基于无线信号的人体姿态估计综述

陈彦; 张锐; 李亚东; 宋瑞源; 耿瑞旭; 龚汉钦; 汪斌全; 张东恒; 胡洋

doi:10.12000/JR24189

基于无线信号的人体姿态估计综述

DOI: 10.12000/JR24189 CSTR: 32380.14.JR24189

1.
中国科学技术大学网络空间安全学院合肥 230026
2.
华盛顿大学电气与计算机工程学院西雅图 98195

基金项目: 国家自然科学基金(62172381, 62201542)

详细信息

作者简介:
陈　彦，博士，教授，主要研究方向为多模态感知、多媒体信号处理和数字健康

张　锐，博士生，主要研究方向为多模态感知、视频图像去噪

李亚东，博士生，主要研究方向为毫米波雷达成像

宋瑞源，博士生，主要研究方向为多模态机器学习

耿瑞旭，博士生，主要研究方向为毫米波雷达成像

龚汉钦，博士生，主要研究方向为无线感知

汪斌全，博士后，主要研究方向为无线感知

张东恒，博士，副研究员，主要研究方向为无线感知

胡　洋，博士，副教授，主要研究方向为计算机视觉、多媒体信号处理和多模态感知

通讯作者:
陈彦 eecyan@ustc.edu.cn

责任主编：金添 Corresponding Editor: JIN Tian
中图分类号: TN957.51
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- 被引次数: 9
出版历程
- 收稿日期: 2024-09-16
- 修回日期: 2024-11-07
- 网络出版日期: 2024-11-26
- 刊出日期: 2025-02-28

An Overview of Human Pose Estimation Based on Wireless Signals

1.
School of Cyber Science and Technology, University of Science and Technology of China, Hefei 230026, China
2.
Department of Electrical and Computer Engineering, University of Washington, Seattle 98195, United States

Funds: The National Natural Science Foundation of China (62172381, 62201542)

More Information

Corresponding author: CHEN Yan, eecyan@ustc.edu.cn

摘要

摘要: 人体姿态估计在人机交互、动作捕捉和虚拟现实等领域具有广泛的应用前景，一直是人体感知研究的重要方向。然而，基于光学图像的姿态估计方法往往受限于光照条件和隐私问题。因此，利用可在各种光照遮挡下工作，且具有隐私保护性的无线信号进行人体姿态估计获得了更多关注。根据无线信号的工作频率，现有技术可分为高频方法和低频方法，且不同的信号频率对应硬件系统、信号特性、噪声处理和深度学习算法设计等方面均有所不同。该文将以毫米波雷达、穿墙雷达和WiFi信号为代表，回顾其在人体姿态重建研究中的进展和代表性工作，分析各类信号模式的优势与局限，并对潜在研究难点以及未来发展趋势进行了展望。
- 人体姿态估计 /
- 无线感知 /
- 深度学习 /
- 毫米波雷达 /
- 穿墙雷达 /
- WiFi
Abstract: Human pose estimation holds tremendous potential in fields such as human-computer interaction, motion capture, and virtual reality, making it a focus in human perception research. However, optical image-based pose estimation methods are often limited by lighting conditions and privacy concerns. Therefore, the use of wireless signals that can operate under various lighting conditions and obstructions while ensuring privacy is gaining increasing attention for human pose estimation. Wireless signal-based pose estimation technologies can be categorized into high-frequency and low-frequency methods. These methods differ in their hardware systems, signal characteristics, noise processing, and deep learning algorithm design based on the signal frequency used. This paper highlights research advancements and notable works in human pose reconstruction using millimeter-wave radar, through-wall radar, and WiFi. It analyzes the advantages and limitations of each signal type and explores potential research challenges and future developments in the field.
- Human pose estimation /
- Wireless sensing /
- Deep learning /
- Millimeter-wave radar /
- Through-Wall Radar (TWR) /
- WiFi

HTML全文

图 1 人体姿态模型

Figure 1. Human pose models

下载: 全尺寸图片幻灯片

图 2 RPM模型框架图^[18]

Figure 2. Diagram of the RPM framework^[18]

下载: 全尺寸图片幻灯片

图 3 基于成像的人体姿态估计方法

Figure 3. Radar imaging-based human pose estimation methods

下载: 全尺寸图片幻灯片

图 4 混凝土墙体对于信号传播路径的影响

Figure 4. The impact of concrete walls on signal propagation paths

下载: 全尺寸图片幻灯片

图 5 Person-in-WiFi 3D模型框架^[42]

Figure 5. The framework of Person-in-WiFi 3D^[42]

下载: 全尺寸图片幻灯片

表 1 基于无线信号的人体姿态估计研究现状总结

Table 1. Summary of research status on pose estimation based on wireless signals

基于频率的分类	设备	雷达特征信息	代表性工作
基于高频无线信号的人体姿态估计	毫米波雷达 (30～300 GHz)	3D point cloud	mmPose^[29]
		Heatmap	RPM^[18]
		Heatmap	RPM 2.0^[14]
		Heatmap	MobiRFPose^[19]
基于低频无线信号的人体姿态估计	穿墙雷达 (300 MHz～10 GHz)	Heatmap	RF-Pose^[34]
		Heatmap	RF-Pose3D^[36]
		单帧3D成像体素	MIMDSN^[37]
		多帧3D成像体素	ST²W-AP^[38]
		Heatmap和3D成像体素	Dual-task Net^[39]
		多帧雷达回波	RadarFormer^[40]
	WiFi (2.400～5.825 GHz)	Channel state information	Person-in-WiFi^[41]
		Channel state information	Person-in-WiFi 3D^[42]
		Channel state information	DensePose From WiFi^[43]

下载: 导出CSV

表 2 基于无线信号的人体姿态估计数据集对比

Table 2. Summary of dataset on pose estimation based on wireless signals

数据集	无线设备	真值采集设备	场景数量	行为种类	用户数量	总样本数(帧)
UWB-HA4D-1.0	穿墙雷达	RGB	3	10	11	110280
HIBER	毫米波雷达	RGB	10	4	10	402380
RT-Pose	毫米波雷达	RGB LiDAR	40	6	10	72000
mRI	毫米波雷达	RGB-D IMU	1	12	20	160000
mmBody	毫米波雷达	RGB	100	7	20	>20万
HuPR	毫米波雷达	RGB	1	3	6	141000

下载: 导出CSV

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