CN108416974B - Automatic alarm device and method based on wireless channel state information - Google Patents
Automatic alarm device and method based on wireless channel state information Download PDFInfo
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- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
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- G08B21/02—Alarms for ensuring the safety of persons
- G08B21/0297—Robbery alarms, e.g. hold-up alarms, bag snatching alarms
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Abstract
The invention discloses an automatic alarm device and method based on wireless channel state information, the wireless channel state information is the channel state information of communication sub-carrier between the transmitting terminal and the receiving terminal in the wireless communication network, the automatic alarm device includes: the alarm module is used for acquiring the channel state information, identifying the human gestures or human actions in the coverage area of the wireless communication network according to the channel state information and generating alarm information according to the identification result; and the communication module is used for receiving the alarm information and giving an alarm according to the alarm information. The automatic alarm device can conveniently and effectively realize automatic alarm and has lower cost.
Description
Technical Field
The invention relates to the technical field of wireless communication, in particular to an automatic alarm device based on wireless channel state information and an automatic alarm method based on the wireless channel state information.
Background
The home security is always a concern of people, along with the development of science and technology, the requirements of people on security are higher and higher, and the main alarm systems are all key-type active alarm and sensor-based passive alarm. There is a limitation to active alarm by push-button. When monitoring large-scale region, passive warning based on the sensor, need arrange certain quantity of sensor in the monitoring area, and along with the increase of the quantity of sensor, can increase the expenditure of cost, passive alarm system based on the sensor also needs the people to dial through communication equipment through the button and reports to the police moreover, under the circumstances that the personal freedom is restricted, can have certain limitation.
Disclosure of Invention
The invention aims to solve the technical problems of high cost, inconvenient alarming and the like of an alarming system at least to a certain extent, and therefore, the invention aims to provide an automatic alarming device based on wireless channel state information, which can conveniently and effectively realize automatic alarming and has lower cost.
The second purpose of the invention is to provide an automatic alarm method based on wireless channel state information.
In order to achieve the above object, an automatic alarm device based on wireless channel state information according to an embodiment of a first aspect of the present invention, where the wireless channel state information is channel state information of communication subcarriers between a transmitting terminal and a receiving terminal in a wireless communication network, includes: the alarm module is used for acquiring the channel state information, identifying the human gestures or human actions in the coverage area of the wireless communication network according to the channel state information, and generating alarm information according to the identification result; and the communication module is used for receiving the alarm information and giving an alarm according to the alarm information.
According to the automatic alarm device based on the wireless channel state information, the alarm module can acquire the channel state information, identify the gesture or the action of a person in the coverage area of the wireless communication network according to the channel state information, generate alarm information according to the identification result, and alarm according to the alarm information, so that the communication module can alarm according to the alarm information by using the channel state information as the physical quantity of action identification, and the automatic alarm device has the advantages of stability, reliability, high precision, no identification blind area and the like.
In addition, the automatic alarm device based on wireless channel state information according to the above embodiment of the present invention may further have the following additional technical features:
further, the alarm module includes: the data processing unit is used for carrying out data preprocessing on the channel state information; the continuous action identification unit is used for carrying out continuous action identification on the channel state information data obtained after data preprocessing by using a continuous action identification algorithm so as to obtain an effective action data section; an action division unit, which is used for dividing the effective action data segment to obtain a plurality of single action data; and the recognition unit is used for recognizing the human gesture or the human action according to the individual action data and a deep learning algorithm.
Specifically, the channel state information is obtained by sampling the channel state information by the receiving terminal at a preset sampling frequency, the channel state information data obtained by each sampling is represented as a matrix, and the data processing unit is configured to: carrying out noise reduction processing on the channel state information data through a Hampel filter and a Butterworth filter; and reconstructing the channel state information data subjected to noise reduction processing by a weighted moving average method.
Specifically, the continuous motion recognition unit is configured to: intercepting channel state information data obtained after data preprocessing through a sliding window with a preset window size by a preset step length to obtain a plurality of partition matrixes; multiplying each partition matrix by the transpose of the partition matrix to obtain a corresponding correlation matrix; calculating the eigenvalue and the eigenvector of each correlation matrix, and judging the change conditions of the eigenvalue and the eigenvector of the multiple correlation matrices; and determining whether a specific action or a large-amplitude action exists according to the change condition so as to determine the effective action data segment.
Specifically, the action division unit is configured to: estimating the starting point and the end point of each action in the effective action data segment to obtain an estimated set:
wherein，ti s、ti eRespectively estimated starting point and ending point of the ith action, and ti s、ti eForming a data pair; setting a safety interval parameter TbAnd expanding each data pair in the pre-estimated set through the safety interval parameters to obtain a new set:
to derive a plurality of individual action data from the new set.
Specifically, the identification unit stores a sample data set, wherein sample data representing gestures and motions of people are learned in a deep learning algorithm in advance, and corresponding sample feature vectors are extracted to establish the sample data set according to the sample feature vectors, and the identification unit is configured to: training each individual action data by utilizing a deep learning algorithm to obtain a feature vector of a corresponding action to be recognized; comparing the feature vector of the action to be recognized with the sample feature vectors in the sample data set to recognize the human gesture or the human action.
Further, the alarm module further comprises: the active alarm unit is used for identifying an alarm number according to the personnel gesture when the identification unit identifies the personnel gesture, and sending the alarm number to the communication module; and the passive alarm unit is used for further judging the type of the personnel action when the identification unit identifies the personnel action, generating corresponding alarm information when a danger is judged according to the type of the personnel action, and sending the alarm information to the communication module.
Further, the communication module performs dialing alarm when receiving the alarm number, and performs alarm to a corresponding alarm receiving mechanism when receiving the alarm information.
In order to achieve the above object, a second embodiment of the present invention provides an automatic alarm method based on wireless channel state information, where the wireless channel state information is channel state information of communication subcarriers between a transmitting terminal and a receiving terminal in a wireless communication network, and the automatic alarm method includes: acquiring the channel state information; identifying the personnel gestures or personnel actions in the coverage area of the wireless communication network according to the channel state information, and generating alarm information according to an identification result; and alarming according to the alarm information.
According to the automatic alarm method based on the wireless channel state information, the channel state information can be obtained, the gestures or actions of the personnel in the coverage area of the wireless communication network can be identified according to the channel state information, the alarm information is generated according to the identification result, and the alarm can be performed according to the alarm information, so that the method has the advantages of stability, reliability, high precision, no identification blind area and the like by using the channel state information as the physical quantity of action identification, reasonably utilizes the conventional wireless communication equipment, has low cost and easy popularization, does not need any active equipment carried by a human body, further reduces the cost, and can conveniently and effectively realize automatic alarm.
In addition, the automatic alarm method based on the wireless channel state information proposed according to the above embodiment of the present invention may further have the following additional technical features:
further, recognizing the human gesture or human action in the coverage area of the wireless communication network according to the channel state information specifically includes: performing data preprocessing on the channel state information; carrying out continuous action recognition on the channel state information data obtained after data preprocessing by using a continuous action recognition algorithm to obtain an effective action data section; dividing the effective action data segment to obtain a plurality of single action data; identifying the person gesture or the person action according to the individual action data and a deep learning algorithm.
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Fig. 1 is a schematic diagram of a wireless communication network according to an embodiment of the present invention;
FIG. 2 is a block diagram of an automatic alarm device based on wireless channel state information according to one embodiment of the present invention;
fig. 3 is a schematic structural diagram of an automatic alarm device based on wireless channel state information according to an embodiment of the present invention;
fig. 4 is a flowchart of an automatic alarm method based on wireless channel state information according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
The automatic alarm device and method based on wireless channel state information according to the embodiments of the present invention are described below with reference to the accompanying drawings.
The wireless channel state information of the embodiment of the invention is the channel state information of communication subcarriers between a transmitting terminal and a receiving terminal in a wireless communication network. The Wireless communication network may be a WIFI (Wireless Fidelity, Wireless local area network), the transmitting terminal may include a router, and the receiving terminal may include a terminal device, such as a mobile phone, a tablet computer, a notebook computer, etc., configured with a Wireless network card and connected to the router.
As shown in fig. 1, a channel of a transmitting terminal and a receiving terminal is composed of a plurality of subcarriers, and when a human body is in a signal transmission space and acts, propagation of a signal may be affected. In the ofdm system according to an embodiment of the present invention, the channel of each pair of the transmitting terminal and the receiving terminal is composed of 30 subcarriers, and when a human body performs some motion near the receiving terminal, a change in the phase value or amplitude value of the CSI on the 30 subcarriers may be caused, and the change may have a significant effect on the weak motion. Therefore, in the embodiment of the present invention, the action recognition can be performed according to the channel state information of the subcarriers.
In an embodiment of the present invention, the CSI may be obtained by sampling at a predetermined sampling frequency by the receiving terminal, and the CSI data obtained by each sampling may be represented as a matrix. Specifically, the receiving terminal may sample the channel state information data at a sampling frequency of 2000 packets/second, with each sampling resulting in a matrix
The channel state information data is stored by an embedded orthogonal frequency division multiplexing system, and then action recognition, alarm and the like are carried out on the basis of the channel state information data.
Fig. 2 is a block diagram illustrating an automatic alarm device based on wireless channel state information according to an embodiment of the present invention.
As shown in fig. 2, the automatic alarm device based on wireless channel status information according to the embodiment of the present invention includes an alarm module 10 and a communication module 20.
The alarm module 10 is configured to acquire channel state information, identify a human gesture or a human action in a coverage area of the wireless communication network according to the channel state information, and generate alarm information according to an identification result; the communication module 20 is configured to receive the alarm information and perform an alarm according to the alarm information.
The alarm module 10 may be disposed in the receiving terminal, or may be disposed outside the receiving terminal, and when the alarm module 10 is disposed outside the receiving terminal, the alarm module 10 may perform wireless communication with the receiving terminal to receive the channel state information acquired by the receiving terminal.
Further, as shown in fig. 2, the alarm module 10 may include a data processing unit 11, a continuous motion recognition unit 12, a motion segmentation unit 13, and a recognition unit 14. The data processing unit 11 is configured to perform data preprocessing on the channel state information; the continuous action recognition unit 12 is configured to perform continuous action recognition on the channel state information data obtained after the data preprocessing by using a continuous action recognition algorithm to obtain an effective action data segment; the action division unit 13 is used for dividing the effective action data segment to obtain a plurality of single action data; the recognition unit 14 is used to recognize human gestures or human actions from individual action data and deep learning algorithms.
In an embodiment of the present invention, the data processing unit 11 is specifically configured to perform noise reduction processing on the channel state information data through a Hampel filter and a Butterworth filter, and reconstruct the channel state information data after the noise reduction processing through a weighted moving average method, because of the influence of factors such as the change of transmission power and the adaptive selection of transmission rate during the conversion process of the signal, it is inevitable that some abrupt changes exist in the sampled channel state information data, according to the principle of the Hampel filter, μ is defined as the median of the sampled data, and σ is defined as the absolute deviation of the median of the sampled data, and [ μ - γ × σ, μ + γ × σ is defined]The abnormal value is the normal range of the sampled channel state information data, and the abnormal value is outside the range. In addition, because the action frequency of the person is usually very low, a point with very high frequency in the channel state information data is also an exception, and a cut-off frequency can be set according to the principle of a Butterworth filter to further process the data and eliminate the exception data with very high frequency. With the above two data processing methods, although most abnormal data is eliminated, noise still exists in the data to affect the subsequent processing of the data, so that the principle of weighted moving average method can be used, and the method can be used for processing data by using the principle of weighted moving average method
Channel state information data is reconstructed, thereby making the channel state information data change more smoothly.
In an embodiment of the present invention, the continuous motion identification unit 12 is specifically configured to intercept channel state information data obtained after data preprocessing is performed through a sliding window with a preset window size by a preset step size to obtain a plurality of partition matrices, multiply each partition matrix by its own transpose to obtain a corresponding correlation matrix, calculate a feature value and a feature vector of each correlation matrix, determine a change condition of the feature value and the feature vector of the plurality of correlation matrices, and determine whether there is a specific motion or a large-scale motion according to the change condition to determine an effective motion data segment. The preset window size may be 500 packets, and the preset step size may be 400 packets.
In an embodiment of the present invention, the action segmentation unit 13 is specifically configured to predict a starting point and an ending point of each action in the valid action data segment, so as to obtain a prediction set:
wherein, ti s、ti eRespectively estimated starting point and ending point of the ith action, and ti s、ti eA data pair is formed. Setting the safety interval parameter T simultaneouslybAnd expanding each data pair in the pre-estimated set through the safety interval parameters to obtain a new set:
i.e. determining the starting and ending points of each action, so that a plurality of individual action data is available from the new set.
In an embodiment of the present invention, the recognition unit 14 stores a sample data set, wherein the sample data representing each human gesture and human motion may be learned in a deep learning algorithm in advance, and a corresponding sample feature vector is extracted to establish the sample data set according to the sample feature vector. The recognition unit 14 is specifically configured to train each individual motion data by using a deep learning algorithm to obtain a feature vector of a corresponding motion to be recognized, and compare the feature vector of the motion to be recognized with a sample feature vector in the sample data set to recognize a human gesture or a human motion.
In one embodiment of the present invention, as shown in FIG. 2, the alarm module 10 may further include an active alarm unit 15 and a passive alarm unit 16.
The active alarm unit 15 is configured to identify an alarm number according to the gesture of the person when the identification unit identifies the gesture of the person, and send the alarm number to the communication module 20. The communication module 20 may dial an alarm when receiving the alarm number.
The passive alarm unit 16 is configured to further determine the type of the human action when the identification unit identifies the human action, generate corresponding alarm information when a danger is determined according to the type of the human action, and send the alarm information to the communication module 20. The communication module 20 can alarm the corresponding alarm receiving mechanism when receiving the alarm information.
In one embodiment of the invention, the automatic alarm device may be used in a home or public place. A transmitting terminal and a receiving terminal can be respectively placed at two positions on the same horizontal line, as shown in fig. 3, the transmitting terminal can be a common wireless router, and the receiving terminal can be composed of an intelligent device and a receiving antenna, wherein the intelligent device is provided with an Intel 5300 wireless network card.
As shown in fig. 3, when a person in a communication coverage area continuously and repeatedly makes a prescribed digital gesture language, the intelligent device may recognize a continuous digital string according to the channel state information, analyze an operator field, a website field, and a serial number field of a number, thereby recognizing an alarm number, and then switch on the communication module to realize an active alarm of gesture dialing.
When the intelligent device monitors that large-amplitude physical attack actions such as beating, falling, violent running and the like exist in the communication coverage area, case situations can be judged to occur, and at the moment, the communication module can report the case situations to a local public security organization through the Internet, so that passive alarm is realized. The intelligent device can also determine the property of the case according to the identified physical attack action, such as fire, emergency, indoor robbery and the like, and the communication module can give an alarm to corresponding responsible persons and responsible departments according to the property of the case.
It should be noted that the receiving terminal and the automatic alarm device may be different or the same intelligent terminal equipment. For the same situation of the intelligent terminal device, for example, when the receiving terminal is a mobile phone, the alarm module 10 may be disposed in the mobile phone, and the communication module 20 may also be disposed in the mobile phone, that is, the mobile phone can recognize the gesture or the action of the person in the coverage area of the wireless communication network according to the channel state information, and generate alarm information according to the recognition result, and can also alarm according to the alarm information.
According to the automatic alarm device based on the wireless channel state information, the alarm module can acquire the channel state information, identify the gesture or the action of a person in the coverage area of the wireless communication network according to the channel state information, generate alarm information according to the identification result, and alarm according to the alarm information, so that the communication module can alarm according to the alarm information by using the channel state information as the physical quantity of action identification, and the automatic alarm device has the advantages of stability, reliability, high precision, no identification blind area and the like.
Corresponding to the embodiment, the invention also provides an automatic alarm method based on the wireless channel state information.
As shown in fig. 4, the automatic alarm method based on wireless channel status information according to the embodiment of the present invention includes the following steps:
s1, acquiring the channel state information.
In an embodiment of the present invention, the CSI may be obtained by sampling at a predetermined sampling frequency by the receiving terminal, and the CSI data obtained by each sampling may be represented as a matrix. Specifically, the receiving terminal may sample the channel state information data at a sampling frequency of 2000 packets/second, with each sampling resulting in a matrix
The channel state information data is stored by an embedded orthogonal frequency division multiplexing system, and then action recognition, alarm and the like are carried out on the basis of the channel state information data.
And S2, recognizing the human gestures or human actions in the coverage area of the wireless communication network according to the channel state information, and generating alarm information according to the recognition result.
Specifically, the channel state information may be subjected to data preprocessing, continuous motion recognition may be performed on the channel state information data obtained after the data preprocessing by using a continuous motion recognition algorithm to obtain an effective motion data segment, the effective motion data segment may be divided to obtain a plurality of individual motion data, and a human gesture or a human motion may be recognized according to the individual motion data and a deep learning algorithm.
In one embodiment of the invention, channel state information data can be subjected to noise reduction processing through a Hampel filter and a Butterworth filter, and the channel state information data subjected to the noise reduction processing is reconstructed through a weighted moving average method, because of the influence of factors such as the change of transmission power of signals in the conversion process, the adaptive selection of transmission rate and the like, values with sudden changes in the sampled channel state information data are inevitably caused, mu is defined as the median of the sampled data, and sigma is defined as the absolute deviation of the median of the sampled data, and [ mu-gamma × sigma, mu + gamma × sigma is defined according to the principle of the Hampel filter]The abnormal value is the normal range of the sampled channel state information data, and the abnormal value is outside the range. In addition, because the action frequency of the person is usually very low, a point with very high frequency in the channel state information data is also an exception, and a cut-off frequency can be set according to the principle of a Butterworth filter to further process the data and eliminate the exception data with very high frequency. With the above two data processing methods, although most abnormal data is eliminated, noise still exists in the data to affect the subsequent processing of the data, so that the principle of weighted moving average method can be used, and the method can be used for processing data by using the principle of weighted moving average method
Channel state information data is reconstructed, thereby making the channel state information data change more smoothly.
In an embodiment of the present invention, the continuous motion identification unit 12 is specifically configured to intercept channel state information data obtained after data preprocessing is performed through a sliding window with a preset window size by a preset step size to obtain a plurality of partition matrices, multiply each partition matrix by its own transpose to obtain a corresponding correlation matrix, calculate a feature value and a feature vector of each correlation matrix, determine a change condition of the feature value and the feature vector of the plurality of correlation matrices, and determine whether there is a specific motion or a large-scale motion according to the change condition to determine an effective motion data segment. The preset window size may be 500 packets, and the preset step size may be 400 packets.
In an embodiment of the present invention, the start point and the end point of each motion in the valid motion data segment can be estimated, and an estimation set is obtained:
wherein, ti s、ti eRespectively estimated starting point and ending point of the ith action, and ti s、ti eA data pair is formed. Setting the safety interval parameter T simultaneouslybAnd expanding each data pair in the pre-estimated set through the safety interval parameters to obtain a new set:
i.e. determining the starting and ending points of each action, so that a plurality of individual action data is available from the new set.
In an embodiment of the present invention, sample data representing gestures and actions of each person may be learned in a deep learning algorithm in advance, and corresponding sample feature vectors are extracted, so as to establish a sample data set according to the sample feature vectors and store the sample data set. And training each individual action data by utilizing a deep learning algorithm during identification to obtain a feature vector of a corresponding action to be identified, and comparing the feature vector of the action to be identified with the sample feature vector in the sample data set to identify the gesture or the action of the person.
And S3, alarming according to the alarm information.
In one embodiment of the invention, active alarming can be realized by recognizing the gesture of a person, and passive alarming can also be realized by recognizing the action of the person. Specifically, when the recognition unit recognizes the gesture of the person, the alarm number can be recognized according to the gesture of the person, and then dialing alarm can be performed according to the alarm number. When the identification unit identifies the action of the personnel, the type of the action of the personnel is further judged, and when the danger is judged according to the type of the action of the personnel, the alarm is given to the corresponding alarm receiving mechanism.
According to the automatic alarm method based on the wireless channel state information, the channel state information can be obtained, the gestures or actions of the personnel in the coverage area of the wireless communication network can be identified according to the channel state information, the alarm information is generated according to the identification result, and the alarm can be performed according to the alarm information, so that the method has the advantages of stability, reliability, high precision, no identification blind area and the like by using the channel state information as the physical quantity of action identification, reasonably utilizes the conventional wireless communication equipment, has low cost and easy popularization, does not need any active equipment carried by a human body, further reduces the cost, and can conveniently and effectively realize automatic alarm.
In the description of the present invention, it is to be understood that the terms "central," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," "circumferential," and the like are used in the orientations and positional relationships indicated in the drawings for convenience in describing the invention and to simplify the description, and are not intended to indicate or imply that the referenced device or element must have a particular orientation, be constructed and operated in a particular orientation, and are not to be considered limiting of the invention.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.
In the present invention, unless otherwise expressly stated or limited, the terms "mounted," "connected," "secured," and the like are to be construed broadly and can, for example, be fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; either directly or indirectly through intervening media, either internally or in any other relationship. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
In the present invention, unless otherwise expressly stated or limited, the first feature "on" or "under" the second feature may be directly contacting the first and second features or indirectly contacting the first and second features through an intermediate. Also, a first feature "on," "over," and "above" a second feature may be directly or diagonally above the second feature, or may simply indicate that the first feature is at a higher level than the second feature. A first feature being "under," "below," and "beneath" a second feature may be directly under or obliquely under the first feature, or may simply mean that the first feature is at a lesser elevation than the second feature.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (5)
1. An automatic alarm device based on wireless channel state information, wherein the wireless channel state information is channel state information of communication subcarriers between a transmitting terminal and a receiving terminal in a wireless communication network, the automatic alarm device comprising:
the alarm module is used for acquiring the channel state information, identifying the human gestures or human actions in the coverage area of the wireless communication network according to the channel state information, and generating alarm information according to the identification result;
a communication module for receiving the alarm information and giving an alarm according to the alarm information,
the alarm module includes: the data processing unit is used for carrying out data preprocessing on the channel state information; the continuous action identification unit is used for carrying out continuous action identification on the channel state information data obtained after data preprocessing by using a continuous action identification algorithm so as to obtain an effective action data section; an action division unit, which is used for dividing the effective action data segment to obtain a plurality of single action data; a recognition unit for recognizing the person gesture or the person action according to the individual action data and a deep learning algorithm,
the channel state information is obtained by sampling the channel state information by the receiving terminal at a preset sampling frequency, the channel state information data obtained by each sampling is represented as a matrix, and the data processing unit is specifically configured to: carrying out noise reduction processing on the channel state information data through a Hampel filter and a Butterworth filter; reconstructing the channel state information data after the noise reduction processing by a weighted moving average method,
the continuous motion recognition unit is specifically configured to: intercepting channel state information data obtained after data preprocessing through a sliding window with a preset window size by a preset step length to obtain a plurality of partition matrixes; multiplying each partition matrix by the transpose of the partition matrix to obtain a corresponding correlation matrix; calculating the eigenvalue and the eigenvector of each correlation matrix, and judging the change conditions of the eigenvalue and the eigenvector of the multiple correlation matrices; determining whether there is a specific action or a large action according to the change condition to determine the effective action data segment,
the action segmentation unit is specifically configured to: estimating the starting point and the end point of each action in the effective action data segment to obtain an estimated set:
wherein, ti s、ti eRespectively estimated starting point and ending point of the ith action, and ti s、ti eForming a data pair; setting a safety interval parameter TbAnd expanding each data pair in the pre-estimated set through the safety interval parameters to obtain a new set:
to derive a plurality of individual action data from the new set.
2. The wireless channel status information-based automatic alarm device according to claim 1, wherein the identification unit stores a sample data set, wherein sample data representing gestures and actions of people are learned in advance in a deep learning algorithm, and corresponding sample feature vectors are extracted to establish the sample data set according to the sample feature vectors, and the identification unit is specifically configured to:
training each individual action data by utilizing a deep learning algorithm to obtain a feature vector of a corresponding action to be recognized;
comparing the feature vector of the action to be recognized with the sample feature vectors in the sample data set to recognize the human gesture or the human action.
3. The wireless channel state information-based automatic warning device according to claim 1 or 2, wherein the warning module further includes:
the active alarm unit is used for identifying an alarm number according to the personnel gesture when the identification unit identifies the personnel gesture, and sending the alarm number to the communication module;
and the passive alarm unit is used for further judging the type of the personnel action when the identification unit identifies the personnel action, generating corresponding alarm information when a danger is judged according to the type of the personnel action, and sending the alarm information to the communication module.
4. The automatic alarm device based on wireless channel status information as claimed in claim 3, wherein the communication module dials an alarm upon receiving the alarm number and alarms to a corresponding alarm receiving mechanism upon receiving the alarm information.
5. An automatic alarm method based on wireless channel state information, wherein the wireless channel state information is channel state information of communication subcarriers between a transmitting terminal and a receiving terminal in a wireless communication network, the automatic alarm method comprising:
acquiring the channel state information;
identifying the personnel gestures or personnel actions in the coverage area of the wireless communication network according to the channel state information, and generating alarm information according to an identification result;
an alarm is given according to the alarm information,
identifying the human gestures or human actions in the coverage area of the wireless communication network according to the channel state information, which specifically comprises the following steps:
performing data preprocessing on the channel state information;
carrying out continuous action recognition on the channel state information data obtained after data preprocessing by using a continuous action recognition algorithm to obtain an effective action data section;
dividing the effective action data segment to obtain a plurality of single action data;
identifying the person gesture or the person action from the individual action data and a deep learning algorithm,
the channel state information is obtained by sampling the channel state information by the receiving terminal at a preset sampling frequency, channel state information data obtained by each sampling is represented as a matrix, and the data preprocessing of the channel state information specifically comprises the following steps:
carrying out noise reduction processing on the channel state information data through a Hampel filter and a Butterworth filter;
reconstructing the channel state information data after the noise reduction processing by a weighted moving average method,
intercepting channel state information data obtained after data preprocessing through a sliding window with the size of a preset window by a preset step length to obtain a plurality of partition matrixes; multiplying each partition matrix by the transpose of the partition matrix to obtain a corresponding correlation matrix; calculating the eigenvalue and the eigenvector of each correlation matrix, and judging the change conditions of the eigenvalue and the eigenvector of the multiple correlation matrices; determining whether there is a specific action or a large action according to the change condition to determine the effective action data segment,
estimating the starting point and the end point of each action in the effective action data segment to obtain an estimated set:
wherein, ti s、ti eRespectively estimated starting point and ending point of the ith action, and ti s、ti eForming a data pair; setting a safety interval parameter TbAnd expanding each data pair in the pre-estimated set through the safety interval parameters to obtain a new set:
to derive a plurality of individual action data from the new set.
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