基于连续图像深度学习的Wi-Fi人体行为识别方法

周启臻; 邢建春; 杨启亮; 韩德帅

doi:10.11959/j.issn.1000-436x.2020141

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基于连续图像深度学习的Wi-Fi人体行为识别方法

学术论文 | 更新时间：2024-06-05

- 基于连续图像深度学习的Wi-Fi人体行为识别方法
- Sequential image deep learning-based Wi-Fi human activity recognition method
- 通信学报 2020年41卷第8期页码：43-54
- 作者机构：
  
  1. 陆军工程大学国防工程学院，江苏南京 210007
  2. 火箭军工程大学作战保障学院，陕西西安 710025
- 作者简介：
  
  [ "周启臻（1993- ），男，浙江温州人，陆军工程大学博士生，主要研究方向为无线感知和智能建筑" ]
  [ "邢建春（1964- ），男，河北正定人，博士，陆军工程大学教授、博士生导师，主要研究方向为国防工程智能化和智能建筑" ]
  [ "杨启亮（1975- ），男，河南信阳人，博士，陆军工程大学副教授、硕士生导师，主要研究方向为软件工程、信息物理融合系统和智能建筑" ]
  [ "韩德帅（1990- ），男，山东聊城人，博士，火箭军工程大学讲师，主要研究方向为软件工程和国防工程智能化" ]
- 基金信息：
  
  国家重点研发计划基金资助项目(2017YFC0704100)
- DOI：10.11959/j.issn.1000-436x.2020141
  中图分类号： TN391
- 网络出版日期：2020-08，
  
  纸质出版日期：2020-08-25
- 稿件说明：
移动端阅览
周启臻, 邢建春, 杨启亮, 等. 基于连续图像深度学习的Wi-Fi人体行为识别方法[J]. 通信学报, 2020,41(8):43-54.

Qizhen ZHOU, Jianchun XING, Qiliang YANG, et al. Sequential image deep learning-based Wi-Fi human activity recognition method[J]. Journal on communications, 2020, 41(8): 43-54.
周启臻, 邢建春, 杨启亮, 等. 基于连续图像深度学习的Wi-Fi人体行为识别方法[J]. 通信学报, 2020,41(8):43-54. DOI： 10.11959/j.issn.1000-436x.2020141.

Qizhen ZHOU, Jianchun XING, Qiliang YANG, et al. Sequential image deep learning-based Wi-Fi human activity recognition method[J]. Journal on communications, 2020, 41(8): 43-54. DOI： 10.11959/j.issn.1000-436x.2020141.

摘要

针对基于深度学习的Wi-Fi人体行为识别技术存在抗噪声能力弱、信号尺寸不兼容和特征提取不充分等问题，提出了一种基于连续图像深度学习的识别方法。首先把时变Wi-Fi信号重构为若干个连续图像帧，确保输入尺寸一致；进而设计低秩分解算法，对噪声湮没的关键运动信息进行分离；同时提出一种时间域和空间域信息融合的深度模型，自动捕捉变长图像序列的时空域特征，并在WiAR数据集和自主采集数据集上对所提方法进行验证。实验结果表明，所提方法平均识别精度分别为0.94和0.96，具备普适场景下的高精度和稳健性。

Abstract

For the problems existing in most of the researches

such as weak anti-noise ability

incompatible signal size and insufficient feature extraction of deep-learning-based Wi-Fi human activity recognition

a kind of sequential image deep learning-based recognition method was proposed.Based on the idea of sequential image deep learning

a series of image frames were reconstructed from time-varied Wi-Fi signal to ensure the consistency of input size.In addition

a low-rank decomposition method was innovatively designed to separate low-rank activity information merged in noises.Finally

a deep model combining temporal stream and spatial stream was proposed to automatically capture the spatiotemporal features from length-varied image sequences.The proposed method was extensively tested in WiAR dataset and self collected dataset.The experimental results show the proposed method could achieve the accuracy of 0.94 and 0.96

which indicate its high-accuracy performance and robustness in pervasive environments.

关键词

Keywords

references

YANG Q , . Activity recognition:linking low-level sensors to high-level intelligence [C ] // Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence . New York:ACM Press , 2009 : 20 - 25 .

罗会兰 , 王婵娟 , 卢飞 . 视频行为识别综述 [J ] . 通信学报 , 2018 , 39 ( 6 ): 169 - 180 .

LUO H L , WANG C J , LU F . Survey of video behavior recognition [J ] . Journal on Communications , 2018 , 39 ( 6 ): 169 - 180 .

王钰翔 , 李晟洁 , 王皓 , 等 . 基于 Wi-Fi 的非接触式行为识别研究综述 [J ] . 浙江大学学报(工学版) , 2017 , 51 ( 4 ): 648 - 654 .

WANG Y X , LI S J , WANG H , et al . Survey on Wi-Fi based contact-less activity recognition [J ] . Journal of Zhejiang University (Engineer-ing Science) , 2017 , 51 ( 4 ): 648 - 654 .

LARA O D , LABRADOR M A . A survey on human activity recognition using wearable sensors [J ] . IEEE Communications Surveys ＆ Tutorials , 2012 , 15 ( 3 ): 1192 - 1209 .

殷晓玲 , 陈晓江 , 夏启寿 , 等 . 基于智能手机内置传感器的人体运动状态识别 [J ] . 通信学报 , 2019 , 40 ( 3 ): 157 - 169 .

YIN X L , CHEN X J , XIA Q S , et al . Human motion state recognition based on smart phone built-in sensor [J ] . Journal on Communications , 2019 , 40 ( 3 ): 157 - 169 .

HERATH S , HARANDI M , PORIKLI F . Going deeper into action recognition:a survey [J ] . Image and Vision Computing , 2017 , 60 : 4 - 21 .

BAHL P , PADMANABHAN V N , BAHL V , et al . RADAR:an in-building RF-based user location and tracking system [C ] // The 19th Annual Joint Conferences of the IEEE Computer and Communications Societies . Piscataway:IEEE Press , 2000 : 775 - 784 .

CISCO . Global mobile data traffic forecast update,2016–2021 white paper [R ] . Cisco ,(2017-02-07)[2020-02-26 ] .

鲁勇 , 吕绍和 , 王晓东 , 等 . 基于 Wi-Fi 信号的人体行为感知技术研究综述 [J ] . 计算机学报 , 2019 , 42 ( 2 ): 3 - 23 .

LU Y , LYU S H , WANG X D , et al . A survey on Wi-Fi based human behavior analysis technology [J ] . Chinese Journal of Computers , 2019 , 42 ( 2 ): 3 - 23 .

SEIFELDIN M , SAEED A , KOSBA A E , et al . Nuzzer:a large-scale device-free passive localization system for wireless environments [J ] . IEEE Transactions on Mobile Computing , 2012 , 12 ( 7 ): 1321 - 1334 .

HALPERIN D , HU W , SHETH A , et al . Tool release:gathering 802.11 n traces with channel state information [J ] . ACM SIGCOMM Computer Communication Review , 2011 , 41 ( 1 ): 53 - 53 .

党小超 , 黄亚宁 , 郝占军 , 等 . 基于信道状态信息的无源室内人员日常行为检测方法 [J ] . 通信学报 , 2019 , 40 ( 4 ): 160 - 170 .

DANG X C , HUANG Y N , HAO Z J , et al . Passive indoor human daily behavior detection method based on channel state information [J ] . Journal on Communications , 2019 , 40 ( 4 ): 160 - 170 .

PALIPANA S , ROJAS D , AGRAWAL P , et al . FallDeFi:ubiquitous fall detection using commodity Wi-Fi devices [J ] . Proceedings of the ACM on Interactive,Mobile,Wearable and Ubiquitous Technologies , 2018 , 1 ( 4 ): 1 - 25 .

ZENG Y , PATHAK P H , MOHAPATRA P . WiWho:Wi-Fi-based person identification in smart spaces [C ] // 2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks . Piscataway:IEEE Press , 2016 : 1 - 12 .

TAN S , YANG J . WiFinger:leveraging commodity Wi-Fi for fine-grained finger gesture recognition [C ] // Proceedings of the 17th ACM International Symposium on Mobile Ad Hoc Networking and Computin . New York:ACM Press , 2016 : 201 - 210 .

WANG W , LIU A X , SHAHZAD M , et al . Understanding and modeling of Wi-Fi signal based human activity recognition [C ] // Proceedings of the 21st Annual International Conference on Mobile Computing and Networking . New York:ACM Press , 2015 : 65 - 76 .

YUE S , HE H , WANG H , et al . Extracting multi-person respiration from entangled RF signals [J ] . Proceedings of the ACM on Interactive,Mobile,Wearable and Ubiquitous Technologies , 2018 , 2 ( 2 ):86.

ZOU H , ZHOU Y , YANG J , et al . Device-free occupancy detection and crowd counting in smart buildings with Wi-Fi-enabled IoT [J ] . Energy and Buildings , 2018 , 174 : 309 - 322 .

MA Y , ZHOU G , WANG S . Wi-Fi sensing with channel state information:a survey [J ] . ACM Computing Surveys (CSUR) , 2019 , 52 ( 3 ): 1 - 36 .

周鑫 , 何晓新 , 郑昌文 . 基于图像深度学习的无线电信号识别 [J ] . 通信学报 , 2019 , 40 ( 7 ): 114 - 125 .

ZHOU X , HE X X , ZHENG C W . Radio signal recognition based on image deep learning [J ] . Journal on Communications , 2019 , 40 ( 7 ): 114 - 125 .

MA Y , ZHOU G , WANG S , et al . SignFi:sign language recognition using Wi-Fi [J ] . Proceedings of the ACM on Interactive,Mobile,Wearable and Ubiquitous Technologies , 2018 , 2 ( 1 ): 1 - 21 .

ZHOU Q , XING J , CHEN W , et al . From signal to image:enabling fine-grained gesture recognition with commercial Wi-Fi devices [J ] . Sensors , 2018 , 18 ( 9 ): 3142 - 3163 .

ZHOU T , TAO D . Godec:randomized low-rank ＆ sparse matrix decomposition in noisy case [C ] // Proceedings of the 28th International Conference on Machine Learning.[S.n.:s.l . ] , 2011 : 33 - 40 .

WANG X , GAO L , SONG J , et al . Beyond frame-level CNN:saliency-aware 3-D CNN with LSTM for video action recognition [J ] . IEEE Signal Processing Letters , 2016 , 24 ( 4 ): 510 - 514 .

GUO L , WANG L , LIN C , et al . WiAR:a public dataset for Wi-Fi-based activity recognition [J ] . IEEE Access , 2019 , 7 ( 1 ): 154935 - 154945 .

QIAN K , WU C , ZHANG Y , et al . Widar2.0:passive human tracking with a single Wi-Fi link [C ] // Proceedings of the 16th Annual International Conference on Mobile Systems,Applications,and Services . New York:ACM Press , 2018 : 350 - 361 .

YOSINSKI J , CLUNE J , BENGIO Y , et al . How transferable are features in deep neural networks? [J ] . In Advances in Neural Information Processing Systems , 2014 , 1 ( 1 ): 3320 - 3328 .

WANG F , GONG W , LIU J . On spatial diversity in Wi-Fi-based human activity recognition:a deep learning-based approach [J ] . IEEE Internet of Things Journal , 2018 , 6 ( 2 ): 2035 - 2047 .

OHARA K , MAEKAWA T , MATSUSHITA Y . Detecting state changes of indoor everyday objects using Wi-Fi channel state information [J ] . Proceedings of the ACM on Interactive,Mobile,Wearable and Ubiquitous Technologies , 2017 , 1 ( 3 ): 1 - 28 .

YOUSEFI S , NARUI H , DAYAL S , et al . A survey on behavior recognition using Wi-Fi channel state information [J ] . IEEE Communications Magazine , 2017 , 55 ( 10 ): 98 - 104 .

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