Abstract
With the rapid development of artificial intelligence, gesture recognition has become the focus of many countries for research. Gesture recognition using Wi-Fi signals has become the mainstream of gesture recognition because it does not require additional equipment and lighting conditions. Firstly, how to extract useful gesture signals in a complex indoor environment. In this paper, after de-noising the signal by Discrete Wavelet Transform (DWT) technology, Principal Component Analysis (PCA) is used to eliminate the problem of signal redundancy between multiple CSI subcarriers, further to remove noise. Secondly, the frequency domain features of the gesture signal are constructed by performing Short-Time Fourier Transform (STFT) on the denoised CSI amplitude signal. Then, the time domain features are combined with the frequency domain features, and the features are trained and classified using the Support Vector Machine (SVM) classification method to complete the training and recognition of gesture. The experimental results show that this paper can effectively identify gestures in complex indoor environments.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zhou, Y., Jiang, G., Lin, Y.: A novel finger and hand pose estimation technique for real-time hand gesture recognition. Pattern Recogn. 49, 102–114 (2016)
Sturman, D.J., Zeltzer, D.: A survey of glove-based input. IEEE Comput. Graphics Appl. 14(1), 30–39 (1994)
He, Y., Yang, J., Shao, Z., Li, Y.: Salient feature point selection for real time RGB-D hand gesture recognition. In: 2017 IEEE International Conference on Real-time Computing and Robotics (RCAR), pp. 103–108. IEEE (2017)
Kellogg, B., Talla, V., Gollakota, S.: Bringing gesture recognition to all devices. In: 11th fUSENIXg Symposium on Networked Systems Design and Implementation (fNSDIg 2014), pp. 303–316 (2014)
Wang, X., Sun, G., Han, D., Zhang, T.: Data glove gesture recognition based on an improved neural network. In: Proceedings of the 29th Chinese Control Conference, pp. 2434–2437. IEEE (2010)
Agrawal, S., Constandache, I., Gaonkar, S., Roy Choudhury, R., Caves, K., DeRuyter, F.: Using mobile phones to write in air. In: Proceedings of the 9th International Conference on Mobile Systems, Applications, and Services, pp. 15–28. ACM (2011)
Wu, X., Mao, X., Chen, L., Xue, Y.: Trajectory-based view-invariant hand gesture recognition by fusing shape and orientation. IET Comput. Vision 9(6), 797–805 (2015)
Microsoft kinect. http://www.microsoft.com/en-us/kinectforwindows
Leap motion. https://www.leapmotion.com
Abdelnasser, H., Youssef, M., Harras, K.A.: WiGest: a ubiquitous wifi-based gesture recognition system. In: 2015 IEEE Conference on Computer Communications (INFOCOM), pp. 1472–1480. IEEE (2015)
Al-qaness, M., Li, F.: WiGeR: WiFi-based gesture recognition system. ISPRS Int. J. Geo-Inf. 5(6), 92 (2016)
Wang, W., Liu, A.X., Shahzad, M., Ling, K., Lu, S.: 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. ACM (2015)
Palipana, S., Rojas, D., Agrawal, P., Pesch, D.: FallDeFi: Ubiquitous fall detection using commodity Wi-Fi devices. Proc. ACM Interact. Mob. Wearable Ubiquit. Technol. 1(4), 155 (2018)
He, W., Wu, K., Zou, Y., Ming, Z.: WiG: WiFi-based gesture recognition system. In: 2015 24th International Conference on Computer Communication and Networks (ICCCN), pp. 1–7. IEEE (2015)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Tian, Z., Ren, M., Jiang, Q., Zhang, X. (2020). Wi-Fi Gesture Recognition Technology Based on Time-Frequency Features (Workshop). In: Gao, H., Feng, Z., Yu, J., Wu, J. (eds) Communications and Networking. ChinaCom 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 313. Springer, Cham. https://doi.org/10.1007/978-3-030-41117-6_33
Download citation
DOI: https://doi.org/10.1007/978-3-030-41117-6_33
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-41116-9
Online ISBN: 978-3-030-41117-6
eBook Packages: Computer ScienceComputer Science (R0)