, et al. (2017) A survey on behavior recognition using wifi channel state information. IEEE Commun Mag 55(10):98–104. https://doi.org/10.1109/MCOM.2017.1700082

    Article  Google Scholar 

  13. Chen Z, Zhang L, Jiang C, et al. (2019) Wifi CSI based passive human activity recognition using attention based BLSTM. IEEE Trans Mob Comput 18(11):2714–2724. https://doi.org/10.1109/TMC.2018.2878233

    Article  Google Scholar 

  14. Wang F, Gong W, Liu J (2019) On spatial diversity in wifi-based human activity recognition: a deep learning-based approach. IEEE Internet Things J 6(2):2035–2047. https://doi.org/10.1109/JIOT.2018.2871445

    Article  Google Scholar 

  15. Halperin D, Hu W, Sheth A, et al. (2011) Tool release: Gathering 802.11 n traces with channel state information. ACM Sigcomm Comp Com 41(1):53–53. https://doi.org/10.1145/1925861.1925870

    Article  Google Scholar 

  16. Xingda YU, Chen W, Wang D et al (2019) A deep learning algorithm for contactless human identification. J Xi’an Jiaotong Univ 53:122–127. https://doi.org/10.7652/xjtuxb201904018

    Article  Google Scholar 

  17. Huang M, Liu J, Zhang Y et al (2019) Passive falling detection method based on wireless channel state information. Journal of Computer Applications

  18. Zhou ZY (2019) Lightweight gait recognition model lwid based on wifi signal. Comput Sci 47 (11):25–31. https://doi.org/10.11896/jsjkx.200200044

    Article  Google Scholar 

  19. Wang X, Yang C, Mao S (2017) Tensorbeat: Tensor decomposition for monitoring multi-person breathing beats with commodity wifi. arXiv:1702.02046. https://doi.org/10.1145/3078855

  20. Wang H, Zhang D, Wang Y, et al. (2017) Rt-fall: a real-time and contactless fall detection system with commodity wifi devices. IEEE Trans Mob Comput 16(2):511–526. https://doi.org/10.1109/TMC.2016.2557795

    Article  MathSciNet  Google Scholar 

  21. Xin T, Guo B, Wang Z et al (2018) Freesense: A robust approach for indoor human detection using wi-fi signals. Proc ACM Interact Mob Wearable Ubiquitous Technol 2 (3):143:1–143:23. https://doi.org/10.1145/3264953

    Article  Google Scholar 

  22. Xiao N, Yang P, Li X et al (2019) Milliback: Real-time plug-n-play millimeter level tracking using wireless backscattering. Proc ACM Interact Mob Wearable Ubiquitous Technol 3(3):112:1–112:23. https://doi.org/10.1145/3351270

    Article  Google Scholar 

  23. Zhang D, Hu Y, Chen Y, et al. (2019) Breathtrack: Tracking indoor human breath status via commodity wifi. IEEE Internet Things J, pp 3899–3911. https://doi.org/10.1109/JIOT.2019.2893330

  24. Tian Z, Yang W, Jin Y, et al. (2019) Mfpl: Multi-frequency phase difference combination based device-free localization. Comput Mater Continua 61(3):861–876. https://doi.org/10.32604/cmc.2020.07297

    Article  Google Scholar 

  25. Zeng Y, Wu D, Gao R et al (2018) Fullbreathe: Full human respiration detection exploiting complementarity of CSI phase and amplitude of wifi signals. Proc ACM Interact Mob Wearable Ubiquitous Technol 2 (3):148:1–148:19. https://doi.org/10.1145/3264958

    Article  Google Scholar 

  26. Zeng Y, Wu D, Xiong J et al (2019) Farsense: Pushing the range limit of wifi-based respiration sensing with CSI ratio of two antennas. Proc ACM Interact Mob Wearable Ubiquitous Technol 3(3):121:1–121:26. https://doi.org/10.1145/3351279

    Article  Google Scholar 

  27. Li S, Liu Z, Zhang Y et al (2020) Wiborder: Precise wi-fi based boundary sensing via through-wall discrimination. Proc ACM Interact Mob Wearable Ubiquitous Technol 4(3):89:1–89:30. https://doi.org/10.1145/3411834

    Article  Google Scholar 

  28. Wang X, Gao L, Mao S et al (2016) CSI-Based fingerprinting for indoor localization: A deep learning approach. IEEE Trans Veh Technol 66(1):763–776. https://doi.org/10.1109/TVT.2016.2545523

    Article  Google Scholar 

  29. Wang X, Yang C, Mao S (2017) Phasebeat: Exploiting CSI phase data for vital sign monitoring with commodity wifi devices. In: Proc IEEE ICDCS, pp 1230–1239. https://doi.org/10.1109/ICDCS.2017.206

  30. Kiymik M K, Güler I, Dizibüyük A, et al. (2005) Comparison of STFT and wavelet transform methods in determining epileptic seizure activity in EEG signals for real-time application. Comput Biol Medicine 35(7):603–616. https://doi.org/10.1016/j.compbiomed.2004.05.001

    Article  Google Scholar 

  31. Ioffe S, Szegedy C (2015) Batch normalization: Accelerating deep network training by reducing internal covariate shift. CoRR arXiv:1502.03167, http://proceedings.mlr.press/v37/ioffe15.html

  32. He K, Zhang X, Ren S et al (2016) Deep residual learning for image recognition. In: Proc IEEE CVPR, pp 770–778. https://doi.org/10.1109/CVPR.2016.90

  33. Bu Q, Ming X, Hu J et al (2021) Transfersense: towards environment independent and one-shot wifi sensing. In: Proc ACM Interact Mob Wearable Ubiquitous Technol (1). https://doi.org/10.1007/s00779-020-01480-6

  34. Gu F, Chung M, Chignell MH, et al. (2022) A survey on deep learning for human activity recognition. ACM Comput Surv 54(8):177:1–177:34. https://doi.org/10.1145/3472290

    Article  Google Scholar 

  35. Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: arXiv:1409.1556

  36. Howard AG, Zhu M, Chen B et al (2017) Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv:1704.04861

  37. Sandler M, Howard AG, Zhu M et al (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proc IEEE CVPR, pp 4510–4520. https://doi.org/10.1109/CVPR.2018.00474

Download references

Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under Grant 61972092; in part by the Research Foundation Plan in Higher Education Institutions of Henan Province under Grant 21A520043; and in part by the Collaborative Innovation Major Project of Zhengzhou under Grant 20XTZX06013.

The authors have no conflicts of interest to declare that are relevant to t he content of this article.

Author information

Authors and Affiliations

  1. School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, China

    Pengsong Duan

  2. School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou, China

    Pengsong Duan, Chen Li & Bo Zhang

  3. Inspur Group, Jinan, China

    Endong Wang

Authors
  1. Pengsong Duan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chen Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bo Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Endong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bo Zhang.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Duan, P., Li, C., Zhang, B. et al. WiPD: A Robust Framework for Phase Difference-based Activity Recognition. Mobile Netw Appl 27, 2280–2291 (2022). https://doi.org/10.1007/s11036-022-02007-4

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11036-022-02007-4

Keywords

Search

Navigation