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A Review of device-free Indoor Positioning for Home-Based Care of the Aged:Techniques and Technologies

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Computer Modeling in Engineering & Sciences 2023年第6期135卷 1901-1940页

作者： Geng Chen Lili Cheng Rui Shao Qingbin Wang Shuihua Wang College of Electronic and Information Engineering Shandong University of Science and TechnologyQingdao266590China School of Computer Science and Technology Henan Polytechnic UniversityJiaozuo454000China

With the development of urbanization,the problem of neurological diseases brought about by population aging has gradually become a social problem of worldwide concern.Aging leads to gradual degeneration of the central nervous system,shrinkage of brain tissue,and decline in physical function in many elderlies,making them susceptible to neurological diseases such as Alzheimer’s disease(AD),stroke,Parkinson’s and major depressive disorder(MDD).Due to the influence of these neurological diseases,the elderly have troubles such as memory loss,inability to move,falling,and getting lost,which seriously affect their quality of life.Tracking and positioning of elderly with neurological diseases and keeping track of their location in real-time are necessary and crucial in order to detect and treat dangerous and unexpected situations in time.Considering that the elderly with neurological diseases forget to wear a positioning device or have mobility problems due to carrying a positioning device,device-free positioning as a passive positioning technology that detects device-free individuals is more suitable than traditional active positioning for the home-based care of the elderly with neurological diseases.This paper provides an extensive and in-depth survey of device-free indoor positioning technology for home-based care and an in-depth analysis of the main features of current positioning systems,as well as the techniques,technologies andmethods they employ,fromthe perspective of the needs of the elderly with neurological conditions.Moreover,evaluation criteria and possible solutions of positioning techniques for the home-based care of the elderly with neurological conditions are proposed.Finally,the opportunities and challenges for the development of indoor positioning technology in 6G mobile networks for home-based care of the elderly with neurological diseases are discussed.This review has provided comprehensive and effective tracking and positioning techniques,technologies and methods for the elderly,by which we can obta

关键词： Home-based care device-free neurological diseases indoor positioning positioning techniques and technologies positioning accuracy

device-free human micro-activity recognition method using WiFi signals

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Geo-Spatial Information Science 2019年第2期22卷 128-137,I0005页

作者： Mohammed A.A.Al-qaness School of Computer Science Wuhan UniversityWuhanChina

Human activity tracking plays a vital role in human–computer interaction.Traditional human activity recognition(HAR)methods adopt special devices,such as cameras and sensors,to track both macro-and micro-activities.Recently,wireless signals have been exploited to track human motion and activities in indoor environments without additional equipment.This study proposes a device-free WiFi-based micro-activity recognition method that leverages the channel state information(CSI)of wireless signals.Different from existed CSI-based microactivity recognition methods,the proposed method extracts both amplitude and phase information from CSI,thereby providing more information and increasing detection accuracy.The proposed method harnesses an effective signal processing technique to reveal the unique patterns of each activity.We applied a machine learning algorithm to recognize the proposed micro-activities.The proposed method has been evaluated in both line of sight(LOS)and none line of sight(NLOS)scenarios,and the empirical results demonstrate the effectiveness of the proposed method with several users.

关键词： Human activity recognition channel state information WiFi device-free microactivity recognition machine learning

Multi-Person device-free Gesture Recognition Using mmWave Signals

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China Communications 2021年第2期18卷 186-199页

作者： Jie Wang Zhouhua Ran Qinghua Gao Xiaorui Ma Miao Pan Kaiping Xue School of Information Science and Technology Dalian Maritime UniversityDalian 116026China Faculty of Electronic Information and Electrical Engineering Dalian University of TechnologyDalian 116023China Department of Electrical and Computer Engineering University of HoustonHouston 77004USA Department of Information Security University of Science and Technology of ChinaHefei 230027China

device-free gesture recognition is an emerging wireless sensing technique which could recognize gestures by analyzing its influence on surrounding wireless signals,it may empower wireless networks with the augmented sensing ability.Researchers have made great achievements for singleperson device-free gesture recognition.However,when multiple persons conduct gestures simultaneously,the received signals will be mixed together,and thus traditional methods would not work well anymore.Moreover,the anonymity of persons and the change in the surrounding environment would cause feature shift and mismatch,and thus the recognition accuracy would degrade remarkably.To address these problems,we explore and exploit the diversity of spatial information and propose a multidimensional analysis method to separate the gesture feature of each person using a focusing sensing strategy.Meanwhile,we also present a deep-learning based robust device free gesture recognition framework,which leverages an adversarial approach to extract robust gesture feature that is insensitive to the change of persons and environment.Furthermore,we also develop a 77GHz mmWave prototype system and evaluate the proposed methods extensively.Experimental results reveal that the proposed system can achieve average accuracies of 93%and 84%when 10 gestures are conducted in Received:Jun.18,2020 Revised:Aug.06,2020 Editor:Ning Ge different environments by two and four persons simultaneously,respectively.

关键词： device-free gesture recognition wireless sensing multi-person deep-learning

Deep learning and transfer learning for device-free human activity recognition:A survey

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Journal of Automation and Intelligence 2022年第1期1卷 34-47页

作者： Jianfei Yang Yuecong Xu Haozhi Cao Han Zou Lihua Xie The School of Electrical and Electronics Engineering Nanyang Technological UniversitySingapore

device-free activity recognition plays a crucial role in smart building,security,and human–computer interaction,which shows its strength in its convenience and cost-efficiency.Traditional machine learning has made significant progress by heuristic hand-crafted features and statistical models,but it suffers from the limitation of manual feature design.Deep learning overcomes such issues by automatic high-level feature extraction,but its performance degrades due to the requirement of massive annotated data and cross-site issues.To deal with these problems,transfer learning helps to transfer knowledge from existing datasets while dealing with the negative effect of background dynamics.This paper surveys the recent progress of deep learning and transfer learning for device-free activity recognition.We begin with the motivation of deep learning and transfer learning,and then introduce the major sensor modalities.Then the deep and transfer learning techniques for device-free human activity recognition are introduced.Eventually,insights on existing works and grand challenges are summarized and presented to promote future research.

关键词： Human activity recognition Deep learning Transfer learning Domain adaptation Action recognition device-free

device-free Through-the-Wall Activity Recognition Using Bi-Directional Long Short-Term Memory and WiFi Channel State Information

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Journal of Electronic Science and Technology 2021年第4期19卷 357-368页

作者： Zi-Yuan Gong Xiang Lu Yu-Xuan Liu Huan-Huan Hou Rui Zhou with the School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu 610054

Activity recognition plays a key role in health management and security.Traditional approaches are based on vision or wearables,which only work under the line of sight(LOS)or require the targets to carry dedicated devices.As human bodies and their movements have influences on WiFi propagation,this paper proposes the recognition of human activities by analyzing the channel state information(CSI)from the WiFi physical layer.The method requires only the commodity:WiFi transmitters and receivers that can operate through a wall,under LOS and non-line of sight(NLOS),while the targets are not required to carry dedicated devices.After collecting CSI,the discrete wavelet transform is applied to reduce the noise,followed by outlier detection based on the local outlier factor to extract the activity segment.Activity recognition is fulfilled by using the bi-directional long short-term memory that takes the sequential features into consideration.Experiments in through-the-wall environments achieve recognition accuracy>95%for six common activities,such as standing up,squatting down,walking,running,jumping,and falling,outperforming existing work in this field.

关键词： Activity recognition bi-directional long short-term memory(Bi-LSTM) channel state information(CSI) device-free through-the-wall.

基于device-free技术的被动式行为感知及应用研究

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基于Device-free技术的被动式行为感知及应用研究

作者：卢欣欣郑州大学

学位级别：硕士

随着无线感知技术的快速发展和人机交互需求的迅猛增长,基于device-free技术的人体行为识别成为研究的热点之一。相对于传统的计算机视觉技术和可穿戴传感技术,WiFi技术不仅可以用来传输数据,还可以以device-free方式感知周围环境信息... 详细信息

随着无线感知技术的快速发展和人机交互需求的迅猛增长,基于device-free技术的人体行为识别成为研究的热点之一。相对于传统的计算机视觉技术和可穿戴传感技术,WiFi技术不仅可以用来传输数据,还可以以device-free方式感知周围环境信息。然而,受多径效应以及周围环境影响,基于WiFi信号的行为数据复杂度较高;另外,现有人体行为识别方法需要手工提取特征,较大程度依赖于先验知识。本文以独居老人为研究对象,使用商用WiFi设备采集物理层信道状态信息(Channel State Information,CSI),并采用深度学习实现对粗粒度和细粒度的人体行为感知研究。本文主要研究内容如下:(1)针对日常生活中幅度大、频率高的走路等人体行为,本文提出了一种基于深度学习的粗粒度人体行为识别模型LTRNN。该模型通过自动提取低层次到高层次的行为特征,分析人体行为与CSI数据之间的内在关系。另外,通过分析CSI数据,选取具有代表性的子载波,通过滑动窗口的方式将长时间行为序列分割成短时行为,以提高模型构建和训练的效率。实验结果表明,在不同滑窗和不同采样率等识别问题上,本文提出模型均有较好的识别效果。(2)针对睡眠期间幅度小、频率低的翻身等人体行为,本文构建了一个深层次的细粒度人体睡眠行为识别模型BIRM-RNN。该模型使用双向循环神经网络学习CSI数据的上下文信息,并将融合后的上下文信息输入独立残差循环神经网络中,提取该类行为的深层次数据特征。同时,为了解决深层次网络梯度消失的问题,利用Batch Normalization(BN)方法对模型进行优化。在不同场景下的大量实验结果表明,该模型在各评价指标上均达到了93.5%以上。综上,本文实现了一种被动式、无隐私、零负担的人体行为识别方法,对独居老人在日常和睡眠期间各种行为进行分析与识别。该方法不仅充分利用现有资源,节省人力物力,而且为基于无线感知的人体行为识别提供了新思路,可以广泛应用在家庭健康监测等领域。

关键词：人体行为识别 device-free 信道状态信息深度学习循环神经网络

Adaptive Scheme for Crowd Counting Using off-the-Shelf Wireless Routers

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Computer Systems Science & Engineering 2022年第4期41卷 255-269页

作者： Wei Zhuang Yixian Shen Chunming Gao Lu Li Haoran Sang Fei Qian School of Computer and Software Nanjing University of Information Science&TechnologyNanjing210044China Engineering Research Center of Digital Forensics Ministry of EducationNanjing University of Information Science and TechnologyNanjing210044China School of Engineering&Technology University of WashingtonTacomaWA 98402USA China General Nuclear Power Group Nanjing210028China Jiangsu Province Hospital of Chinese Medicine Affiliated Hospital of Nanjing University of Chinese MedicineNanjing210029China

Since the outbreak of the world-wide novel coronavirus pandemic,crowd counting in public areas,such as in shopping centers and in commercial streets,has gained popularity among public health administrations for preventing the crowds from gathering.In this paper,we propose a novel adaptive method for crowd counting based on Wi-Fi channel state information(CSI)by using common commercial wireless routers.Compared with previous researches on device-free crowd counting,our proposed method is more adaptive to the change of environ-ment and can achieve high accuracy of crowd count estimation.Because the dis-tance between access point(AP)and monitor point(MP)is typically non-ﬁxed in real-world applications,the strength of received signals varies and makes the tra-ditional amplitude-related models to perform poorly in different environments.In order to achieve adaptivity of the crowd count estimation model,we used convo-lutional neural network(ConvNet)to extract features from correlation coefﬁcient matrix of subcarriers which are insensitive to the change of received signal strength.We conducted experiments in university classroom settings and our model achieved an overall accuracy of 97.79%in estimating a variable number of participants.

关键词： CSI device-free deep learning crowd counting Wi-Fi wireless sensing