Vol. 79
Latest Volume
All Volumes
PIERM 126 [2024] PIERM 125 [2024] PIERM 124 [2024] PIERM 123 [2024] PIERM 122 [2023] PIERM 121 [2023] PIERM 120 [2023] PIERM 119 [2023] PIERM 118 [2023] PIERM 117 [2023] PIERM 116 [2023] PIERM 115 [2023] PIERM 114 [2022] PIERM 113 [2022] PIERM 112 [2022] PIERM 111 [2022] PIERM 110 [2022] PIERM 109 [2022] PIERM 108 [2022] PIERM 107 [2022] PIERM 106 [2021] PIERM 105 [2021] PIERM 104 [2021] PIERM 103 [2021] PIERM 102 [2021] PIERM 101 [2021] PIERM 100 [2021] PIERM 99 [2021] PIERM 98 [2020] PIERM 97 [2020] PIERM 96 [2020] PIERM 95 [2020] PIERM 94 [2020] PIERM 93 [2020] PIERM 92 [2020] PIERM 91 [2020] PIERM 90 [2020] PIERM 89 [2020] PIERM 88 [2020] PIERM 87 [2019] PIERM 86 [2019] PIERM 85 [2019] PIERM 84 [2019] PIERM 83 [2019] PIERM 82 [2019] PIERM 81 [2019] PIERM 80 [2019] PIERM 79 [2019] PIERM 78 [2019] PIERM 77 [2019] PIERM 76 [2018] PIERM 75 [2018] PIERM 74 [2018] PIERM 73 [2018] PIERM 72 [2018] PIERM 71 [2018] PIERM 70 [2018] PIERM 69 [2018] PIERM 68 [2018] PIERM 67 [2018] PIERM 66 [2018] PIERM 65 [2018] PIERM 64 [2018] PIERM 63 [2018] PIERM 62 [2017] PIERM 61 [2017] PIERM 60 [2017] PIERM 59 [2017] PIERM 58 [2017] PIERM 57 [2017] PIERM 56 [2017] PIERM 55 [2017] PIERM 54 [2017] PIERM 53 [2017] PIERM 52 [2016] PIERM 51 [2016] PIERM 50 [2016] PIERM 49 [2016] PIERM 48 [2016] PIERM 47 [2016] PIERM 46 [2016] PIERM 45 [2016] PIERM 44 [2015] PIERM 43 [2015] PIERM 42 [2015] PIERM 41 [2015] PIERM 40 [2014] PIERM 39 [2014] PIERM 38 [2014] PIERM 37 [2014] PIERM 36 [2014] PIERM 35 [2014] PIERM 34 [2014] PIERM 33 [2013] PIERM 32 [2013] PIERM 31 [2013] PIERM 30 [2013] PIERM 29 [2013] PIERM 28 [2013] PIERM 27 [2012] PIERM 26 [2012] PIERM 25 [2012] PIERM 24 [2012] PIERM 23 [2012] PIERM 22 [2012] PIERM 21 [2011] PIERM 20 [2011] PIERM 19 [2011] PIERM 18 [2011] PIERM 17 [2011] PIERM 16 [2011] PIERM 14 [2010] PIERM 13 [2010] PIERM 12 [2010] PIERM 11 [2010] PIERM 10 [2009] PIERM 9 [2009] PIERM 8 [2009] PIERM 7 [2009] PIERM 6 [2009] PIERM 5 [2008] PIERM 4 [2008] PIERM 3 [2008] PIERM 2 [2008] PIERM 1 [2008]
2019-02-16
Body Gesture Recognition Based on Polarimetric Micro-Doppler Signature and Using Deep Convolutional Neural Network
By
Progress In Electromagnetics Research M, Vol. 79, 71-80, 2019
Abstract
Body gesture recognition can be applied not only to social security but also to rescue operations. In reality, body gesture can produce unique micro-Doppler signatures (MDSs), which can be used for identification. In this paper, we first acquired the echo signals of four body gestures via a Ka-band dual polarization radar system under different angles and distances. The four gestures are respectively swinging arm up and down, swinging arm left and right, nodding, and shaking head. Then, time-frequency spectrograms were obtained by short-time Fourier transform, from which we can see that different body gestures have different polarimetric MDSs. Finally, we propose to classify four body gestures using the deep convolutional neural network (DCNN) method. It is shown that by combining HH and HV polarizations, about 92.7% recognition rate is achieved while only about 77.5% and 89.3% rates are obtained by using single HH polarization and single HV polarization, respectively.
Citation
Wenwu Kang, Yunhua Zhang, and Xiao Dong, "Body Gesture Recognition Based on Polarimetric Micro-Doppler Signature and Using Deep Convolutional Neural Network," Progress In Electromagnetics Research M, Vol. 79, 71-80, 2019.
doi:10.2528/PIERM18111509
References

1. Tekeli, B., S. Z. Gurbuz, and M. Yuksel, "Information-theoretic feature selection for human micro-Doppler signature classification," IEEE Transactions on Geoscience and Remote Sensing, Vol. 54, No. 5, 2749-2762, 2016.
doi:10.1109/TGRS.2015.2505409

2. Garcia-Rubia, J. M., O. Kilic, V. Dang, Q. M. Nguyen, and N. Tran, "Analysis of moving human micro-doppler signature in forest environments," Progress In Electromagnetics Research, Vol. 148, 1-14, 2014.
doi:10.2528/PIER14012306

3. Chen, V. C., F. Li, S. S. Ho, and H. Wechsler, "Micro-Doppler effect in radar: Phenomenon, model, and simulation study," IEEE Transactions on Aerospace and Electronic Systems, Vol. 42, No. 1, 2-21, 2006.
doi:10.1109/TAES.2006.1603402

4. Chen, V. C., F. Li, S. S. Ho, and H. Wechsler, "Analysis of micro-Doppler signature," IEE Proceeding - Radar Sonar and Navigation, Vol. 150, No. 4, 271-276, 2003.
doi:10.1049/ip-rsn:20030743

5. Li, L., Z. Huang, and W. Zhang, "Instantaneous frequency estimation methods of micro-doppler signal," Progress In Electromagnetics Research C, Vol. 58, 125-134, 2015.
doi:10.2528/PIERC15060203

6. Kim, Y. and H. Ling, "Human activity classification based on micro-Doppler signatures using a support vector machine," IEEE Transactions on Geoscience and Remote Sensing, Vol. 47, No. 5, 1328-1337, 2009.
doi:10.1109/TGRS.2009.2012849

7. Fioranelli, F., M. Ritchie, and H. Griffiths, "Multistatic human micro-Doppler classification of armed/unarmed personnel," IET Radar Sonar and Navigation, Vol. 9, No. 7, 857-865, 2015.
doi:10.1049/iet-rsn.2014.0360

8. Kim, Y. and T. Moon, "Human detection and activity classification based on micro-Doppler signatures using deep convolutional neural networks," IEEE Geoscience and Remote Sensing Letters, Vol. 13, No. 1, 8-12, 2016.
doi:10.1109/LGRS.2015.2491329

9. Kim, Y. and B. Toomajian, "Hand gesture recognition using micro-doppler signatures with convolutional neural network," IEEE Access, Vol. 4, 7125-7130, 2016.
doi:10.1109/ACCESS.2016.2617282

10. Kim, Y. and Y. Li, "Human activity classification with transmission and reflection coefficients of on-body antennas through deep convolutional neural networks," IEEE Transactions on Antennas and Propagation, Vol. 65, No. 5, 2764-2768, 2017.
doi:10.1109/TAP.2017.2677918

11. Chen, Z., G. Li, F. Fioranelli, and H. Griffiths, "Personnel recognition and gait classification based on multistatic micro-Doppler signatures using deep convolutional neural networks," IEEE Geoscience and Remote Sensing Letters, Vol. 15, No. 5, 669-673, 2018.
doi:10.1109/LGRS.2018.2806940

12. Cao, P., W. Xia, M. Ye, J. Zhang, and J. Zhou, "Radar-ID: human identification based on radar micro-Doppler signatures using deep convolutional neural networks," IET Radar Sonar and Navigation, Vol. 12, No. 7, 729-734, 2018.
doi:10.1049/iet-rsn.2017.0511

13. Kang, W., Y. Zhang, and X. Dong, "Polarimetric MDS of pedestrian," Electronics Letters, Vol. 54, No. 17, 1051-1053, 2018.
doi:10.1049/el.2018.0208

14. Kim, B. K., H. Kang, and S. Park, "Experimental analysis of small drone polarimetry based on micro-Doppler signature," IEEE Geoscience and Remote Sensing Letters, Vol. 14, No. 10, 1670-1674, 2017.
doi:10.1109/LGRS.2017.2727824

15., Available at http://download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz/.
doi:10.1109/LGRS.2017.2727824

16. Chen, V. C., The Micro-Doppler Effect in Radar, Artech House, Boston, 2011.

17. Li, J., P. Wang, Y. Li, Y. Zhao, X. Liu, and K. Luan, "Transfer learning of pre-trained Inception-v3 model for colorectal cancer lymph node metastasis classification," 2018 IEEE Inter. Conf. on Mechatronics and Automation, 1650-1654, Changchun, China, Aug. 5–8, 2018.

18. Du, L., L. Li, B. Wang, and J. Xiao, "Micro-doppler feature extraction based on time-frequency spectrogram for ground moving targets classification with low-resolution radar," IEEE Sensors Journal, Vol. 16, No. 10, 3756-3763, 2016.
doi:10.1109/JSEN.2016.2538790