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Doppler-Radar Based Hand Gesture Recognition System Using Convolutional Neural Networks

  • Jiajun Zhang
  • Jinkun Tao
  • Zhiguo Shi
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 463)

Abstract

Hand gesture recognition has long been a study topic in the field of Human Computer Interaction. Traditional camera-based hand gesture recognition systems can not work properly under dark circumstances. In this paper, a Doppler-Radar based hand gesture recognition system using convolutional neural networks is proposed. A cost-effective Dopper radar sensor with dual receiving channels at 5.8 GHz is used to acquire a big database of four standard gestures. The received hand gesture signals are then processed with time-frequency analysis. Convolutional neural networks are used to classify different gestures. Experimental results verify the effectiveness of the system with an accuracy of 98%. Besides, related factors such as recognition distance and gesture scale are investigated.

Notes

Acknowledgments

This work is supported by Intel under agreement No. CG# 30397855.

References

  1. 1.
    Hjelmås, E., Low, B.K.: Face detection: a survey. Comput. Vis. Image Underst. 83(3), 236–274 (2001)Google Scholar
  2. 2.
    Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recognition: a literature survey. ACM Comput. Surv. (CSUR) 35(4), 399–458 (2003)Google Scholar
  3. 3.
    Gavrila, D.M.: The visual analysis of human movement: a survey. Comput. Vis. Image Underst. 73(1), 82–98 (1999)Google Scholar
  4. 4.
    Molchanov, P., Gupta, S., Kim, K., Pulli, K.: Multi-sensor system for driver’s hand-gesture recognition. In: 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), vol. 1, pp. 1–8. IEEE (2015)Google Scholar
  5. 5.
    Molchanov, P., Gupta, S., Kim, K., Pulli, K.: Short-range FMCW monopulse radar for hand-gesture sensing. In: RadarCon, pp. 1491–1496. IEEE (2015)Google Scholar
  6. 6.
    Arbabian, A., Callender, S., Kang, S., Rangwala, M., Niknejad, A.M.: A 94 GHz mm-wave-to-baseband pulsed-radar transceiver with applications in imaging and gesture recognition. IEEE J. Solid-State Circ. 48(4), 1055–1071 (2013)Google Scholar
  7. 7.
    Hügler, P., Geiger, M., Waldschmidt, C.: Rcs measurements of a human hand for radar-based gesture recognition at e-band. In: Microwave Conference (GeMiC) 2016, German, pp. 259–262. IEEE (2016)Google Scholar
  8. 8.
    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 and Networking, pp. 27–38. ACM (2013)Google Scholar
  9. 9.
    Mercuri, M., Soh, P.J., Pandey, G., Karsmakers, P., Vandenbosch, G.A., Leroux, P., Schreurs, D.: Analysis of an indoor biomedical radar-based system for health monitoring. IEEE Trans. Microw. Theor. Tech. 61(5), 2061–2068 (2013)Google Scholar
  10. 10.
    Peng, Z., Muñoz-Ferreras, J.-M., Gómez-García, R., Li, C.: FMCW radar fall detection based on isar processing utilizing the properties of rcs, range, and doppler. In: IEEE MTT-S International on Microwave Symposium (IMS) 2016, pp. 1–3. IEEE (2016)Google Scholar
  11. 11.
    Zhou, Z., Zhang, J., Zhang, Y.D.: Ultra-wideband radar and vision based human motion classification for assisted living. In: 2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM), pp. 1–5. IEEE (2016)Google Scholar
  12. 12.
    Sejdić, E., Djurović, I., Jiang, J.: Time-frequency feature representation using energy concentration: an overview of recent advances. Digit. Signal Proc. 19(1), 153–183 (2009)Google Scholar
  13. 13.
    Mallat, S.: A Wavelet Tour of Signal Processing. Academic press, New York (1999)Google Scholar
  14. 14.
    LeCun, Y., Kavukcuoglu, K., Farabet, C.: Convolutional networks and applications in vision. In: Proceedings of 2010 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 253–256. IEEE (2010)Google Scholar
  15. 15.
    LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)Google Scholar
  16. 16.
    Fan, J., Xu, W., Wu, Y., Gong, Y.: Human tracking using convolutional neural networks. IEEE Trans. Neural Netw. 21(10), 1610–1623 (2010)Google Scholar
  17. 17.
    LeCun, Y., Huang, F.J., Bottou, L.: Learning methods for generic object recognition with invariance to pose and lighting. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition CVPR 2004, vol. 2, pp. II–104. IEEE (2004)Google Scholar
  18. 18.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar
  19. 19.
    Ciresan, D.C., Meier, U., Masci, J., Maria Gambardella, L., Schmidhuber, J.: Flexible, high performance convolutional neural networks for image classification. In: IJCAI Proceedings, vol. 22(1), p. 1237. Barcelona, Spain (2011)Google Scholar
  20. 20.
    Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)Google Scholar
  21. 21.
    Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015)
  22. 22.
    Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., Wei, W.: Deformable convolutional networks. arXiv preprint arXiv:1703.06211 (2017)

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.College of Information Science and Electronic EngineeringZhejiang UniversityHangzhouChina

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