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Telecommunications and Radio Engineering
SJR: 0.203 SNIP: 0.44 CiteScore™: 1

ISSN 印刷: 0040-2508
ISSN オンライン: 1943-6009

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Telecommunications and Radio Engineering

DOI: 10.1615/TelecomRadEng.v79.i1.50
pages 47-57

HUMAN GESTURE RECOGNITION USING BISPECTRUM-BASED WIRELESS SIGNAL PROCESSING

O. G. Viunytskyi
National Aerospace University (Kharkiv Aviation Institute), 17 Chkalov St., Kharkiv, 61070, Ukraine
A. V. Totsky
National Aerospace University (Kharkiv Aviation Institute), 17, Chkalov St., Kharkiv, 61070, Ukraine
Karen O. Egiazarian
Tampere University, Department of Signal Processing, P. O. Box 553, FIN-33101, Tampere, Finland

要約

Recently, state-of-the-art technologies are attracting considerable attention and developing successfully for human gesture detection, recognition and classification by using wireless signals. By using different hand motion in-air, a user could provide remote control of smart home devices or car multimedia systems. Systems operating without any physical contact between humans and different devices can be also useful for interface with computers exploiting of human gestures, gaming, outdoor lighting, remote controlling the UAV/multicopters, security applications, industrial robotics, health (vital sensing) and others. In these systems, detection, recognition and classification procedures are performed by extraction the contributions in the electromagnetic field caused by human gesture impact. In this paper, a novel bispectrum-based strategy is proposed and experimentally studied for human gesture classification. A novel type of classification features extracted from signal distorted by human gestures is suggested by evaluation of the third-order spectrum named bispectrum. It has been demonstrated that phase bispectrum or biphase contains unique discriminative features serving for the detection and classification of human gestures. The feasibility of developed hardware and software is demonstrated experimentally. It is shown that suggested bispectrum-based strategy provides invariance property to random signal time delays and considerable signal magnitude variations usually observed in the intricate indoor multi-path interference environment. Human gesture classification accuracy has been evaluated and discussed. Our results indicate the robustness of bispectrum-based information features for human gesture recognition and classification in a complicated indoor interference environment.

参考

  1. Adib, F. and Katabi, D., (2013) See through walls with Wi-Fi, Proc. ACM Special Interest Group on Data Communications Conference, Hong Kong, China, pp. 75-86.

  2. Bryce Kellog, Varsmi Talla, and Shyamnath Gollacota, (2014) Bringing gesture recognition to all devices, NSDI'14 Proc. 11th USENIX Conference on Networked Systems Design and Implementation, USENIXBerkley, CA, USA, pp. 303-316.

  3. Mu-Cyun Tang, Fu-Kang Wang, and Tzyy-Sheng Horng, (2015) Human gesture sensor using ambient wireless signals based on passive radar technology, Proceedings of 2015 IEEE MTT-S International Microwave Symposium, Phoenix, Arizona, USA, pp. 1376-1379.

  4. Chen Zhao, Ke-Yu Chen, Md Tanvir Islam Aumi, Shwetak Patel, and Matthew S. Reynolds, (2014) SideSwipe: detecting in-air gestures around mobile devices using actual GSM signals, Proceedings of ACM Symposium on User Interface Software and Technology (UIST), UIST '14, Honolulu, HI, USA, pp. 527-534.

  5. Totsky, A.V., Zelensky, A.A., and Kravchenko, V.F., (2015) Bispectral Methods of Signal Processing, ISBN 978-3-11-037456-8, Walter de Gruyter GmbH, Berlin/Munich/Boston, 199 p.

  6. Nikias, C.L. and Raghuveer, M.R. (1987) Bispectral estimation: A digital signal processing framework, Proceeding IEEE, 75(4), pp. 869-891.

  7. Viunytskyi, O.G., Borodavko, A.A., and Totsky, A.V., (2016) A new method for recognizing and classifying gestures based on the allocation of informative features from the bispectrum estimate of a radio signal, Radioelectronic and Computer system, 4(78), pp. 108-112, (in Russian).

  8. Viunytskyi, O. and Totsky, A., (2017) Novel Bispectrum-Based Wireless Vision Technique Using Disturbance of Electromagnetic Field by Human Gestures, Proceedings of 2017 IEEE Signal Processing Symposium, Jachranka, near Warsaw, Poland, pp. 250-253.


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