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http://dx.doi.org/10.5762/KAIS.2020.21.7.685

Study on Hand Gestures Recognition Algorithm of Millimeter Wave  

Nam, Myung Woo (Dept. of Digital Electronics, Hyejeon College)
Hong, Soon Kwan (Dept. of Digital Electronics, Hyejeon College)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.21, no.7, 2020 , pp. 685-691 More about this Journal
Abstract
In this study, an algorithm that recognizes numbers from 0 to 9 was developed using the data obtained after tracking hand movements using the echo signal of a millimeter-wave radar sensor at 77 GHz. The echo signals obtained from the radar sensor by detecting the motion of a hand gesture revealed a cluster of irregular dots due to the difference in scattering cross-sectional area. A valid center point was obtained from them by applying a K-Means algorithm using 3D coordinate values. In addition, the obtained center points were connected to produce a numeric image. The recognition rate was compared by inputting the obtained image and an image similar to human handwriting by applying the smoothing technique to a CNN (Convolutional Neural Network) model trained with MNIST (Modified National Institute of Standards and Technology database). The experiment was conducted in two ways. First, in the recognition experiments using images with and without smoothing, average recognition rates of 77.0% and 81.0% were obtained, respectively. In the experiment of the CNN model with augmentation of learning data, a recognition rate of 97.5% and 99.0% on average was obtained in the recognition experiment using the image with and without smoothing technique, respectively. This study can be applied to various non-contact recognition technologies using radar sensors.
Keywords
Millimeter Wave; Gesture Recognition; K-Means; Smoothing; Deep Learning;
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Times Cited By KSCI : 5  (Citation Analysis)
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1 S. Mitra and T. Acharya, "Gesture Recognition: A Survey", in IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 37, no. 3, pp. 311-324, May 2007. DOI: http://dx.doi.org/10.1109/TSMCC.2007.893280   DOI
2 H. C. Yoon and J. S. Cho, "Hand Feature Extraction Algorithm Using Curvature Analysis For Recognition of Various Hand Gestures", Journal of The Korea Society of Computer and Information, Vol.20, No.5, pp.13-20, May 2015. DOI : http://dx.doi.org/10.9708/jksci.2015.20.5.013   DOI
3 Y. S. Lee, "Study on the Hand Gesture Recognition System and Algorithm based on Millimeter Wave Radar", Journal of Korea Institute of Information, Electronics, and Communication Technology, Vol.12, No.3, pp.251-256, Dec. 2019. DOI : http://dx.doi.org/10.17661/jkiiect.2019.12.3.251   DOI
4 P. Molchanov, S. Gupta, K. Kim and K. Pulli, "Short-range FMCW monopulse radar for hand-gesture sensing", 2015 IEEE Radar Conference (RadarCon), Arlington, VA, pp. 1491-1496, Jun. 2015. DOI: http://dx.doi.org/10.1109/RADAR.2015.7131232
5 http://www.ti.com/lit/ds/symlink/iwr1443.pdf (accessed Dec. 01, 2019)
6 http://www.ti.com/lit/an/swra553a/swra553a.pdf (accessed Dec. 01, 2019)
7 http://yann.lecun.com/exdb/mnist/ (accessed March 10, 2020)
8 https://www.tensorflow.org/ (accessed March 10, 2020)
9 Jurgen Schmidhuber, "Deep learning in neural networks: An overview", Neural Networks, Vol.61, pp.85-117, Jan. 2015. DOI: http://dx.doi.org/10.1016/j.neunet.2014.09.003   DOI
10 M. J. Kang. "Comparison of Gradient Descent for Deep Learning" Journal of the Korea Academia-Industrial cooperation Society, Vol.21, No.2, pp.189-194, Feb. 2020. DOI: http://dx.doi.org/10.5762/KAIS.2020.21.2.189   DOI
11 Fritsch, F. N. and R. E. Carlson. "Monotone Piecewise Cubic Interpolation", SIAM Journal on Numerical Analysis. Vol. 17, pp.238-246, 1980. DOI: http://dx.doi.org/10.1137/0717021   DOI
12 https://github.com/franneck94/MNIST-Data-Augmentation (accessed March 10, 2020)