Skip to main content
SpringerLink
Log in

Dynamic gesture recognition using wireless signals with less disturbance

  • Original Article
  • Published:
Personal and Ubiquitous Computing Aims and scope Submit manuscript

Abstract

As a nonverbal body language, gestures undoubtedly can play a very significant role when interacting with smart devices. One of the most discrete ways of gesture recognition is through the use of Wi-Fi signals. Recent literatures start to explore the feasibility of utilizing the widely deployed Wi-Fi infrastructure to track human motions and interact with smart devices. In this paper, we develop a gesture recognition system, which adopts off-the-shelf Wi-Fi devices to collect fine-grained wireless Channel State Information (CSI). First, low pass filter is used to eliminate noise, then principal component analysis (PCA) is used to reduce data dimension as well as eliminate noise further. Moving objects may have significant disturbance in the gesture recognition and this may occur frequently in the actual environment; thus, we introduce a disturbance eliminating module and independent component analysis (ICA) is used for disturbance eliminate. The experimental results have shown that our system can keep high accuracy even with effects of moving objects.

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

Similar content being viewed by others

References

  1. Wang Z, Guo B, Yu Z, Zhou X (2018) Wi-fi csi-based behavior recognition: from signals and actions to activities. IEEE Commun Mag 56(5):109–115

    Article  Google Scholar 

  2. Xin T, Guo B, Wang Z, Wang P, Yu Z (2018) FreeSense: human-behavior understanding using Wi-Fi signals. J. Ambient Intell Humaniz Comput 9(5):1611–1622

    Article  Google Scholar 

  3. Chapre Y, Ignjatovic A, Seneviratne A, Jha SK (2015) CSI-MIMO: an efficient wi-fi fingerprinting using channel state information with MIMO. Pervasive Mob Comput 23:89–103

    Article  Google Scholar 

  4. Wang W, Liu AX, Shahzad M, Ling K, Lu S (2015) Understanding and modeling of wifi signal based human activity recognition. In: Proceedings of the 21st annual international conference on mobile computing and networking, pp 65–76

  5. Wang X, Gao L, Mao S, Pandey S (2017) Csi-based fingerprinting for indoor localization: a deep learning approach. IEEE Trans Veh Technol 66(1):763–776

    Google Scholar 

  6. Shi S, Sigg S, Ji Y (2012) Passive detection of situations from ambient fm-radio signals. In: The ACM, conference on ubiquitous computing, pp 1049–1053

  7. Wang H, Zhang D, Ma J, Wang Y, Wang Y, Wu D, Gu T, Xie B (2016) Human respiration detection with commodity wifi devices: do user location and body orientation matter?. In: Proceedings of the ACM, international joint conference on pervasive and ubiquitous computing, pp 25–36

  8. Zhang D, Wang H, Wang Y, Ma J (2015) Anti-fall: a non-intrusive and real-time fall detector leveraging CSI from commodity wifi devices. In: Inclusive smart cities and e-health - 13th international conference on smart homes and health telematics, pp 181–193

  9. Ali K, Liu AX, Wang W, Shahzad M (2015) Keystroke recognition using wifi signals. In: Proceedings of the 21st annual international conference on mobile computing and networking, pp 90–102

  10. Pavlovic V, Sharma R, Huang TS (1997) Visual interpretation of hand gestures for human-computer interaction: a review. IEEE Trans Pattern Anal Mach Intell 19(7):677–695

    Article  Google Scholar 

  11. Rautaray SS, Agrawal A (2015) Vision based hand gesture recognition for human computer interaction: a survey. Artif Intell Rev 43(1):1–54

    Article  Google Scholar 

  12. Escalera S, Athitsos V, Guyon I (2016) Challenges in multimodal gesture recognition. J Mach Learn Res 17:72:1–72:54

    MathSciNet  Google Scholar 

  13. Patel SN, Reynolds MS, Abowd GD (2008) Detecting human movement by differential air pressure sensing in HVAC system ductwork: an exploration in infrastructure mediated sensing. In: 6th international conference pervasive computing, pp 1–18

  14. Tapia EM, Intille SS, Larson K (2004) Activity recognition in the home using simple and ubiquitous sensors. In: Second international conference pervasive computing, pp 158–175

  15. van Kasteren T, Noulas AK, Englebienne G, Kröse BJA (2008) Accurate activity recognition in a home setting. In: 10th international conference ubiquitous computing, pp 1–9

  16. Dekate A, Kamal A, Surekha K (2014) Magic glove-wireless hand gesture hardware controller. In: Electronics and communication systems (ICECS), pp 1–4

  17. Wang X, Sun G, Han D, Zhang T (2010) Data glove gesture recognition based on an improved neural network. In: Control conference (CCC), pp 2434–2437

  18. Zhang M, Li P, Yang P, Xiong J, Tian C (2016) Poster: sonicnect: accurate hands-free gesture input system with smart acoustic sensing. In: Proceedings of the 14th annual international conference on mobile systems, applications, and services companion, p 91

  19. Chen H, Li F, Wang Y (2017) EchoTrack: Acoustic device-free hand tracking on smart phones. In: 2017 IEEE conference on computer communications (INFOCOM), IEEE, pp 1422–1430

  20. Bernardes Jr JL, Nakamura R, Tori R (2009) Design and implementation of a flexible hand gesture command interface for games based on computer vision. In: 2009 VIII Brazilian symposium on games and digital entertainment (SBGAMES). IEEE, pp 64–73

  21. Wu X, Mao X, Chen L, Xue Y (2015) Trajectory-based view-invariant hand gesture recognition by fusing shape and orientation. IET Comput Vis 9(6):797–805

    Article  Google Scholar 

  22. Adib F, Katabi D (2013) See through walls with wifi!. In: ACM SIGCOMM, Conference, pp 75–86

  23. Pu Q, Gupta S, Gollakota S, Patel S (2013) Whole-home gesture recognition using wireless signals. In: The 19th annual international conference on mobile computing and networking, pp 27–38

  24. Zhou G, Jiang T, Liu Y, Liu W (2015) Dynamic gesture recognition with wi-fi based on signal processing and machine learning. In: IEEE global conference on signal and information processing, pp 717–721

  25. Aljumaily MS, Al-suhail al-suhail GA (2017) Towards ubiquitous human gestures recognition using wireless networks. Int J Pervasive Computing and Communications 13(4):408–418

    Article  Google Scholar 

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

    Article  Google Scholar 

  27. Xiao Y (2005) IEEE, 802.11n: enhancements for higher throughput in wireless lans. IEEE Wirel Commun 12 (6):82–91

    Article  Google Scholar 

  28. Qian K, Wu C, Yang Z, Liu Y, Zhou Z (2014) PADS:, passive detection of moving targets with dynamic speed using PHY layer information. In: 20th IEEE international conference on parallel and distributed systems, pp 1–8

  29. Hyvärinen A (1999) Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans Neural Netw 10(3):626–634

    Article  Google Scholar 

  30. Hou T, Qin H (2012) Continuous and discrete mexican hat wavelet transforms on manifolds. Graph Model 74(4):221–232

    Article  Google Scholar 

  31. Wang K, Gasser T, et al. (1997) Alignment of curves by dynamic time warping. Ann Stat 25(3):1251–1276

    Article  MathSciNet  MATH  Google Scholar 

Download references

Funding

The work is partially supported by the National Natural Science Foundation of China (NSFC) under Grant Nos. 61772077, 61370192, 61432015, 61572347, 61802018, the US National Science Foundation under Grant No. CNS-1343355, the U.S. Department of Transportation Center for Advanced Multimodal Mobility Solutions and Education, China Scholarship Council, and by the Beijing Institute of Technology Research Fund Program for Young Scholars. Fan Li is the corresponding author.

Author information

Authors and Affiliations

  1. School of Computer Science, Beijing Institute of Technology, Beijing, 100081, China

    Jiahui Chen, Fan Li, Huijie Chen & Song Yang

  2. Department of Computer Science, University of North Carolina at Charlotte, Charlotte, NC, 28223, USA

    Yu Wang

Authors
  1. Jiahui Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Huijie Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Song Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yu Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fan Li.

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

Chen, J., Li, F., Chen, H. et al. Dynamic gesture recognition using wireless signals with less disturbance. Pers Ubiquit Comput 23, 17–27 (2019). https://doi.org/10.1007/s00779-018-1182-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00779-018-1182-x

Keywords

Search

Navigation