基于RDSNet的毫米波雷达人体跌倒检测方法

元志安; 周笑宇; 刘心溥; 卢大威; 邓彬; 马燕新

doi:10.12000/JR21015

基于RDSNet的毫米波雷达人体跌倒检测方法

DOI: 10.12000/JR21015

1.
国防科技大学电子科学学院长沙 410073
2.
国防科技大学气象海洋学院长沙 410073

基金项目: 国家自然科学基金(61871386)，湖南省杰出青年基金(2019JJ20022)

详细信息

作者简介:
元志安(1997–)，男，山东潍坊人，国防科技大学电子科学学院硕士研究生，主要研究方向为智能信息感知与处理

周笑宇(1997–)，男，重庆北碚人，国防科技大学电子科学学院硕士研究生，主要研究方向为三维点云目标跟踪

刘心溥(1997–)，男，河北石家庄人，国防科技大学电子科学学院硕士研究生，主要研究方向为三维点云补全

卢大威(1981–)，男，湖北安陆人，国防科技大学电子科学学院讲师，主要研究方向为雷达系统仿真、雷达信号处理、随机有限集滤波与扩展目标跟踪

邓　彬(1981–)，男，山东邹城人，国防科技大学电子科学学院副研究员，主要研究方向为合成孔径雷达、太赫兹雷达微动与成像

马燕新(1989–)，男，江西永新人，国防科技大学气象海洋学院讲师，主要研究方向为三维计算机视觉

通讯作者:
卢大威 davidloo.nudt@gmail.com

责任主编：黄磊 Corresponding Editor: HUANG Lei
中图分类号: TN957.51
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出版历程
- 收稿日期: 2021-02-26
- 修回日期: 2021-07-13
- 网络出版日期: 2021-08-28

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

1.
College of Electronic Science, National University of Defense Technology, Changsha 410073, China
2.
College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China

Funds: The National Natural Science Foundation of China (61871386), The Natural Science Fund for Distinguished Young Scholars of Hunan Province (2019JJ20022)

More Information

Corresponding author: LU Dawei, davidloo.nudt@gmail.com

摘要

摘要: 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。
- 毫米波雷达 /
- 跌倒检测 /
- 距离多普勒 /
- 卷积神经网络 /
- 长短时记忆网络
Abstract: 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.
- Millimeter-wave radar /
- Fall detection /
- Range Doppler /
- Convolutional Neural Network (CNN) /
- Long Short-Term Memory network (LSTM)

HTML全文

图 1 跌倒检测系统构成

Figure 1. The pipeline of fall detection

下载: 全尺寸图片幻灯片

图 2 LSTM网络单元

Figure 2. The unit of LSTM network

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图 3 RDSNet网络结构

Figure 3. The structure of RDSNet

下载: 全尺寸图片幻灯片

图 4 实验流程

Figure 4. The process of experiments

下载: 全尺寸图片幻灯片

图 5 实验场景和雷达视角示意图

Figure 5. Experimental scene and radar perspective diagram

下载: 全尺寸图片幻灯片

图 6 4种动作分类RDSNet混淆矩阵

Figure 6. RDSNet confusion matrix for 4 types of action classification

下载: 全尺寸图片幻灯片

表 1 动作设计及距离多普勒热图序列

Table 1. Motion design and range Doppler heat map sequence

动作	距离多普勒热图序列
跌倒
挥手
起立
静止
走动
翻身

下载: 导出CSV

表 2 不同检测方法结果对比

Table 2. Comparative experimental results of different detection methods

分类	方法模型	传感器	动作种类	检测准确率(%)	实时
穿戴式	数据融合检测法^[3]	加速度计+RGBD相机	4	99.00	否
	基于CNN的手机跌倒检测模型^[4]	加速度计+陀螺仪+线性加速度计	2	98.51	是
	基于RNN的可穿戴跌倒检测器^[21]	加速度计	2	96.70	是
非穿戴式	基于深度学习的多患者行为检测^[13]	毫米波雷达	6	84.49	是
	基于RGB-D跌倒检测算法^[7]	RGBD相机	3	98.00	是
	复杂场景下的跌倒检测算法^[8]	RGBD相机	2	89.80	否
	基于超宽带雷达的跌倒检测算法^[12]	超宽带雷达(3个)	2	90.00	否
	智能家居超宽带雷达跌倒检测^[11]	超宽带雷达	2	97.50	是
	老年人智能检测系统^[9]	WiFi	6	84.60	否
	RDSNet	毫米波雷达	2	98.33	是(<50 ms)
			4	96.67
			6	93.33

下载: 导出CSV

表 3 相同传感器下不同网络结构的结果对比

Table 3. The results of different network structures under the same sensor

方法模型	网络结构	动作种类	检测准确率(%)	实时
基于深度学习的多患者行为检测^[13]	CNN	6	84.49	是
基于LKCNN的跌倒检测算法^[14]	LKCNN	2	95.24	是
基于Bi-SLAM的人体行为连续分类检测^[15]	Bi-LSTM	6	91.00	否
基于Bi-SLAM的多模态人体姿态识别与跌倒检测^[16]	Bi-LSTM	6	88.90	否
本文测试	CNN	6	89.00	是(<5 ms)
	LSTM	6	76.66	是(<35 ms)
	CNN+LSTM	2	98.33	是(<50 ms)
	CNN+LSTM	6	93.33	是(<50 ms)

下载: 导出CSV

表 4 不同网络结构组合结果对比

Table 4. Comparison results of different network structure combinations

CNN(层)	LSTM(层)	准确率(%)	漏警率(%)	虚警率(%)	时延(ms)
1	1	89.00	11.00	15	<38
2	1	96.67	3.33	8	<50
3	1	92.67	7.33	6	<69
4	1	67.00	33.00	26	<80
1	2	91.67	8.33	21	>100
2	2	95.33	4.67	11	>100
3	2	84.00	16.00	16	>100
4	2	61.33	38.67	34	>100

下载: 导出CSV

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