摘要
针对现有的手势识别方法存在数据集过少、利用特征信息较少和神经网络部分提取信息不充分的问题,提出一种基于毫米波雷达传感器RAI图像的手势识别方法。首先使用TI公司的IWR1443毫米波雷达传感器采集10类手势数据构建数据集,再通过对手部反射的雷达信号进行时频分析,获取固定帧数的RDI和RAI。为了充分提取手势特征并精确分类,在卷积神经网络基础上,引入了残差块和通道注意力块。实验结果表明,相较其他特征如RDI,RAI能更准确的表征手势,所提出的网络相比于CNN方法准确率提高了12.72%,相比于VGG16-Net和单参数VGG16-Net方法准确率提高了8.93%与10.41%,参数量降低了90.68%,时间复杂度降低了17.2%。
Aiming at the problems that the existing gesture recognition methods have too few datasets,less feature information and insufficient information extracted by the neural network,a gesture recognition method based on RAI image of millimeter-wave radar sensor is proposed.First,use TI's IWR1443 millimeter-wave radar sensor to collect 10 types of gesture data to build a dataset,and then perform time-frequency analysis on the radar signal reflected by the hand to obtain a fixed frame number of RDI and RAI.In order to fully extract gesture features and classify them accurately,residual blocks and channel attention blocks are introduced on the basis of convolutional neural network.Experimental results show that compared with other features such as RDI,RAI can more accurately characterize gestures,the accuracy of the proposed network is increased by 11.78%compared with the CNN method,the accuracy rate of VGG16-Net and single-parameter VGG16-Net is increased by 7.98%and 11.78%,the parameter volume is reduced by 90.68%,and the time complexity is reduced by 17.2%.
作者
许妍
常俊
吴彭
罗金燕
王义元
Xu Yan;Chang Jun;Wu Peng;Luo Jinyan;Wang Yiyuan(College of information,Yunnan University,Kunming 650500,China;University Key Laboratory of Internet of Things Technology and Application,Kunming 650500,China)
出处
《电子测量技术》
北大核心
2023年第6期15-22,共8页
Electronic Measurement Technology
基金
国家自然科学基金(61562090)
云南大学研究生实践创新基金(2021Y190)项目资助
关键词
手势识别
毫米波传感器
距离角度图像
残差注意力
卷积神经网络
gesture recognition
millimeter wave sensor
range-angle image
residual attention
convolutional neural networks
