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.
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
Hjelmås, E., Low, B.K.: Face detection: a survey. Comput. Vis. Image Underst. 83(3), 236–274 (2001)
Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recognition: a literature survey. ACM Comput. Surv. (CSUR) 35(4), 399–458 (2003)
Gavrila, D.M.: The visual analysis of human movement: a survey. Comput. Vis. Image Underst. 73(1), 82–98 (1999)
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)
Molchanov, P., Gupta, S., Kim, K., Pulli, K.: Short-range FMCW monopulse radar for hand-gesture sensing. In: RadarCon, pp. 1491–1496. IEEE (2015)
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)
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)
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)
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)
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)
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)
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)
Mallat, S.: A Wavelet Tour of Signal Processing. Academic press, New York (1999)
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)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Fan, J., Xu, W., Wu, Y., Gong, Y.: Human tracking using convolutional neural networks. IEEE Trans. Neural Netw. 21(10), 1610–1623 (2010)
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)
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)
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)
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)
Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015)
Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., Wei, W.: Deformable convolutional networks. arXiv preprint arXiv:1703.06211 (2017)
Acknowledgments
This work is supported by Intel under agreement No. CG# 30397855.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhang, J., Tao, J., Shi, Z. (2019). Doppler-Radar Based Hand Gesture Recognition System Using Convolutional Neural Networks. In: Liang, Q., Mu, J., Jia, M., Wang, W., Feng, X., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2017. Lecture Notes in Electrical Engineering, vol 463. Springer, Singapore. https://doi.org/10.1007/978-981-10-6571-2_132
Download citation
DOI: https://doi.org/10.1007/978-981-10-6571-2_132
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-6570-5
Online ISBN: 978-981-10-6571-2
eBook Packages: EngineeringEngineering (R0)