[go: up one dir, main page]

WO2018133264A1 - Procédé et système de détection automatique de positionnement de corps humain à l'intérieur - Google Patents

Procédé et système de détection automatique de positionnement de corps humain à l'intérieur Download PDF

Info

Publication number
WO2018133264A1
WO2018133264A1 PCT/CN2017/084427 CN2017084427W WO2018133264A1 WO 2018133264 A1 WO2018133264 A1 WO 2018133264A1 CN 2017084427 W CN2017084427 W CN 2017084427W WO 2018133264 A1 WO2018133264 A1 WO 2018133264A1
Authority
WO
WIPO (PCT)
Prior art keywords
human body
positioning
wireless signal
channel state
information
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/CN2017/084427
Other languages
English (en)
Chinese (zh)
Inventor
伍楷舜
黄梓琪
王璐
明仲
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shenzhen University
Original Assignee
Shenzhen University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Shenzhen University filed Critical Shenzhen University
Publication of WO2018133264A1 publication Critical patent/WO2018133264A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/0257—Hybrid positioning

Definitions

  • the present invention relates to positioning technology, and in particular to a method for automatically detecting indoor positioning of a human body, and to a system for implementing the method.
  • the detection system built by these methods has its own shortcomings.
  • the visual positioning detection system it is necessary to mount a high-resolution camera in a detection environment in which the subject is located to take a large number of images, and then to track the positioning by the captured images.
  • installing the camera will in some way infringe on the personal privacy of the subject.
  • the visual positioning detection system cannot work effectively in dark conditions.
  • the wavelength should not be too short, otherwise it will be greatly affected by scattering, reflection, and multipath, which makes the positioning accuracy low.
  • other sounds or other environmental factors around the subject can easily interfere with the operation of the environmental device, resulting in lower accuracy.
  • UWB Ultra Wideband, carrierless communication technology
  • UWB pulse radio technology for indoor positioning, although the average transmission power is very low, can be "quiet coexistence" with other wireless communication systems, low energy consumption, low cost, good confidentiality, anti- Multipath interference and other advantages, but at the same time, the pulse UWB system has low spectrum utilization and is not suitable for high data rate transmission. Another important reason is UWB positioning. It is necessary to be additionally tagged by people and objects (relative to the natural label of the terminal such as a mobile phone), and these methods will cause a certain degree of inconvenience to the life of the subject.
  • the present invention provides a method for automatically detecting indoor positioning of a human body, and a system for realizing the automatic detection method for indoor positioning of the human body.
  • the method for automatically detecting indoor positioning of a human body comprises the following steps:
  • S1 the wireless receiving end receives the wireless signal transmitted by the wireless signal transmitter, and uploads the wireless signal information to the server;
  • the present invention is further improved, and further includes a parameter correction step A: correcting the parameters used in the algorithm detected in step S3 by using a sensor attached to the human body.
  • the present invention is further improved.
  • the sensor includes a sensor attached to the mobile phone, and the sensor included in the mobile phone includes a gravity sensor, an acceleration sensor, and a gyroscope.
  • the invention is further improved, and the parameter correction method comprises:
  • A1 setting the start and end of the human body motion, recording the motion characteristics with the sensor, and recording the time period;
  • A2 The sensor calculates the number of steps of human motion during this time period
  • A3 The speed of the human body is calculated by the time period and the number of steps, and the parameters of the detection algorithm are corrected.
  • step S2 evaluating channel state information includes the following steps:
  • S21 Collect channel state data, where the channel state data includes CSI values of M subcarriers in N spatial streams, where N and M are both natural numbers greater than one;
  • S22 For each spatial stream, obtain an average value of CSI values of P consecutive subcarriers at the same time point, and use the average value as channel state information, and P is a natural number greater than 1 and less than M;
  • step S3 includes the following steps:
  • S32 receiving wireless signal transmitter coordinates in the network layer and channel state information CSI from the physical layer;
  • step S3 further includes step S34: adaptively modifying parameters in the positioning algorithm in step S33 by using a fingerprint card algorithm.
  • step S3 further includes step S35: establishing a database, and positioning the bit Set the sample to map to the radio map.
  • step S35 a statistical machine learning is used to create a determination region for the channel response mode, and the received wireless signals are cross-correlated for location identification.
  • the method for the statistical machine to learn to identify the location includes the following steps:
  • B2 statistics the cross-correlation of the received wireless signals of the overall statistical information of the channel responses at each grid point;
  • B4 The Mahalanobis distance is calculated according to the multidimensional Gaussian distribution and the maximum likelihood estimation method, and the human body position is determined by the best match with the radio signal in the radio map in the indoor propagation model.
  • the present invention also provides a system for implementing the method, comprising: a wireless signal transmitter for transmitting a wireless signal; and a wireless receiving end for receiving a wireless signal transmitted by the wireless signal transmitter and uploading the wireless signal information to the server, Obtaining the positioning result returned by the server; the server is configured to receive the wireless signal information of the wireless receiving end, and evaluate the channel state information, detect the positioning information of the human body according to the change of the channel state information, and then return the calculated positioning result to the wireless receiving end.
  • the invention has the beneficial effects that the detection accuracy of the detected action is 84% to 94% in an indoor environment with less decoration, and in an indoor environment with more decorations, the detection is performed.
  • Accuracy rate can reach 78%; can accurately locate the human body indoors, and use the system's self-learning function to deal with false positives, further reduce the false positive rate;
  • based on the existing wireless network and equipment, carry out indoor Inspection work no need to install other specific detection equipment in the tested environment, can be used in any environment spread over the wireless network, has a very high popularity, and the testee does not need to carry any additional sensing equipment, avoiding being detected
  • the inconvenience caused by carrying the testing equipment provides convenience for their lives.
  • FIG. 1 is a schematic structural diagram of a system according to an embodiment of the present invention.
  • FIG. 2 is a schematic diagram of a server implementation process of the present invention
  • FIG. 3 is a flow chart of a method according to an embodiment of the present invention.
  • the automatic indoor positioning automatic detection system of the present invention comprises a wireless signal transmitter for transmitting a wireless signal, and a wireless receiving end for receiving a wireless signal transmitted by the wireless signal transmitter and uploading the wireless signal information to the server.
  • the wireless receiving end is a wireless network card
  • the wireless signal transmitter is a wireless router
  • the server is a mobile phone
  • the method is based on a radio propagation mechanism in an indoor environment, and establishes a wireless signal and a human body movement.
  • the relationship between the distances only needs to use the existing wireless network equipment of the home, that is, the change of the wireless signal caused by the distance of the detected person can be analyzed, thereby obtaining a precise indoor positioning position.
  • the rich channel state information of the wireless network can be collected through the wireless network card.
  • the system has a plurality of antennas for transmitting and receiving wireless signals, respectively; the wireless network card used by the system can receive channel state information.
  • the number of the wireless signal transmitters is one or two, and the number of the wireless receiving ends is one or more. Preferably, the number of wireless signal transmitters and wireless receiving ends is 2 or 3 respectively.
  • the first wireless receiving end receives the CSI from the first wireless signal transmitter (the abbreviation of Channel State Information, that is, channel state information, in the field of wireless communication, CSI is the channel attribute of the communication link, and describes the signal in each transmission path.
  • the weakening factor the second wireless receiving end receives the CSI from the second wireless signal transmitter. In the environment being tested, the subject does not need to carry other additional equipment.
  • the system will use the CSI received by the two wireless receiving ends to detect the motion of the detected object, and thereby determine the motion or position of the detected object, such as whether it falls or the like.
  • the present invention uses the channel state information CSI of the wireless network as an indicator.
  • CSI can describe the propagation path of a signal under the combined influence of time delay, amplitude attenuation, and phase shift.
  • the present invention establishes a link between CSI and human body travel distance.
  • a particular indoor environment such as a room
  • the present invention uses Orthogonal Frequency Division Multiplex (OFDM) to obtain CSI in the form of a subcarrier.
  • OFDM Orthogonal Frequency Division Multiplex
  • the method for automatically detecting the indoor positioning of the human body based on the above system includes the following steps:
  • the wireless receiving end receives the wireless signal transmitted by the wireless signal transmitter, and uploads the wireless signal information to the server.
  • the wireless signal transmitter will propagate the wireless network signal
  • the wireless receiving end (such as the mobile terminal) in a specific area collects the CSI as the initial channel state data and uploads it to the server. After the data processing through the server.
  • the server receives the wireless signal information of the wireless receiving end, and evaluates the channel state information, where the evaluating the channel state information includes the following steps:
  • S21 Collect channel state data, where the channel state data includes CSI values of M subcarriers in N spatial streams, where N and M are both natural numbers greater than one;
  • S22 For each spatial stream, obtain an average value of CSI values of P consecutive subcarriers at the same time point, and use the average value as channel state information, and P is a natural number greater than 1 and less than M;
  • the present invention uses a 3 ⁇ 3 Multiple-Input Multiple-Out-put (MIMO) as an example.
  • MIMO Multiple-Input Multiple-Out-put
  • the initial channel state data obtained during the sensing phase is divided into 9 spatial streams, and each There will be 30 subcarriers in a stream. Changes in the distance traveled by the human body will affect the data contained in different spatial streams, and will have a similar effect on all subcarriers in each spatial stream.
  • environmental factors such as temperature, room settings
  • the CSI values of 30 subcarriers in each independent spatial stream are combined and combined into a single channel state information.
  • the present invention utilizes a data filtering technique and a moving average method, specifically, using a weighted moving average to smooth the channel state information processed by the above to reduce noise in the data. .
  • the server detects the positioning information of the human body according to the change of the channel state information.
  • the processing method for detecting human body positioning information includes the following steps:
  • S32 receiving wireless signal transmitter coordinates in the network layer and channel state information CSI from the physical layer;
  • step S3 further includes step S34: adaptively modifying parameters in the positioning algorithm in step S33 by using a fingerprint card algorithm. In order to achieve precise positioning with a small amount of training samples.
  • the present invention develops an indoor propagation model based on CSI information and distance.
  • the first step is to collect multiple grouped CSI data at two points to train the environmental factor and the path loss fading index of the indoor propagation model; the second step: test the efficiency of the parameter estimation using the CSI collected at the third point.
  • an indoor propagation model based on CSI information and distance is established, and the distance between the unknown point and the known point can be calculated by the CSI information.
  • the unknown point must be located on a sphere with a known radius as the center of the sphere.
  • the unknown point is at the intersection of the three sphere circumferences.
  • a point on the back side of the reception can be discarded, so that the position of the unknown point can be accurately measured.
  • the present invention uses a fingerprint method to correct the positioning algorithm.
  • Beacon information from a wireless signal transmitter is received at each sample point location, the beacon information including channel response information for a plurality of subcarriers.
  • the mobile receiver receives 30 sets of CSI information at the same time.
  • step S3 of this example further includes step S35: establishing a database to map the located location samples to the radio map.
  • step S35 this example uses statistical machine learning to create a decision region for the channel response mode and cross-correlate the received wireless signals for the mode of position recognition.
  • the algorithm is located using a maximum likelihood estimate with some probability distribution assumptions.
  • the eigenvector follows a multidimensional Gaussian distribution characterized by two statistical parameters (ie, mean vector and covariance matrix).
  • the decision region can be defined by the Mahalanobis distance.
  • the machine learning phase is equivalent to calculating the average vector and covariance matrix of the sample data during statistical machine learning.
  • the method for implementing the statistical machine learning to identify the location is as follows:
  • the cross-correlation of the channel response information of the received wireless signal at the sampling point is calculated.
  • the relevant statistical properties can be described by the statistical properties of the autocorrelation function of the transmitted signal and the cross-correlation function of the channel response.
  • the statistical properties of the autocorrelation function of the transmitted signal can be considered to be constant as it is determined by the modulation and transmission filters of the signal.
  • the cross-correlation function of the channel response depends on the changing environment and the location of the transmitter. Therefore, the cross-correlation fluctuations are mainly affected by the cross-correlation of the channel response.
  • This example uses statistical machine learning, where fluctuations are used as statistical properties of the channel response. This means that the proposed method needs to statistically implement cross-correlation of received signals with overall statistical information about the channel response at each grid point, as follows:
  • the eigenvector is actually assumed by the central limit theorem
  • a multidimensional Gaussian distribution characterized by two statistical parameters (ie, mean vector and covariance matrix) is followed.
  • the decision region can be defined by the Mahalanobis distance.
  • the machine learning phase is equivalent to calculating the average vector and covariance matrix of the sample data during statistical machine learning.
  • This example assumes a eigenvector
  • the multidimensional Gaussian distribution is followed, and the Mahalanobis distance can be calculated by using the maximum likelihood estimation method.
  • the learning data ie the mean vector and the covariance matrix, are defined as follows:
  • H is the conjugate transpose of the matrix (also known as Hermitian transposition), for Average vector, To of The covariance matrix defines the probability function forming decision area as:
  • p(xxx) is the intrinsic format of a probability function.
  • p refers to the probability of the probability function of the decision region.
  • ⁇ ) means the probability of seeking under the condition of ⁇ .
  • the average vector And covariance matrix Both are functions of the antennas k and l and the position information ⁇ .
  • an evaluation function corresponding to the decision area can be defined.
  • the most suspicious location can be estimated as the location with the highest likelihood, ie, the best match between the radio signals in the radio map in the previous training model to infer its location.
  • the present invention further includes a parameter correction step A: correcting the parameters used in the algorithm detected in step S3 by using a sensor attached to the human body.
  • the sensor of this example is preferably a sensor that is provided by the mobile phone, such as a gravity sensor, an acceleration sensor, a gyroscope, and the like that are provided by the mobile phone.
  • the parameter correction method includes:
  • A1 setting the start and end of the human body motion, recording the motion characteristics with the sensor, and recording the time period;
  • A2 The sensor calculates the number of steps of human motion during this time period
  • A3 The speed of the human body is calculated by the time period and the number of steps, and the parameters of the detection algorithm are corrected.
  • the shaking mobile phone action is used as a flag for setting the start and end of the movement, and the time period is recorded at the start and end time points, and the distance moved by the time period is calculated by using the mobile phone's own sensor.
  • the distance and angle information collected are used to calculate the position after the movement, and the positioning algorithm is corrected to make the positioning more accurate.
  • the invention further comprises a step S4: the server returns the calculated positioning result to the wireless receiving end. After the server calculates the positioning result, the positioning result is returned to the wireless receiving end, such as a wireless network card, and other required terminals can obtain positioning information from the wireless network card.
  • the wireless receiving end such as a wireless network card, and other required terminals can obtain positioning information from the wireless network card.
  • the invention is based on the radio propagation mechanism in the indoor environment, establishes the channel state information CSI and the human body moving distance, and judges the distance of the human body movement through the change of the CSI, thereby effectively calculating the position of the human body indoor, which has the following beneficial effects: In indoor environments with less artifacts (such as laboratories), the detection accuracy of detected actions is 84% to 94%, while in indoor environments with more decorations (such as dormitory), the detection accuracy can reach 78%. . It can accurately locate the human body indoors, and use the self-learning function of the system to deal with false alarms and further reduce the false alarm rate.
  • the present invention performs indoor testing on the basis of existing wireless networks and devices.
  • testee does not need to carry any additional sensing equipment, and the testee is not required to carry the detection equipment. The inconvenience caused by it has facilitated its life.

Landscapes

  • Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Position Fixing By Use Of Radio Waves (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

L'invention concerne un procédé et un système de détection automatique de positionnement de corps humain à l'intérieur. Le procédé comprend les étapes suivantes : (S1) une extrémité de réception sans fil reçoit un signal sans fil émis par un émetteur de signal sans fil et téléverse des informations de signal sans fil vers un serveur ; (S2) le serveur reçoit les informations de signal sans fil provenant de l'extrémité de réception sans fil et évalue des informations d'état de canal ; (S3) le serveur détecte des informations de positionnement de corps humain en fonction d'un changement dans les informations d'état de canal ; et (S4) le serveur renvoie un résultat de positionnement calculé à l'extrémité de réception sans fil. Le procédé comprend également une étape A de correction de paramètre : utilisation d'un capteur intégré pour un corps humain afin de corriger les paramètres utilisés dans un algorithme de détection à l'étape S3. Le procédé et le système peuvent réaliser un positionnement précis sur un corps humain à l'intérieur et peuvent utiliser une fonction d'auto-apprentissage d'un système pour traiter une situation d'alarme intempestive, ce qui permet de réduire davantage le taux d'alarmes intempestives, et ils peuvent être utilisés dans n'importe quel environnement où se trouve un réseau sans fil, et une personne détectée n'a pas besoin de transporter un quelconque dispositif de détection supplémentaire, ce qui est très pratique.
PCT/CN2017/084427 2017-01-18 2017-05-16 Procédé et système de détection automatique de positionnement de corps humain à l'intérieur Ceased WO2018133264A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201710035562.3A CN106802404B (zh) 2017-01-18 2017-01-18 一种人体室内定位自动检测方法及系统
CN201710035562.3 2017-01-18

Publications (1)

Publication Number Publication Date
WO2018133264A1 true WO2018133264A1 (fr) 2018-07-26

Family

ID=58984472

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2017/084427 Ceased WO2018133264A1 (fr) 2017-01-18 2017-05-16 Procédé et système de détection automatique de positionnement de corps humain à l'intérieur

Country Status (2)

Country Link
CN (1) CN106802404B (fr)
WO (1) WO2018133264A1 (fr)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110082716A (zh) * 2019-04-29 2019-08-02 徐州医科大学 一种医院复杂环境室内定位系统及定位方法
CN114285500A (zh) * 2021-12-14 2022-04-05 电子科技大学 一种uwb室内定位信道质量评估方法

Families Citing this family (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109495193A (zh) * 2017-09-12 2019-03-19 成都华为技术有限公司 信号传输方法、相关装置及系统
CN107909771B (zh) * 2017-10-08 2020-04-10 南京邮电大学 一种基于无线传感网络的人员跌倒报警系统及其实现方法
CN107816985B (zh) * 2017-10-31 2021-03-05 南京阿凡达机器人科技有限公司 人体检测装置及方法
CN108534779B (zh) * 2018-03-09 2020-09-08 华中科技大学 一种基于轨迹纠正和指纹改进的室内定位地图构建方法
CN110609254B (zh) * 2018-06-15 2022-09-27 富士通株式会社 基于无线信号的动作检测方法、检测装置和电子设备
WO2020145949A1 (fr) 2019-01-08 2020-07-16 Google Llc Détection de déplacement à l'aide de réseaux locaux sans fil
JP7183829B2 (ja) * 2019-01-31 2022-12-06 株式会社Soken 車両用位置推定システム
CN110502105B (zh) * 2019-07-08 2020-12-11 南京航空航天大学 一种基于csi相位差的手势识别系统及识别方法
DE102019220630A1 (de) 2019-12-30 2021-07-01 Airbus Defence and Space GmbH System und Verfahren zur Objektlokalisierung in einer Innenumgebung
CN111398893B (zh) * 2020-05-14 2021-11-23 南京工程学院 一种基于无线定位的网格人体模型测量装置及方法
TWI741821B (zh) * 2020-10-07 2021-10-01 廣達電腦股份有限公司 具有重力感測器輔助定位的電子裝置
CN113422660B (zh) * 2021-05-14 2022-07-19 山东科技大学 基于无线信号的步数检测方法
CN113692022B (zh) * 2021-07-09 2024-06-11 深圳市赛云数据有限公司 一种基于空信道预判利用的运营商通讯系统

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103606248A (zh) * 2013-09-30 2014-02-26 广州市香港科大霍英东研究院 一种人体摔倒自动检测方法及系统
CN104333903A (zh) * 2014-09-17 2015-02-04 北京邮电大学 基于rssi和惯性测量的室内多目标的定位系统和方法
CN104812061A (zh) * 2015-03-24 2015-07-29 成都希盟泰克科技发展有限公司 一种基于mimo-ofdm信道状态信息的室内测距及定位方法
WO2015119635A1 (fr) * 2014-02-10 2015-08-13 Hewlett Packard Development Company, L.P. Estimation de distance
CN105979485A (zh) * 2016-05-11 2016-09-28 南京邮电大学 基于信道状态信息的室内环境下人员检测方法
CN106332277A (zh) * 2016-09-05 2017-01-11 中南大学 一种基于信道状态信息分布的室内定位方法

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103596266B (zh) * 2013-11-26 2017-06-27 无锡市中安捷联科技有限公司 一种人体检测和定位的方法、装置及系统
CN104267439A (zh) * 2014-08-20 2015-01-07 哈尔滨工程大学 一种无监督人体检测与定位的方法
CN106154222B (zh) * 2016-06-20 2018-06-12 北京大学 一种利用无线电射频信号检测人的行走方向的方法

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103606248A (zh) * 2013-09-30 2014-02-26 广州市香港科大霍英东研究院 一种人体摔倒自动检测方法及系统
WO2015119635A1 (fr) * 2014-02-10 2015-08-13 Hewlett Packard Development Company, L.P. Estimation de distance
CN104333903A (zh) * 2014-09-17 2015-02-04 北京邮电大学 基于rssi和惯性测量的室内多目标的定位系统和方法
CN104812061A (zh) * 2015-03-24 2015-07-29 成都希盟泰克科技发展有限公司 一种基于mimo-ofdm信道状态信息的室内测距及定位方法
CN105979485A (zh) * 2016-05-11 2016-09-28 南京邮电大学 基于信道状态信息的室内环境下人员检测方法
CN106332277A (zh) * 2016-09-05 2017-01-11 中南大学 一种基于信道状态信息分布的室内定位方法

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110082716A (zh) * 2019-04-29 2019-08-02 徐州医科大学 一种医院复杂环境室内定位系统及定位方法
CN114285500A (zh) * 2021-12-14 2022-04-05 电子科技大学 一种uwb室内定位信道质量评估方法
CN114285500B (zh) * 2021-12-14 2023-03-24 电子科技大学 一种uwb室内定位信道质量评估方法

Also Published As

Publication number Publication date
CN106802404B (zh) 2018-10-16
CN106802404A (zh) 2017-06-06

Similar Documents

Publication Publication Date Title
WO2018133264A1 (fr) Procédé et système de détection automatique de positionnement de corps humain à l'intérieur
CN103596266B (zh) 一种人体检测和定位的方法、装置及系统
CN104502982B (zh) 一种细粒度免校验的室内被动人体检测方法
Choi et al. Deep learning based NLOS identification with commodity WLAN devices
CN108387940B (zh) 一种基于Wi-Fi无线感知的室内移动物体检测方法
CN110475221B (zh) 一种基于信道状态信息的人员动作识别和位置估计方法
CN103606248B (zh) 一种人体摔倒自动检测方法及系统
CN104883732B (zh) 一种增强型室内无源被动人体定位方法
CN107968689A (zh) 基于无线通信信号的感知识别方法及装置
WO2016197648A1 (fr) Procédé de détection et de reconnaissance d'action basé sur un signal sans fil
CN108038419B (zh) 基于Wi-Fi的室内人员被动检测方法
CN112333653A (zh) 基于WiFi信道状态信息的身份智能识别方法、系统
CN104267439A (zh) 一种无监督人体检测与定位的方法
Liu et al. A research on CSI-based human motion detection in complex scenarios
Shi et al. Human activity recognition using deep learning networks with enhanced channel state information
CN106411433A (zh) 一种基于wlan的细粒度室内被动入侵检测方法
Xun et al. Depthwise separable convolution based passive indoor localization using CSI fingerprint
CN114222202A (zh) 一种基于WiFi CSI的环境自适应活动检测方法及系统
CN111770527A (zh) 基于二维特征的可视与非可视信道识别方法及装置
CN108122310A (zh) 一种基于WiFi信道状态信息和动态时间规整的人流量统计方法
Tang et al. Human behavior recognition based on WiFi channel state information
Choi et al. Simultaneous crowd estimation in counting and localization using WiFi CSI
Man et al. PWiG: A phase-based wireless gesture recognition system
CN105785454A (zh) 基于信道频域响应的室内运动检测方法
CN113783639B (zh) 无接触感知边界确定方法、系统、计算机设备及存储介质

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 17892094

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

32PN Ep: public notification in the ep bulletin as address of the adressee cannot be established

Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC (EPO FORM 1205N DATED 30/08/2019)

122 Ep: pct application non-entry in european phase

Ref document number: 17892094

Country of ref document: EP

Kind code of ref document: A1