Handwriting Number Recognition Based on Millimeter-wave Radar with Dual-view Feature Fusion Network
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摘要: 疫情常态化背景下，非接触式人机交互在医疗、健康领域蕴藏着巨大的应用前景，其中利用手势识别方法实现非接触式的仪器操控逐渐成为研究热点。对此，该文提出一种利用毫米波雷达双视角时序特征融合来实现手势数字识别的方法，以提高手势识别的鲁棒性和准确性。首先，该文同步采集正面、侧面视角的毫米波雷达手势数字0～9的时序回波数据；接着，对各视角的数据进行预处理，实现杂波抑制、数据压缩；随后提取两方向的距离、速度的时序特征，并就特征的时间相关性构建嵌入注意力机制的双视角时序特征融合网络(ADVFNet)；最后，基于实测数据集完成了网络训练、时序特征融合、手势数字识别等步骤。实验结果表明，该文所提方法在实测数据集上识别准确率达到95%，网络收敛速度快、模型泛化能力好，与现有方法相比具有一定优势，为后续毫米波雷达人机交互提供了新思路。Abstract: Against the epidemic background, the contactless human-computer interaction has great application prospects in the medical and health field. Among them, using gesture recognition method to realize non-contact instrument control is becoming the hotspot. To improve the robustness and accuracy, a method is proposed to realize the digital gesture recognition based on dual-view sequential feature fusion of millimeter-wave radars in this paper. Firstly, time series echo data of gesture numbers 0～9 from positive and side perspectives are collected synchronously. Secondly, datasets from different perspectives are preprocessed by implementing clutter suppression and data compression. Furthermore, the Attention embedded Dual View Fusion Network (ADVFNet) is constructed based on the intrinsic correlation of temporal features. Finally, using the collected dataset, the task of training network, fusing sequential feature, and recognizing digital gesture could be completed. Experimental results show that the recognition accuracy of proposed method is about 95%, which has faster network convergence and better model generalization ability compared with several existing methods. Moreover, the method could provide a new idea for future human-computer interaction of millimeter-wave radars.
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表 1 网络具体参数说明
类型 输入尺寸 输出尺寸 说明 正向输入(Postive Input) – (128×128×1) – 侧向输入(Side Input) – (128×128×1) – 双向LSTM(Bi-LSTM) (128×128) (128×128) units = 64 注意力模块(Attention Module) (128×128) (128×128) – 相乘层(Multiply) (128×128);(128×128) (128×128) – 平铺层(Flatten) (128×128) 16384 Activation = "ReLU" 特征融合层(Add) 16384;16384 16384 Dropout = 0.3 全连接层1(Dense) 16384 256 Activation = "ReLU" 全连接层2(Dense) 256 10 Activation = "Softmax" 
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表 2 雷达参数配置
参数 数值 参数 数值 发射天线数(个) 1 帧周期(ms) 20 接收天线数(个) 4 Chirp数(个) 128 带宽(GHz) 2 采样点数(个) 64 调频斜率(MHz/μs) 50 帧数(个) 100 采样率(MHz) 2 距离分辨率(cm) 7.5
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表 3 不同情况下的纵向距离与径向距离的绝对差值与相对差值
x(cm) y=40 cm y=50 cm y=60 cm r(cm) AD(cm) RD(%) r(cm) AD(cm) RD(%) r(cm) AD(cm) RD(%) 0 40.000 0 0 50.000 0 0 60.000 0 0 1 40.012 0.012 0.031 50.010 0.010 0.020 60.008 0.008 0.014 2 40.049 0.049 0.124 50.040 0.040 0.080 60.033 0.033 0.056 3 40.112 0.112 0.280 50.090 0.090 0.180 60.075 0.075 0.125 4 40.199 0.199 0.498 50.160 0.160 0.319 60.133 0.133 0.222 5 40.311 0.311 0.778 50.249 0.249 0.499 60.208 0.208 0.347 6 40.447 0.447 1.118 50.359 0.359 0.717 60.299 0.299 0.499 7 40.607 0.607 1.519 50.488 0.488 0.975 60.407 0.407 0.678 8 40.792 0.792 1.980 50.636 0.636 1.272 60.531 0.531 0.885 9 41.000 1.000 2.500 50.804 0.804 1.607 60.671 0.671 1.119 10 41.231 1.231 3.077 50.990 0.990 1.980 60.828 0.828 1.379
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表 4 手势识别准确率(%)
输入特征 网络类型 平均准确率 输入特征 网络类型 平均准确率 PRTM Bi-LSTM 84.95 PVTM Bi-LSTM 94.55 SRTM Bi-LSTM 80.34 SVTM Bi-LSTM 74.45 PRTM+SRTM DVFNet 92.54 PVTM+SVTM DVFNet 90.95 PRTM Bi-LSTM+Attention 88.45 PVTM Bi-LSTM+Attention 94.60 SRTM Bi-LSTM+Attention 86.59 SVTM Bi-LSTM+Attention 87.00 PRTM+SRTM ADVFNet 95.30 PVTM+SVTM ADVFNet 96.80
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表 5 在单视角下ADVFNet的识别准确率
输入 网络类型 平均准确率(%) 训练时长(ms/step) SRTM+SVTM 双流卷积神经网络 89.95 323 PRTM+PVTM 双流卷积神经网络 92.65 320 SRTM+SVTM ADVFNet 93.70 57 PRTM+PVTM ADVFNet 95.12 58
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