Research Center of Intelligent Control Engineering Technology, Gannan Normal University, Ganzhou, 341000, Jiangxi, China.
School of Physics and Electronic Information, Gannan Normal University, Ganzhou, 341000, Jiangxi, China.
Sci Rep. 2024 Nov 24;14(1):29097. doi: 10.1038/s41598-024-80783-7.
Addressing the issue of low recognition accuracy in human motion detection when relying on a single feature, a novel approach integrating Frequency Modulated Continuous Wave (FMCW) radar technology with a Residual Network (ResNet) architecture has been proposed. This method commences by capturing the echo signals of six distinct human motions using an FMCW radar. These signals undergo preprocessing, followed by the application of a two-dimensional Fourier transform to derive the Range-time Map (RTM) and Doppler-time Map (DTM) representations of the human motions. To enhance the extraction and precise identification of human motion features, the conventional single-channel input structure of convolutional neural networks has been refined. Specifically, the ResNet18 residuals have been upgraded by incorporating Inception V1 modules. Furthermore, the Convolutional Block Attention Module (CBAM) has been integrated to engineer a dual-channel fusion residual network capable of recognizing and classifying human motions effectively. Empirical results demonstrate that the recognition accuracy of human motion detection has been enhanced by 1-4% when employing this dual-feature fusion structure, as compared to single-feature domain recognition. This improvement attests to the robust recognition capabilities of the proposed model.
针对单一特征在人体运动检测中识别准确率低的问题，提出了一种将调频连续波（FMCW）雷达技术与残差网络（ResNet）结构相结合的新方法。该方法首先使用 FMCW 雷达捕获六种不同人体运动的回波信号。这些信号经过预处理，然后应用二维傅里叶变换得到人体运动的 Range-time Map（RTM）和 Doppler-time Map（DTM）表示。为了增强人体运动特征的提取和精确识别，对卷积神经网络的传统单通道输入结构进行了改进。具体来说，通过引入 Inception V1 模块对 ResNet18 残差进行了升级。此外，还集成了卷积注意力模块（CBAM），构建了一个双通道融合残差网络，能够有效地识别和分类人体运动。实验结果表明，与单一特征域识别相比，采用这种双特征融合结构可将人体运动检测的识别准确率提高 1-4%，这证明了所提出模型具有强大的识别能力。
