基于RDSNet的毫米波雷达人体跌倒检测方法

随着人口老龄化的到来，跌倒检测逐渐成为研究热点。针对基于毫米波雷达的人体跌倒检测应用，该文提出了一种融合卷积神经网络和长短时记忆网络的距离多普勒热图序列检测网络(RDSNet)模型。首先通过卷积神经网络对距离多普勒热图进行特征提取得到特征向量，然后将动态序列对应的特征向量序列依次输入长短时记忆网络，进而学习得到热图序列的时间相关性信息，最后通过分类器网络得到检测结果。利用毫米波雷达采集了不同对象的多种人体动作，构建了距离多普勒热图数据集。对比试验表明，所提出的RDSNet网络模型检测准确率可达到96.67%，计算时延小于50 ms，而且具有良好的泛化能力，可为跌倒检测和人体姿态识别提供新的技术思路。 

Human Fall Detection Method Using Millimeter-wave Radar Based on RDSNet

With the advent of the aging population, fall detection has gradually become a research hotspot. Aiming at the detection of human fall using millimeter-wave radar, a Range-Doppler heat map Sequence detection Network (RDSNet) model that combines the convolutional neural network and long short-term memory network is proposed in this study. First, feature extraction is performed using the convolutional neural network. After obtaining the feature vector, the feature vector corresponding to the dynamic sequence is inputted to the long short-term memory network. Subsequently, the time correlation information of the heat map sequence is learned. Finally, the detection results are obtained using the classifier. Moreover, diverse human movement information of different objects is collected using millimeter-wave radar, and a range-Doppler heat map dataset is built in this work. Comparative experiments show that the proposed RDSNet model can reach an accuracy of 96.67% and the calculation delay is not higher than 50 ms. The proposed RDSNet model has good generalization capabilities and provides new technical ideas for human fall detection and human posture recognition.