基于多通道调频连续波毫米波雷达的微动手势识别

夏朝阳; 周成龙; 介钧誉; 周涛; 汪相锋; 徐丰

doi:10.11999/JEIT190797

基于多通道调频连续波毫米波雷达的微动手势识别

doi: 10.11999/JEIT190797

复旦大学电磁波信息科学教育部重点实验室上海 200433

基金项目: 国家自然科学基金(61822107)

详细信息

作者简介:
夏朝阳：男，1993年生，博士生，研究方向为雷达信号处理、目标识别和深度学习

周成龙：男，1995年生，硕士生，研究方向为雷达目标识别和深度学习应用

介钧誉：男，1993年生，硕士生，研究方向为雷达目标识别和深度学习应用

周涛：男，1996年生，硕士生，研究方向为雷达目标识别和深度学习应用

汪相锋：男，1995年生，硕士生，研究方向为雷达目标识别和深度学习应用

徐丰：男，1982年生，教授，博士生导师，研究方向为SAR图像解译、电磁散射建模和类脑人工智能

通讯作者:
徐丰　fengxu@fudan.edu.cn

中图分类号: TN958.94
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出版历程
- 收稿日期: 2019-10-16
- 修回日期: 2019-11-27
- 网络出版日期: 2019-12-09
- 刊出日期: 2020-01-21

Micro-motion Gesture Recognition Based on Multi-channel Frequency Modulated Continuous Wave Millimeter Wave Radar

Key Laboratory for Information Science of Electromagnetic Waves(MoE), Fudan University, Shanghai 200433, China

Funds: The National Natural Science Foundation of China (61822107)

摘要

摘要:
该文提出一种基于多通道调频连续波(FMCW)毫米波雷达的微动手势识别方法，并给出一种微动手势特征提取的最优雷达参数设计准则。通过对手部反射的雷达回波进行时频分析处理，估计目标的距离多普勒谱、距离谱、多普勒谱和水平方向角度谱。设计固定帧时间长度拼接的距离-多普勒-时间图特征，与距离-时间特征、多普勒-时间特征、水平方向角度-时间图特征和三者联合特征等，分别对7类微动手势进行表征。根据手势运动过程振幅和速度差异，进行手势特征捕获和对齐。利用仅有5层的轻量化卷积神经网络对微动手势特征进行分类。实验结果表明，相较其他特征，设计的距离-多普勒-时间图特征能够更为准确地表征微动手势，且对未经训练的测试对象具有更好的泛化能力。
- 毫米波雷达 /
- 微动手势识别 /
- 调频连续波 /
- 卷积神经网络
Abstract:
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.
- Millimeter wave radar /
- Micro-motion gesture recognition /
- Frequency Modulated Continuous Wave(FMCW) /
- Convolutional Neural Network(CNN)

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图 1 实验流程图

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图 2 7类微动手势的动作示意图与5种特征图

下载: 全尺寸图片幻灯片

图 3 单通道与8通道平均的手势帧RD图信噪比对比

下载: 全尺寸图片幻灯片

表 1 不同时间长度特征的分类准确率对比(%)

数据集长度	6帧	8帧	10帧	15帧
平均分类准确率	76.86	91.86	99.14	99.29

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表 2 多种手势表征方法的对比

特征类型	分类方法	5名训练对象平均分类准确率(%)
SC-RDTM	单通道CNN	98.28
CA-RDTM	单通道CNN	99.14
CA-DTM	单通道CNN	97.14
CA-RTM	单通道CNN	88.00
HATM	单通道CNN	71.71
CA-RTM, CA-DTM 与HATM联合	3通道CNN	93.57

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表 3 多种手势表征方法的对比

特征类型	分类方法	测试对象A平均分类准确率(%)	测试对象B平均分类准确率(%)
SC-RDTM	单通道CNN	86.00	84.29
CA-RDTM	单通道CNN	87.71	85.43
CA-DTM	单通道CNN	84.57	83.43
CA-RTM	单通道CNN	27.14	25.42
HATM	单通道CNN	34.28	30.57
CA-RTM, CA-DTM与HATM联合	3通道CNN	65.14	55.71

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表 4 7种微动手势分类的混淆矩阵

		预测类别
		食指双击	食指顺时针绕圈	食指逆时针绕圈	食指拇指分开	食指拇指并拢	拇指在食指上前搓动	拇指在食指上后搓动	准确度(%)
真实类别	食指双击	100	0	0	0	0	0	0	100
	食指顺时针绕圈	0	100	0	0	0	0	0	100
	食指逆时针绕圈	0	0	100	0	0	0	0	100
	食指拇指分开	0	0	0	100	0	0	0	100
	食指拇指并拢	0	0	0	0	98	0	2	98
	拇指在食指上前搓动	0	0	0	0	0	100	0	100
	拇指在食指上后搓动	0	0	0	0	4	0	96	96
	准确度(%)	100	100	100	100	96.08	100	97.96	99.14

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表 5 测试对象A 7类微动手势分类的混淆矩阵

		预测类别
		食指双击	食指顺时针绕圈	食指逆时针绕圈	食指拇指分开	食指拇指并拢	拇指在食指上前搓动	拇指在食指上后搓动	准确度(%)
真实类别	食指双击	46	3	1	0	0	0	0	92
	食指顺时针绕圈	0	35	15	0	0	0	0	70
	食指逆时针绕圈	3	13	34	0	0	0	0	68
	食指拇指分开	0	0	0	49	0	1	0	98
	食指拇指并拢	0	0	0	0	46	0	4	92
	拇指在食指上前搓动	0	0	0	1	0	49	0	98
	拇指在食指上后搓动	0	0	0	0	2	0	48	96
	准确度(%)	93.88	68.63	68	98	95.83	98	92.31	87.71

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表 6 测试对象B 7类微动手势分类的混淆矩阵

		预测类别
		食指双击	食指顺时针绕圈	食指逆时针绕圈	食指拇指分开	食指拇指并拢	拇指在食指上前搓动	拇指在食指上后搓动	准确度(%)
真实类别	食指双击	45	2	3	0	0	0	0	90
	食指顺时针绕圈	0	32	18	0	0	0	0	64
	食指逆时针绕圈	1	16	33	0	0	0	0	66
	食指拇指分开	0	0	0	46	0	4	0	92
	食指拇指并拢	0	0	0	0	48	0	2	96
	拇指在食指上前搓动	0	0	0	2	0	48	0	96
	拇指在食指上后搓动	0	0	0	0	3	0	47	94
	准确度(%)	97.83	64	61.11	95.83	94.12	92.31	95.92	85.43

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参考文献(19)

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

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