CN113238659A - Real-time behavior identification method and system based on WIFI signal - Google Patents
Real-time behavior identification method and system based on WIFI signal Download PDFInfo
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- 230000000694 effects Effects 0.000 claims description 17
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- 238000012216 screening Methods 0.000 claims description 9
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- G06F18/00—Pattern recognition
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Abstract
The invention discloses a real-time behavior identification method and a real-time behavior identification system based on WIFI signals.A user terminal is used for extracting physical layer information of the WIFI signals, and the behaviors of indoor users can be automatically judged according to the change condition of the WIFI signals; the device is convenient to work in a sheltering environment and a night environment, and has the advantages of being free of carrying equipment, capable of being scanned through a wall, capable of guaranteeing personal privacy and the like based on action recognition of wireless signals.
Description
Technical Field
The invention relates to the field of wireless perception, in particular to a real-time behavior identification method and system based on WIFI signals.
Background
Human behavior recognition is one of the important research contents for intelligent application. Human behavior recognition refers to the classification and recognition of human behaviors according to a certain algorithm by measuring certain signal data generated when a human performs various actions. By accurately identifying human behaviors, the human-computer interaction quality can be improved, the intelligent application range is expanded, the future development trend of intelligent life is realized, and the method has huge application prospects and economic values for researching intelligent home, intelligent teaching, medical assistance, keyboard identification and the like.
Although the traditional method based on computer vision can realize better human body action recognition performance, because the indoor environment is complex, the accuracy of the human body action recognition system based on computer vision is obviously influenced by the problems of obstacles and ambient light, and more importantly, the serious privacy leakage risk exists when the human body action is recognized through the common visual information. Therefore, there is a need for a non-invasive and privacy-preserving identification method.
Disclosure of Invention
The invention aims to provide a real-time behavior identification method and system based on a WIFI signal, which are convenient to work in a sheltering environment and a night environment, and can ensure personal privacy by identifying human behaviors based on the WIFI signal.
The purpose of the invention is realized by the following technical scheme:
a WIFI signal based real-time behavior recognition system, comprising: a user terminal; the user terminal includes: the device comprises a graphical interface module, a WIFI signal acquisition module, a signal preprocessing module, a signal analysis module, a feature extraction and representation module and a classification detection module; wherein:
the graphical interface module is used for realizing human-computer interaction so as to control the operation of the user terminal and displaying the prediction result output by the classification detection module;
the WIFI signal acquisition module is used for acquiring WIFI signals and transmitting the WIFI signals to the signal preprocessing module;
the signal preprocessing module is used for preprocessing the acquired WIFI signal, filtering out high-frequency components by using a filtering method, removing direct-current components at the same time, and transmitting all subcarrier data subjected to high-frequency component filtering to the signal analysis module;
the signal analysis module is used for performing time domain analysis and frequency domain analysis on all input subcarrier signals and transmitting the time domain analysis and frequency domain analysis results of the subcarrier signals to the characteristic extraction and representation module;
the characteristic extraction and representation module is used for screening out components which meet the set requirements in relation to human activities from the time domain analysis and frequency domain analysis results output by the signal analysis module, and then processing the components into data which can be processed by the classification detection module;
and the classification detection module is used for predicting the behavior category of the user according to the output of the feature extraction and representation module.
A real-time behavior identification method based on WIFI signals comprises the following steps:
collecting WIFI signals;
preprocessing the acquired WIFI signal, filtering out high-frequency components by using a filtering method, removing direct-current components, and performing signal analysis on all subcarrier data after the high-frequency components are filtered out; wherein the signal analysis comprises: time domain analysis and frequency domain analysis;
screening out components which are related to human activities and meet set requirements from time domain analysis and frequency domain analysis results of subcarrier signals, and processing the components into data which can be processed in a classification detection process;
and predicting the behavior category of the user according to the processed data.
According to the technical scheme provided by the invention, the physical layer information of the WIFI signal is extracted, and the behavior of an indoor user can be automatically judged according to the change condition of the WIFI signal; the device is convenient to work in a sheltering environment and a night environment, and has the advantages of being free of carrying equipment, capable of being scanned through a wall, capable of guaranteeing personal privacy and the like based on action recognition of wireless signals.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
Fig. 1 is a schematic diagram of a real-time behavior recognition system based on WIFI signals according to an embodiment of the present invention;
fig. 2 is a schematic diagram of an application scenario provided in an embodiment of the present invention;
fig. 3 is a flowchart of a real-time behavior recognition method based on WIFI signals according to an embodiment of the present invention;
the reference numbers shown in fig. 1: 1. user terminal, 2, graphical interface module, 3, WIFI signal acquisition module, 4, signal preprocessing module, 5, time domain analysis module, 6, frequency domain analysis module, 7, wavelet analysis module, 8, characteristic extraction module, 9, characteristic representation module, 10, classification module, 11, action detection module.
Detailed Description
The technical solutions in the embodiments of the present invention are clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
An embodiment of the present invention provides a real-time behavior recognition system based on WIFI signals, as shown in fig. 1, which mainly includes: a user terminal; the user terminal includes: the device comprises a graphical interface module, a WIFI signal acquisition module, a signal preprocessing module, a signal analysis module, a feature extraction and representation module and a classification detection module. Specifically, the method comprises the following steps:
1) and the graphical interface module is used for realizing human-computer interaction so as to control the operation of the user terminal and displaying the prediction result output by the classification detection module.
In the embodiment of the invention, the graphical interface module is reserved with the expansion interface which can be used for subsequent development so as to optimize the fluency, the functionality and the user friendliness of the graphical interface.
2) And the WIFI signal acquisition module is used for acquiring WIFI signals and transmitting the WIFI signals to the signal preprocessing module.
In the embodiment of the invention, the WIFI signal acquisition module acquires the WIFI signal by using the wireless network card with the set model and the matched wireless network driving program. The present invention is not limited with respect to the specific model and the specific content of the wireless network driver.
In the embodiment of the invention, the instruction can be issued according to the graphical interface module, and the WIFI signal is transmitted to the signal preprocessing module, so that the whole behavior recognition process is started.
3) The signal preprocessing module is used for preprocessing the acquired WIFI signal, filtering out high-frequency components by using a filtering method, removing direct-current components at the same time, and transmitting all subcarrier data subjected to high-frequency component filtering to the signal analysis module
In an embodiment of the present invention, the preprocessing may include: and extracting the WIFI signal, removing abnormal points and performing lost data interpolation processing, performing noise reduction processing according to the quality of the WIFI signal, and using Kalman smoothing filtering and band-pass filtering.
In addition, the signal preprocessing module can continuously adjust the optimal carrier signal according to the analysis result.
4) And the signal analysis module is used for performing time domain analysis and frequency domain analysis on all the input subcarrier signals and transmitting the time domain analysis and frequency domain analysis results of the subcarrier signals to the characteristic extraction and representation module.
In an embodiment of the present invention, the signal analysis module includes: the system comprises a time domain analysis module, a frequency domain analysis module and a wavelet analysis module; wherein:
4.1) the time-frequency analysis module is used for quantizing all subcarrier signals output by the signal preprocessing module into joint distribution on a time domain and a frequency domain, and obtaining joint distribution information on the time domain and the frequency domain of the WIFI signal.
And 4.2) the frequency domain analysis module is used for quantizing all subcarrier signals output by the signal preprocessing module into distribution on a frequency domain to obtain distribution information on the frequency domain of the WIFI signal.
4.3) the wavelet analysis module is used for quantizing all subcarrier signals output by the signal preprocessing module into local time and distribution on a frequency domain to obtain local time frequency domain distribution information of the WIFI signal; through time subdivision at a high frequency position and frequency subdivision at a low frequency position of the WIFI signal, under the condition that a user executes different actions, related information of the high frequency and the low frequency of the WIFI signal is acquired, and meanwhile filtering is also carried out.
It will be understood by those skilled in the art that high frequencies herein refer to detail coefficients corresponding to discrete wavelet changes, and low frequencies refer to approximation coefficients corresponding to discrete wavelet changes.
5) The characteristic extraction and representation module is used for screening out components which meet the set requirements in relation to human activities from the time domain analysis and frequency domain analysis results output by the signal analysis module, and then processing the components into data which can be processed by the classification detection module.
In an embodiment of the present invention, the feature extraction and representation module includes: the device comprises a feature extraction module and a feature representation module; wherein:
5.1) the characteristic extraction module is used for screening out the components which are related to the human activities and meet the set requirements from the time domain analysis and frequency domain analysis results output by the signal analysis module.
The component which satisfies a set requirement in relation to human activity includes: amplitude, phase, signal-to-noise ratio and RSSI of the WIFI signal. For example, whether the human activity correlation satisfies a predetermined requirement may be determined by comparing the human activity correlation with a predetermined threshold value, and if the human activity correlation is greater than the predetermined threshold value, the human activity correlation is considered to be large, and thus, the correlated component is selected.
And 5.2) the feature representation module is used for processing the result output by the feature extraction module into data which can be processed by the classification detection module.
Considering that the feature extraction module can output various types of data, the data needs to be uniformly processed into data types which can be processed by a subsequent process; the specific processing mode and the type of the processed data are set by the user according to experience.
6) And the classification detection module is used for predicting the behavior category of the user according to the output of the feature extraction and representation module.
In an embodiment of the present invention, the classification detection module includes: a classification module and an action detection module; wherein:
6.1) the classification module is used for obtaining a classification result through a classifier according to the output of the feature extraction and representation module.
The classifier includes: a linear support vector machine, a naive Bayes classifier, a logistic regression classifier, a K-nearest neighbor classifier, a random forest classifier, a decision tree, a multi-tier perceptron, or a linear difference analysis. The user can adaptively select the required classifier according to actual requirements.
And 6.2) the action detection module is used for receiving the classification result output by the classification module and converting the classification result into the behavior category of the user.
The behavior categories of the user include: walking, running, standing, sitting still and reference conditions (i.e. empty room) in human motion. It is worth noting that the system has good expansibility, and can adaptively and effectively expand the recognition action set according to the actual action of the user.
As shown in fig. 2, an example of an application scenario is provided. In this example, a WIFI signal transmitter and a user terminal are placed indoors, where the user terminal is a specific model of WIFI receiver, and various modules are arranged in the receiver according to the method shown in fig. 1, and the receiver is placed indoors. In fig. 2, AB indicates two indoor locations, the right device indicates a WIFI signal transmitter, and the left device indicates a user terminal.
The WIFI signal acquisition module is operated on the WIFI signal receiver, and the acquired original WIFI signal is transmitted to the signal preprocessing module, so that indoor people are allowed to perform any activities in the acquisition process; the signal preprocessing module extracts the WIFI signal, removes abnormal points and interpolates lost data, and performs noise reduction processing, Kalman smoothing filtering and band-pass filtering on the signal according to the quality of the WIFI signal, so as to filter high-frequency components and remove direct-current components. The frequency domain analysis module, the time domain analysis module and the wavelet analysis module perform time domain and frequency domain analysis on the preprocessed WIFI signal so as to extract WIFI signal components strongly related to human activities; the time-frequency analysis of the WIFI signals with the coarse granularity is completed by a time domain analysis module and a frequency domain analysis module, and the time-frequency analysis of the WIFI signals with the fine granularity is completed by a wavelet analysis module; inputting the analyzed result into a feature extraction module, screening the WIFI signal, selecting components with high correlation with human activities, such as amplitude, phase, signal-to-noise ratio, RSSI and other information of the WIFI signal, and inputting the components into a feature representation module; the feature representation module processes different types of data in the feature extraction module, unifies the data types which can be processed by the classification module, and then transmits the data types to the classification module; the classification module receives the unified data type transmitted by the characteristic representation module, inputs the unified data type into a classifier of the classification module, the classifier automatically judges five classification results and inputs the results into the action detection module, and the classifier can be a linear support vector machine, a naive Bayes classifier, a logistic regression classifier, a K nearest neighbor classifier, a random forest classifier, a decision tree, a multilayer sensing machine, a linear difference analysis and the like. The motion detection module receives the output from the classification module and converts the classification result of the classification module into human behaviors including walking, running, standing, sitting still and vacant room (which is a reference state in the case of human motion).
It should be noted that the specific behavior category can be set by the user, so that the invention supports more complex human behavior recognition.
Another embodiment of the present invention further provides a real-time behavior recognition method based on WIFI signals, as shown in fig. 3, which mainly includes:
1) and collecting WIFI signals.
2) Preprocessing the acquired WIFI signal, filtering out high-frequency components by using a filtering method, removing direct-current components, and performing signal analysis on all subcarrier data after the high-frequency components are filtered out; wherein the signal analysis comprises: time domain analysis and frequency domain analysis.
3) And screening out components which meet set requirements in relation to human activities from time domain analysis and frequency domain analysis results of the subcarrier signals, and processing the components into data which can be processed in a classification detection process.
4) And predicting the behavior category of the user according to the processed data.
Through the above description of the embodiments, it is clear to those skilled in the art that the above embodiments can be implemented by software, and can also be implemented by software plus a necessary general hardware platform. With this understanding, the technical solutions of the embodiments can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.), and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods according to the embodiments of the present invention.
It will be clear to those skilled in the art that, for convenience and simplicity of description, the foregoing division of the functional modules is merely used as an example, and in practical applications, the above function distribution may be performed by different functional modules according to needs, that is, the internal structure of the system is divided into different functional modules to perform all or part of the above described functions.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (9)
1. A real-time behavior recognition system based on WIFI signals, comprising: a user terminal; the user terminal includes: the device comprises a graphical interface module, a WIFI signal acquisition module, a signal preprocessing module, a signal analysis module, a feature extraction and representation module and a classification detection module; wherein:
the graphical interface module is used for realizing human-computer interaction so as to control the operation of the user terminal and displaying the prediction result output by the classification detection module;
the WIFI signal acquisition module is used for acquiring WIFI signals and transmitting the WIFI signals to the signal preprocessing module;
the signal preprocessing module is used for preprocessing the acquired WIFI signal, filtering out high-frequency components by using a filtering method, removing direct-current components at the same time, and transmitting all subcarrier data subjected to high-frequency component filtering to the signal analysis module;
the signal analysis module is used for performing time domain analysis and frequency domain analysis on all input subcarrier signals and transmitting the time domain analysis and frequency domain analysis results of the subcarrier signals to the characteristic extraction and representation module;
the characteristic extraction and representation module is used for screening out components which meet the set requirements in relation to human activities from the time domain analysis and frequency domain analysis results output by the signal analysis module, and then processing the components into data which can be processed by the classification detection module;
and the classification detection module is used for predicting the behavior category of the user according to the output of the feature extraction and representation module.
2. The real-time behavior recognition system based on the WIFI signal of claim 1, wherein the WIFI signal acquisition module acquires the WIFI signal by using a wireless network card of a set model and a matched wireless network driver.
3. The WIFI signal based real-time behavior recognition system according to claim 1, wherein the preprocessing performed in the signal preprocessing module comprises:
and extracting the WIFI signal, removing abnormal points and performing lost data interpolation processing, performing noise reduction processing according to the quality of the WIFI signal, and using Kalman smoothing filtering and band-pass filtering.
4. The WIFI signal-based real-time behavior recognition system according to claim 1, wherein the signal analysis module comprises: the system comprises a time domain analysis module, a frequency domain analysis module and a wavelet analysis module; wherein:
the time-frequency analysis module is used for quantizing all subcarrier signals output by the signal preprocessing module into joint distribution on a time domain and a frequency domain to obtain joint distribution information on the time domain and the frequency domain of the WIFI signal;
the frequency domain analysis module is used for quantizing all subcarrier signals output by the signal preprocessing module into distribution on a frequency domain to obtain distribution information on the frequency domain of the WIFI signal;
the wavelet analysis module is used for quantizing all subcarrier signals output by the signal preprocessing module into distribution on local time and frequency domain to obtain local time frequency domain distribution information of the WIFI signals; through time subdivision at a high frequency position and frequency subdivision at a low frequency position of the WIFI signal, under the condition that a user executes different actions, the high-frequency and low-frequency related information of the WIFI signal is acquired.
5. The system of claim 1, wherein the feature extraction and representation module comprises: the device comprises a feature extraction module and a feature representation module; wherein:
the characteristic extraction module is used for screening out components which meet the set requirements in relation to human activities from the time domain analysis and frequency domain analysis results output by the signal analysis module; the component which satisfies a set requirement in relation to human activity includes: the amplitude, phase, signal-to-noise ratio and RSSI of the WIFI signal;
and the characteristic representation module is used for processing the result output by the characteristic extraction module into data which can be processed by the classification detection module.
6. The WIFI signal-based real-time behavior recognition system according to claim 1, wherein the classification detection module comprises: a classification module and an action detection module; wherein:
the classification module is used for obtaining a classification result through a classifier according to the output of the feature extraction and representation module;
and the action detection module is used for receiving the classification result output by the classification module and converting the classification result into the behavior category of the user.
7. The WIFI signal based real-time behavior recognition system according to claim 6, wherein the classifier comprises: a linear support vector machine, a naive Bayes classifier, a logistic regression classifier, a K-nearest neighbor classifier, a random forest classifier, a decision tree, a multi-tier perceptron, or a linear difference analysis.
8. The WIFI signal-based real-time behavior recognition system according to claim 1 or 6, wherein the behavior categories of the users include: reference states in walking, running, standing, sitting and body movements.
9. A real-time behavior identification method based on WIFI signals is characterized by comprising the following steps:
collecting WIFI signals;
preprocessing the acquired WIFI signal, filtering out high-frequency components by using a filtering method, removing direct-current components, and performing signal analysis on all subcarrier data after the high-frequency components are filtered out; wherein the signal analysis comprises: time domain analysis and frequency domain analysis;
screening out components which are related to human activities and meet set requirements from time domain analysis and frequency domain analysis results of subcarrier signals, and processing the components into data which can be processed in a classification detection process;
and predicting the behavior category of the user according to the processed data.
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