Citation: | Zhaoyang XIA, Chenglong ZHOU, Junyu JIE, Tao ZHOU, Xiangfeng WANG, Feng XU. Micro-motion Gesture Recognition Based on Multi-channel Frequency Modulated Continuous Wave Millimeter Wave Radar[J]. Journal of Electronics & Information Technology, 2020, 42(1): 164-172. doi: 10.11999/JEIT190797 |
A micro-motion gesture recognition method based on multi-channel Frequency Modulated Continuous Wave (FMCW) millimeter wave radar is proposed, and an optimal radar parameter design criterion for feature extraction of micro-motion gestures is presented. The time-frequency analysis process is performed on the radar echo reflected by the hand, and the range Doppler spectrum, the range spectrum, the Doppler spectrum and the horizontal direction angle spectrum of the target are estimated. Then the range-Doppler-time-map feature is designed, range-time-map feature, Doppler-time-map feature, horizontal-angle-time-map feature, and three-joint feature with fixed frame time length are used to characterize the 7 classes micro-motion gestures, respectively. And these gesture features are captured and aligned according to the difference in amplitude and speed of the gesture motion process. Then a five-layer lightweight convolutional neural network is designed to classify the gesture features. The experimental results show that, the range-Doppler-time-map feature designed in this paper characterizes the micro-motion gesture more accurately and has a better generalization ability for untrained test objects compared with other features.
WAN Qian, LI Yiran, LI Changzhi, et al. Gesture recognition for smart home applications using portable radar sensors[C]. The 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, USA, 2014: 6414–6417.
|
KHAN F, LEEM S K, and CHO S H. Hand-based gesture recognition for vehicular applications using IR-UWB radar[J]. Sensors, 2017, 17(4): 833. doi: 10.3390/s17040833
|
XIA Zia, SANG Xinzhu, CHEN Duo, et al. An interactive VR system based on full-body tracking and gesture recognition[C]. The SPIE 10021, Optical Design and Testing VⅡ, Beijing, China 2016: 1002129.
|
LEE B G and LEE S M. Smart wearable hand device for sign language interpretation system with sensors fusion[J]. IEEE Sensors Journal, 2018, 18(3): 1224–1232. doi: 10.1109/JSEN.2017.2779466
|
TARANTA Ⅱ E M, SIMONS T K, SUKTHANKAR R, et al. Exploring the benefits of context in 3D gesture recognition for game-based virtual environments[J]. ACM Transactions on Interactive Intelligent Systems, 2015, 5(1): Article No.1.
|
BONATO V, FERNANDES M M, and MARQUES E. A smart camera with gesture recognition and SLAM capabilities for mobile robots[J]. International Journal of Electronics, 2006, 93(6): 385–401. doi: 10.1080/00207210600565465
|
孟春宁, 吕建平, 陈萱华. 基于普通红外摄像机的手势识别[J]. 计算机工程与应用, 2015, 51(16): 17–22. doi: 10.3778/j.issn.1002-8331.1504-0303
MENG Chunning, LÜ Jianping, and CHEN Xuanhua. Gesture recognition based on universal infrared camera[J]. Computer Engineering and Applications, 2015, 51(16): 17–22. doi: 10.3778/j.issn.1002-8331.1504-0303
|
PLOUFFE G and CRETU A M. Static and dynamic hand gesture recognition in depth data using dynamic time warping[J]. IEEE Transactions on Instrumentation and Measurement, 2016, 65(2): 305–316. doi: 10.1109/TIM.2015.2498560
|
YANG Qifan, TANG Hao, ZHAO Xuebing, et al. Dolphin: Ultrasonic-based gesture recognition on smartphone platform[C]. The 2014 IEEE 17th International Conference on Computational Science and Engineering, Chengdu, China, 2014: 1461–1468.
|
KELLOGG B, TALLA V, and GOLLAKOTA S. Bringing gesture recognition to all devices[C]. The 11th Usenix Conference on Networked Systems Design and Implementation, Seattle, USA, 2014: 303–316.
|
ZOU Yongpan, XIAO Jiang, HAN Jinsong, et al. GRfid: A device-free RFID-based gesture recognition system[J]. IEEE Transactions on Mobile Computing, 2017, 16(2): 381–393. doi: 10.1109/TMC.2016.2549518
|
ABDELNASSER H, YOUSSEF M, and HARRAS K A. WiGest: A ubiquitous WiFi-based gesture recognition system[C]. Proceedings of IEEE Conference on Computer Communications, Hongkong, China, 2015: 1472–1480.
|
ZHANG Jiajun, TAO Jinkun, and SHI Zhiguo. Doppler-radar based hand gesture recognition system using convolutional neural networks[C]. The 2017 International Conference on Communications, Signal Processing, and Systems, Singapore, 2019: 1096–1113.
|
LIEN J, GILLIAN N, KARAGOZLER M, et al. Soli: Ubiquitous gesture sensing with millimeter wave radar[J]. ACM Transactions on Graphics, 2016, 35(4): Article No.142.
|
WANG Saiwen, SONG Jie, LIEN J, et al. Interacting with Soli: Exploring fine-grained dynamic gesture recognition in the radio-frequency spectrum[C]. The 29th Annual Symposium on User Interface Software and Technology, Tokyo, Japan, 2016: 851–860.
|
HAZRA S and SANTRA A. Robust gesture recognition using Millimetric-wave radar system[J]. IEEE Sensors Letters, 2018, 2(4): 7001804.
|
MALYSA G, WANG Dan, NETSCH L, et al. Hidden Markov model-based gesture recognition with FMCW radar[C]. 2016 IEEE Global Conference on Signal and Information Processing, Washington, USA, 2016: 1017–1021.
|
王勇, 吴金君, 田增山, 等. 基于FMCW雷达的多维参数手势识别算法[J]. 电子与信息学报, 2019, 41(4): 822–829. doi: 10.11999/JEIT180485
WANG YONG, WU Jinjun, TIAN Zengshan, et al. Gesture recognition with multi-dimensional parameter using FMCW radar[J]. Journal of Electronics &Information Technology, 2019, 41(4): 822–829. doi: 10.11999/JEIT180485
|
刘熠辰, 徐丰. 基于雷达技术的手势识别[J]. 中国电子科学研究院学报, 2016, 11(6): 609–613. doi: 10.3969/j.issn.1673-5692.2016.06.009
LIU Yichen and XU Feng. Gesture recognition based on radar technology[J]. Journal of CAEIT, 2016, 11(6): 609–613. doi: 10.3969/j.issn.1673-5692.2016.06.009
|