Cross-scene passive human activity recognition using commodity WiFi | Frontiers of Computer Science Skip to main content
Springer Nature Link
Log in

Cross-scene passive human activity recognition using commodity WiFi

  • Research Article
  • Published:
Frontiers of Computer Science Aims and scope Submit manuscript

Abstract

With the development of the Internet of Things (IoT) and the popularization of commercial WiFi, researchers have begun to use commercial WiFi for human activity recognition in the past decade. However, cross-scene activity recognition is still difficult due to the different distribution of samples in different scenes. To solve this problem, we try to build a cross-scene activity recognition system based on commercial WiFi. Firstly, we use commercial WiFi devices to collect channel state information (CSI) data and use the Bi-directional long short-term memory (BiLSTM) network to train the activity recognition model. Then, we use the transfer learning mechanism to transfer the model to fit another scene. Finally, we conduct experiments to evaluate the performance of our system, and the experimental results verify the accuracy and robustness of our proposed system. For the source scene, the accuracy of the model trained from scratch can achieve over 90%. After transfer learning, the accuracy of cross-scene activity recognition in the target scene can still reach 90%.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Chen Y, Yu L, Ota K, Dong M. Robust activity recognition for aging society. IEEE Journal of Biomedical and Health Informatics, 2018, 22(6): 1754–1764

    Article  Google Scholar 

  2. Yousefi S, Narui H, Dayal S, Ermon S, Valaee S. A survey on behavior recognition using wifi channel state information. IEEE Communications Magazine, 2017, 55: 98–104

    Article  Google Scholar 

  3. Wu K, Xiao J, Yi Y, Chen D, Luo X, Ni L M. CSI-based indoor localization. IEEE Transactions on Parallel and Distributed Systems, 2012, 24: 1300–1309

    Article  Google Scholar 

  4. Wang C, Chen S, Yang Y, Hu F, Liu F, Wu J. Literature review on wireless sensing-Wi-Fi signal-based recognition of human activities. Tsinghua Science and Technology, 2018, 23: 203–222

    Article  Google Scholar 

  5. Paul A S, Wan E A. RSSI-based indoor localization and tracking using sigma-point Kalman smoothers. IEEE Journal of Selected Topics in Signal Processing, 2009, 3: 860–873

    Article  Google Scholar 

  6. Xiao J, Wu K, Yi Y, Wang L, Ni L M. Pilot: passive device-free indoor localization using channel state information. In: Proceedings of the 33rd IEEE International Conference on Distributed Computing Systems. 2013, 236–245

  7. Wang X, Gao L, Mao S, Pandey S. CSI-based fingerprinting for indoor localization: a deep learning approach. IEEE Transactions on Vehicular Technology, 2016, 66: 763–776

    Google Scholar 

  8. Qian K, Wu C, Yang Z, Liu Y, Jamieson K. Widar: decimeter-level passive tracking via velocity monitoring with commodity Wi-Fi. In: Proceedings of the 18th ACM International Symposium on Mobile Ad Hoc Networking and Computing. 2017, 1–10

  9. Qian K, Wu C, Zhang Y, Zhang G, Yang Z, Liu Y. Widar2. 0: passive human tracking with a single Wi-Fi link. In: Proceedings of the 16th Annual International Conference on Mobile Systems, Applications, and Services. 2018, 350–361

  10. Pu Q, Gupta S, Gollakota S, Patel S. Whole-home gesture recognition using wireless signals. In: Proceedings of the 19th Annual International Conference on Mobile Computing & Networking. 2013, 27–38

  11. Wang Y, Wu K, Ni L M. Wifall: device-free fall detection by wireless networks. IEEE Transactions on Mobile Computing, 2016, 16: 581–594

    Article  Google Scholar 

  12. Wang H, Zhang D, Wang Y, Ma J, Wang Y, Li S. RT-Fall: a real-time and contactless fall detection system with commodity WiFi devices. IEEE Transactions on Mobile Computing, 2016, 16: 511–526

    Article  Google Scholar 

  13. Wang G, Zou Y, Zhou Z, Wu K, Ni L M. We can hear you with Wi-Fi!. IEEE Transactions on Mobile Computing, 2016, 15: 2907–2920

    Article  Google Scholar 

  14. Zeng Y, Pathak P H, Mohapatra P. WiWho: wifi-based person identification in smart spaces. In: Proceedings of the 15th ACM/IEEE International Conference on Information Processing in Sensor Networks. 2016, 1–12

  15. Wu X, Chu Z, Yang P, Xiang C, Zheng X, Huang W. TW-See: human activity recognition through the wall with commodity Wi-Fi devices. IEEE Transactions on Vehicular Technology, 2018, 68: 306–319

    Article  Google Scholar 

  16. Li H, Ota K, Dong M, Guo M. Learning human activities through Wi-Fi channel state information with multiple access points. IEEE Communications Magazine, 2018, 56(5): 124–129

    Article  Google Scholar 

  17. Zhang J, Tang Z, Li M, Fang D, Nurmi P, Wang Z. CrossSense: Towards cross-site and large-scale WiFi sensing. In: Proceedings of the 24th Annual International Conference on Mobile Computing and Networking. 2018, 305–320

  18. Jiang W, Miao C, Ma F, Yao S, Wnag Y, Yuan Y, Xue H, Song C, Ma X, Koutsonikolas D, Xu W, Su L. Towards environment independent device free human activity recognition. In: Proceedings of the 24th Annual International Conference on Mobile Computing and Networking. 2018, 289–304

  19. Zheng Y, Zhang Y, Qian K, Zhang G, Liu Y, Wu C, Yang Z. Zero-effort cross-domain gesture recognition with Wi-Fi. In: Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services. 2019, 313–325

  20. Halperin D, Hu W, Sheth A, Wetherall D. Tool release: gathering 802.11 n traces with channel state information. ACM SIGCOMM Computer Communication Review, 2011, 41(1): 53–53

    Article  Google Scholar 

  21. Sen S, Lee J, Kim K H, Congdon P. Avoiding multipath to revive inbuilding WiFi localization. In: Proceedings of the 11th Annual International Conference on Mobile Systems, Applications, and Services. 2013, 249–262

  22. Wang X, Gao L, Mao S, Pandey S. DeepFi: deep learning for indoor fingerprinting using channel state information. In: Proceedings of the 2015 IEEE Wireless Communications and Networking Conference. 2015, 1666–1671

  23. Gers F A, Schmidhuber J, Cummins F. Learning to forget: continual prediction with LSTM. Neural Computation, 2000, 12(10): 2451–2471

    Article  Google Scholar 

  24. Pan S J, Yang Q. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 2010, 22: 1345–1359

    Article  Google Scholar 

  25. Yosinski J, Clune J, Bengio Y, Lipson H. How transferable are features in deep neural networks? In: Proceedings of the 37th International Conference on Neural Information Processing Systems. 2014, 3320–3328

  26. Howard J, Ruder S. Universal language model fine-tuning for text classification. In: Proceedings of the 56th Annual Meeting of the Association for Computagional linguistics. 2018, 328–339

  27. Long M, Cao Y, Wang J, Jordan M. Learning transferable features with deep adaptation networks. In: Proceedings of the International Conference on Machine Learning. 2015, 97–105

  28. Ganin Y, Ustinova E, Ajakan H, Germain P, Larochelle H, Laviolette F, Marchand M, Lempitsky V. Domain-adversarial training of neural networks. The Journal of Machine Learning Research, 2016, 17: 2096–2030

    MathSciNet  MATH  Google Scholar 

  29. Esmael B, Arnaout A, Fruhwirth R K, Thonhauser G. Improving time series classification using Hidden Markov Models. In: Proceedings of the 12th International Conference on Hybrid Intelligent Systems. 2012, 502–507

  30. Kate R J. Using dynamic time warping distances as features for improved time series classification. Data Mining and Knowledge Discovery, 2016, 30: 283–312

    Article  MathSciNet  MATH  Google Scholar 

  31. Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Proceedings of Internationcol Conference on Machine Learning. 2015, 448–456

  32. Ng A Y. Feature selection, L1 vs. L2 regularization, and rotational invariance. In: Proceedings of the 21st International Conference on Machine Learning. 2004

  33. Kingma D P, Ba J. Adam: a method for stochastic optimization. 2014, arXiv preprint arXiv:1412.6980

  34. Zhou R, Lu X, Zhao P, Chen J. Device-free presence detection and localization with SVM and CSI fingerprinting. IEEE Sensors Journal, 2017, 17: 7990–7999

    Article  Google Scholar 

  35. Wang Z, Guo B, Yu Z, Zhou X. Wi-Fi CSI-based behavior recognition: from signals and actions to activities. IEEE Communications Magazine, 2018, 56: 109–115

    Article  Google Scholar 

  36. Zeng Y, Pathak P H, Mohapatra P. Analyzing shopper’s behavior through wifi signals. In: Proceedings of the 2nd Workshop on Physical Analytics. 2015, 13–18

  37. Sigg S, Blanke U, Troster G. The telepathic phone: frictionless activity recognition from wifi-rssi. In: Proceedings of the 2014 IEEE International Conference on Pervasive Computing and Communications. 2014, 148–155

Download references

Author information

Authors and Affiliations

  1. School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China

    Yuanrun Fang, Fu Xiao, Biyun Sheng, Letian Sha & Lijuan Sun

Authors
  1. Yuanrun Fang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Fu Xiao
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Biyun Sheng
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Letian Sha
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Lijuan Sun
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Fu Xiao.

Additional information

Yuanrun Fang received a Bachelor’s degree in Communication Engineering from Nanjing University of Posts and Telecommunications, China. He is currently a postgraduate in School of Computer, Nanjing University of Posts and Telecommunications, China. His current research interests are wireless sensor networks.

Fu Xiao received the PhD degree in Computer Science and Technology from Nanjing University of Science and Technology, China in 2007. He is currently a Professor and PhD supervisor with the School of Computer, Nanjing University of Posts and Telecommunications, China. His research papers have been published in many prestigious conferences and journals such as IEEE INFOCOM, IEEE ICC, IEEE IPCCC, IEEE/ACM ToN, IEEE JSAC, IEEE TMC, ACM TECS, IEEE TVT and so on. His research interests are mainly in the areas of Internet of things and mobile computing. Dr. Xiao is a member of the IEEE Computer Society and the Association for Computing Machinery.

Biyun Sheng received the BS and MS degrees in the School of Electrical and Information Engineering, Jiangsu University, China, and the PhD degree in School of Automation, Southeast University, China in 2010, 2013 and 2017, respectively. Now, she is with the School of Computer, Nanjing University of Posts and Telecommunications, China. Her research interests include pattern recognition, computer vision, machine learning and wireless sensing.

Letian Sha received the BS and MS degrees in the School of computer, Southwest Jiaotong University, China, and the PhD degree in School of computer, Wuhan University, China in 2007, 2010 and 2014, respectively. Now, he is with the School of Computer, Nanjing University of Posts and Telecommunications, China. His research interests include network security WSN security, IoT security and information security.

Lijuan Sun received the PhD degree in information and communication from the Nanjing University of Posts and Telecommunications, China in 2007. She is currently a Professor and a PhD Supervisor with the School of Computer, Nanjing University of Posts and Telecommunications, China. Her main research interests are wireless sensor networks and wireless mesh networks.

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fang, Y., Xiao, F., Sheng, B. et al. Cross-scene passive human activity recognition using commodity WiFi. Front. Comput. Sci. 16, 161502 (2022). https://doi.org/10.1007/s11704-021-0407-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11704-021-0407-8

Keywords