Human Activity Recognition Based on the Method of CFAR-SOPC Using Millimeter-Wave Radar

IF 4.3 2区综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Sensors Journal Pub Date : 2025-08-11 DOI:10.1109/JSEN.2025.3595930

Fang Zhou;Xinyu Liao;Jing Fang;Mengdao Xing;Marina Gashinova

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引用次数: 0

Abstract

Human activity recognition (HAR) has become a research hotspot due to its broad application prospects in search and rescue, health monitoring, safety monitoring, and sports science. Millimeter-wave radar, with its low cost and noncontact sensing method, provides an ideal technical solution for protecting user privacy, so it is widely used in HAR research. In the study, a human motion classification method based on the constant false alarm rate-sampling the overall point cloud (CFAR-SOPC) is proposed. First, the human motion data from a millimeter-wave radar are resized into a 2-D cube. Second, the phasor average cancellation (PAC) method is applied to the data to filter out clutter, and then, the data are resized into a 3-D cube, and CFAR-SOPC is performed on the data to generate a range–Doppler (RD)–time stereo contour point cloud (SCPC), which effectively reduces the size of the features. Finally, the sample is input into the PointNet network that specializes in processing point cloud data for feature extraction and activity recognition, with an accuracy rate of 96.7%. The experimental results present a fact that compared with the existing approaches, CFAR-SOPC improves the accuracy of classification and reduces the cost of memory and time.

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基于毫米波雷达CFAR-SOPC方法的人体活动识别

人体活动识别（HAR）因其在搜救、健康监测、安全监测、运动科学等方面具有广阔的应用前景而成为研究热点。毫米波雷达以其低成本和非接触式传感方式为保护用户隐私提供了理想的技术解决方案，因此在HAR研究中得到了广泛的应用。提出了一种基于恒虚警率采样整体点云（CFAR-SOPC）的人体运动分类方法。首先，从毫米波雷达获得的人体运动数据被调整成二维立方体。其次，对数据进行相量平均对消（PAC）方法滤除杂波，然后将数据调整为三维立方体，对数据进行CFAR-SOPC处理，生成距离-多普勒（RD）时间立体轮廓点云（SCPC），有效减小特征尺寸；最后，将样本输入专门处理点云数据的PointNet网络进行特征提取和活动识别，准确率达到96.7%。实验结果表明，与现有的分类方法相比，CFAR-SOPC提高了分类的准确率，减少了记忆和时间开销。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Sensors Journal 工程技术-工程：电子与电气

CiteScore

7.70

自引率

14.00%

发文量

2058

审稿时长

5.2 months

期刊介绍： The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following: -Sensor Phenomenology, Modelling, and Evaluation -Sensor Materials, Processing, and Fabrication -Chemical and Gas Sensors -Microfluidics and Biosensors -Optical Sensors -Physical Sensors: Temperature, Mechanical, Magnetic, and others -Acoustic and Ultrasonic Sensors -Sensor Packaging -Sensor Networks -Sensor Applications -Sensor Systems: Signals, Processing, and Interfaces -Actuators and Sensor Power Systems -Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting -Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data) -Sensors in Industrial Practice