Vol. 113

Latest Volume
All Volumes
All Issues

Human Motion Recognition in Small Sample Scenarios Based on GaN and CNN Models

By Ying-Jie Zhong and Qiusheng Li
Progress In Electromagnetics Research M, Vol. 113, 101-113, 2022


In the research of radar-based human motion classification and recognition, the traditional manual feature extraction is more complicated, and the echo dataset is generally smaller. In view of this problem, a method of human motion recognition in small sample scenarios based on Generative Adversarial Network (GAN) and Convolutional Neural Network (CNN) models is proposed. First, a 77 GHz millimeter wave radar data acquisition system is built to obtain echo data. Secondly, the collected human motion echo data is preprocessed, the micro-Doppler features are extracted, and the range Doppler map (RDM) is used to project the velocity dimension and accumulate the two-dimensional micro-Doppler time-frequency map dataset of the human motion frame by frame. Finally, the deep convolution generative adversarial network (DCGAN) is constructed to achieve data augmentation of the sample set, and the CNN is constructed to realize automatic feature extraction to complete the classification recognition of different human motions. Experimental studies have shown that the combination of GAN and CNN can achieve effective recognition of daily human motions, and the recognition accuracy can reach 96.5%. Compared with the manual feature extraction, the recognition accuracy of CNNs is improved by 7.3%. Compared with the original data set, the system recognition accuracy based on the sample augmentation data set is improved by 2.17%, which shows that the GAN has an excellent performance in human motion recognition in small sample scenarios.


Ying-Jie Zhong and Qiusheng Li, "Human Motion Recognition in Small Sample Scenarios Based on GaN and CNN Models," Progress In Electromagnetics Research M, Vol. 113, 101-113, 2022.


    1. Deng, P. and M. Wu, "Human motion and gesture recognition method based on machine learning," Chinese Journal of Inertial Technology, Vol. 30, No. 1, 37-43, 2022.

    2. Luo, H., K. Tong, and F. Kong, "Review of human action recognition in video based on deep learning," Journal of Electronic Arts, Vol. 47, No. 5, 1162-1173, 2019.

    3. Ding, Y., R. Liu, and X. Xu, "Micro-Doppler frequency estimation method for human target based on continuous wave radar," Journal of Central South University (Natural Science Edition), Vol. 53, No. 4, 1273-1280, 2022.

    4. Bryan, J. D., et al., "Application of ultra-wide band radar for classification of human activities," IET Radar, Sonar & Navigation, Vol. 6, No. 3, 172-179, 2012.

    5. Ding, C., et al., "Non-contact human motion recognition based on UWB radar," IEEE Journal on Emerging and Selected Topics in Circuits and Systems, Vol. 8, No. 2, 306-315, 2018.

    6. Jiang, L., X. Zhou, and L. Che, "Small-sample human action recognition based on carrier-free ultra-wideband radar," Journal of Electronic Engineering, Vol. 48, No. 3, 602-615, 2020.

    7. Zheng, Y., G. Li, and Y. Li, "A review of the application of deep learning in image recognition," Computer Engineering and Applications, Vol. 55, No. 12, 20-36, 2019.

    8. Li, A., et al., "Speech enhancement using progressive learning-based convolutional recurrent neural network," Applied Acoustics, Vol. 166, 107347, 2020.

    9. Prabhakar, S. K., D.-O. Won, and Y. Maleh, "Medical text classification using hybrid deep learning models with multihead attention," Computational Intelligence and Neuroscience, Vol. 2021, 9425655, 2021.

    10. 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.

    11. Park, J., R. J. Javier, and Y. Kim, "Micro-Doppler based classification of human aquatic activities via transfer learning of convolutional neural networks," Sensors, Vol. 16, No. 12, 1990, 2016.

    12. Sun, X., et al., "A new cyclical generative adversarial network based data augmentation method for multiaxial fatigue life prediction," International Journal of Fatigue, 162, 2022.

    13. Jin, H., et al., "GrapeGAN: Unsupervised image enhancement for improved grape leaf disease recognition," Computers and Electronics in Agriculture, 198, 2022.

    14. Alnujaim, I., D. Oh, and Y. Kim, "Generative adversarial networks for classification of micro-Doppler signatures of human activity," IEEE Geoscience and Remote Sensing Letters, Vol. 17, No. 3, 396-400, 2020.

    15. Cha, D., et al., "Multi-input deep learning based FMCW radar signal classification," Electronics, Vol. 10, 1144, 2021.

    16. Chen, V. C., D. Tahmoush, and W. J. Miceli, "Radar micro-doppler signatures: Processing and applications," IET Digital Library, 406, 2014.

    17. Jin, T., et al., "Research progress on human behavior perception by ultra-wideband radar," Journal of Electronics and Information, Vol. 44, No. 4, 1147-1155, 2022.