Abstract
In recent years, hand gesture recognition has increasingly become important in the field of human-computer interaction. This paper proposes a two-dimension parameter based hand gesture recognition method using frequency modulated continuous wave (FMCW) radar. Specifically, we analyze the time domain of the radar signal and estimate the radial distance and angle parameters of hand gestures, and then construct the parameter dataset. The dataset is fed into an improved convolutional neural network to extract features. Finally, the extracted features are fused and then classified by the full connection layer. Experimental results show that the recognition accuracy of the proposed approach is significantly higher than that of the single-parameter ones.
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Acknowledgments
This work was supported in part by the National Natural Science Foundation of China (61771083, 61704015), Program for Changjiang Scholars and Innovative Research Team in University (IRT1299), Special Fund of Chongqing Key Laboratory (CSTC), Fundamental and Frontier Research Project of Chongqing (cstc2017jcyjAX0380, cstc2015jcyjBX0065), University Outstanding Achievement Transformation Project of Chongqing (KJZH17117), and Postgraduate Scienti_cResearch and Innovation Project of Chongqing (CYS17221).
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Wang, Y., Zhao, Z., Zhou, M., Wu, J. (2019). Two Dimensional Parameters Based Hand Gesture Recognition Algorithm for FMCW Radar Systems. In: Jia, M., Guo, Q., Meng, W. (eds) Wireless and Satellite Systems. WiSATS 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 280. Springer, Cham. https://doi.org/10.1007/978-3-030-19153-5_23
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DOI: https://doi.org/10.1007/978-3-030-19153-5_23
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