WO2017004880A1

WO2017004880A1 - Method, device for behavior recognition and computer storage medium

Info

Publication number: WO2017004880A1
Application number: PCT/CN2015/089058
Authority: WO
Inventors: 贺炎; 王斌; 王忠民; 梁琛; 张�荣; 宋辉; 衡霞; 范琳; 王文浪; 贺菲菲
Original assignee: 中兴通讯股份有限公司
Priority date: 2015-07-08
Filing date: 2015-09-07
Publication date: 2017-01-12
Also published as: CN106339071A

Abstract

A method, a device for behavior recognition and a computer storage medium, the method includes: behavior data corresponding to the behavior to be recognized is obtained (11); a value for a set of wavelets of the behavior data is obtained (12); according to the value for a set of wavelets, the behavior to be recognized is determined (13).

Description

Behavior recognition method, device and computer storage medium

Technical field

The invention relates to a behavior recognition technology, in particular to a behavior recognition method, a device and a computer storage medium.

Background technique

In recent years, with the popularization of mobile devices such as mobile phones, smart watches and smart bracelets, human behavior recognition technology based on mobile devices has become a research hotspot. Among them, the conventional behavior recognition method is as follows: the behavior data of the human body, that is, the sample data, is collected by the sensor configured in the mobile device, the feature data of the collected sample data is optimized, and the behavioral model is used to identify the human behavior. The human body behavior may be daily movements of the human body such as stationary, walking, running, jumping, sitting, standing, and the like. These daily movements of the human body are relatively easy to recognize and have high recognition accuracy due to the large difference between the movements. However, considering the actual application, the human body will also have such subdivided actions such as going upstairs and going downstairs, walking slowly, brisk walking and standing still. The current behavior recognition method is used to identify the accuracy of this subdivision action. Not high, the probability of misidentification is large.

Summary of the invention

In order to solve the existing technical problems, the embodiments of the present invention provide a behavior recognition method, a device, and a computer storage medium, which can improve the accuracy of the subdivision motion recognition and reduce the false recognition probability.

The technical solution of the embodiment of the present invention is implemented as follows:

The embodiment of the invention provides a behavior recognition method, and the method includes:

Obtaining behavior data corresponding to the behavior to be identified;

Obtaining a wavelet feature value of the behavior data;

Determining the behavior to be identified according to the wavelet feature value.

In the foregoing solution, the obtaining behavior data corresponding to the behavior to be identified includes:

Acquiring an acceleration signal generated by the human body when the behavior to be recognized occurs in a predetermined sampling period at a predetermined sampling frequency;

It is determined that any acquired acceleration signal is the behavior data.

In the foregoing solution, the obtaining the wavelet feature value of the behavior data includes:

Performing N-layer wavelet decomposition on the behavior data to obtain an approximation coefficient of each layer, wherein the approximation coefficient includes a high frequency approximation coefficient and a low frequency approximation coefficient, and N is a wavelet decomposition layer number, and is a positive integer greater than or equal to 1;

Retaining a low frequency approximation coefficient of at least one predetermined layer;

A wavelet feature value of each of the at least one predetermined layer is determined according to the low frequency approximation coefficient of the at least one predetermined layer retained.

In the foregoing solution, the method includes:

At least the behavior data, the predetermined wavelet decomposition layer number N=6 is input into a preset wavelet transform function to perform 6-layer wavelet decomposition, to obtain a high-frequency approximation coefficient and a low-frequency approximation coefficient of each layer;

Retain the low frequency approximation coefficients of Layer 2, Layer 3 and Layer 4;

Using the low frequency approximation coefficients of the second layer, the third layer, and the fourth layer, at least one of the following characteristic parameters is calculated: the wavelet energy value of each layer in all the layers retained, the average size of the wavelet peak, and the number of wavelet peaks ;

It is determined that the calculated characteristic parameter of each layer is the wavelet feature value of the corresponding layer.

In the foregoing solution, determining, according to the wavelet feature value, the behavior to be identified, including:

Determining the characteristic parameters of all layers as input signals of the pre-trained classifier model;

Inputting the input signal to the classifier model;

Performing an operation of a preset algorithm on the input signal by the classifier model identifies the behavior to be recognized.

The embodiment of the invention further provides a behavior recognition device, the device comprising:

The first obtaining unit is configured to obtain behavior data corresponding to the behavior to be identified;

a second acquiring unit, configured to acquire a wavelet feature value of the behavior data;

The first determining unit is configured to determine the to-be-identified behavior according to the wavelet feature value.

In the foregoing solution, the first acquiring unit is further configured to:

It is determined that any acquired acceleration signal is the behavior data.

In the foregoing solution, the second acquiring unit is further configured to:

In the foregoing solution, the first determining unit is further configured to:

Inputting the input signal to the classifier model;

The embodiment of the invention further provides a computer storage medium, wherein the computer storage medium stores computer executable instructions, and the computer executable instructions are used to execute the foregoing behavior recognition method.

The behavior recognition method, the device and the computer storage medium provided by the embodiment of the invention acquire the behavior data corresponding to the behavior to be identified; acquire the wavelet feature value of the behavior data; and determine the to-be-identified behavior according to the wavelet feature value. The wavelet feature value is the characteristic parameter in the frequency domain, and the recognition of the human body segmentation action behavior from the frequency domain angle can improve the recognition accuracy and reduce the false recognition probability.

DRAWINGS

1 is a schematic flowchart of implementing a behavior recognition method according to an embodiment of the present invention;

2 is a schematic flowchart of an implementation process of acquiring wavelet feature values of behavior data according to an embodiment of the present invention;

3 is a schematic diagram of coordinates of a sample serial number and a sample amplitude according to an embodiment of the present invention;

FIG. 4 is a schematic structural diagram of a composition of a behavior recognition apparatus according to an embodiment of the present invention.

detailed description

The behavior recognition method of the embodiment of the present invention is applied to a mobile device, which may be a mobile phone, a smart watch, a smart bracelet, a smart glasses, or a tablet PC, a personal Digital assistant PDA, e-reader, etc., are not specifically limited herein.

1 is a schematic flowchart of an implementation of a behavior recognition method according to an embodiment of the present invention; as shown in FIG. 1, the method includes:

Step 11: Obtain behavior data corresponding to the behavior to be identified;

The behavior to be identified is at least two subdivision action actions, such as two subdivision actions, upstairs and downstairs, and also three subdivision actions, such as walking, brisk walking, and in situ, for example, going upstairs and down. The three subdivisions of the building and the walk. The technical solution of the embodiment of the invention lies in how to accurately identify each subdivision action behavior with high accuracy.

In this embodiment, the behavior to be identified includes an upstairs and a downstairs, and the behavior data is an acceleration signal generated by the human body under certain behaviors, such as an acceleration generated when going upstairs and an acceleration generated when going downstairs. The acceleration signal is preferably a synthetic acceleration signal. The acceleration signal can be sensed by an acceleration sensor built into the mobile device. When the acceleration sensor is a three-axis acceleration sensor, the acceleration in the X direction, the Y direction, and the Z direction in the XYZ three-dimensional coordinate system is sensed by the three-axis acceleration sensor, and the accelerations in the three directions are combined. The resulting synthetic acceleration signal is obtained.

Generally, the acceleration sensor adopts an acceleration signal under a certain behavior at a predetermined sampling frequency. For example, the acquisition frequency is 50 times/s, and the behavior data of the human body in the current behavior is collected for 1 s, and the acceleration data is collected within 1 s. The behavior data of 50 times/s*1s=50 is the acceleration signal, and this 1s is regarded as a predetermined sampling period. In this embodiment, the acquiring behavior data corresponding to the behavior to be identified may be acquiring an acceleration signal generated by the human body when the to-be-identified behavior occurs in a predetermined collection period at a predetermined sampling frequency; determining any collected The acceleration signal is the behavior data. And through the subsequent technical solutions to identify which behavior is specifically for the behavior of going upstairs or going downstairs. The sampling frequency and the sampling period can be flexibly set according to actual conditions.

Step 12: Acquire a wavelet feature value of the behavior data.

Here, the wavelet feature value includes at least one of the following characteristic parameters: wavelet energy after wavelet decomposition of the behavior data, average size of the wavelet peak, and number of wavelet peaks.

As shown in FIG. 2, the step 12 further includes:

Step 121: performing N-layer wavelet decomposition on the behavior data to obtain an approximate coefficient of each layer, where the approximate coefficient includes a high frequency approximation coefficient and a low frequency approximation coefficient;

Here, N is a positive integer greater than or equal to 1, which can be flexibly set according to experience. Usually, the maximum value of N needs to satisfy the signal in the largest layer when the behavior data is decomposed into the largest layer, which can be expressed as a complete behavior. As indicated as a complete upstairs action. In the present embodiment, the number of wavelet decomposition layers is preferably N=6. In the pre-configured library function, the wavelet transform function is called, and the current behavior data collected by the acceleration sensor, the pre-selected wavelet basis function, and the wavelet decomposition layer number N are input as three input signals of the wavelet transform function to The wavelet transform function, the output of the wavelet transform function, that is, the behavior data is decomposed by wavelet into the approximate coefficients of each layer of the N layer. The approximation coefficient of each layer includes at least the low frequency approximation coefficient and the high frequency approximation coefficient; wherein, the low frequency approximation coefficient is that the behavior data of the signal that is decomposed into the layer changes slowly can be embodied as the contour part of the behavior signal, and the high frequency approximation coefficient is decomposed The behavioral data that changes rapidly to the layer's signal can be embodied as a detail part of the behavioral signal. In this step, the behavior data is subjected to N-layer wavelet decomposition, and the behavior data is wavelet-transformed into the frequency domain.

In this embodiment, the wavelet transform based on multi-resolution analysis is preferably used to perform wavelet decomposition on the behavior data, and the selected wavelet basis function is Hal wavelet. For the above concepts and uses of the high frequency, low frequency approximation coefficients and wavelet basis functions, please refer to the existing related descriptions, and will not be repeated here.

Step 122: retain a low frequency approximation coefficient of at least one predetermined layer;

Here, since the high frequency approximation coefficient is a detail portion of the signal, the detail portion is generally regarded as noise, and in the embodiment of the present invention, the high frequency approximation coefficient of all layers is discarded; the low frequency approximation coefficient of at least one predetermined layer is retained. Taking N=6 as an example, when the behavior data is subjected to wavelet decomposition of N=6 layers, the low-frequency approximation coefficients of the second, third and fourth layers are selected as the low-frequency approximation coefficients of the first to sixth layers. A low frequency approximation coefficient of at least one predetermined layer retained. The low frequency approximation coefficients of which layer or layers are retained are usually obtained empirically.

Step 123: Determine a wavelet feature value of each layer in the at least one predetermined layer according to the retained low frequency approximation coefficient of the at least one predetermined layer;

The wavelet feature value includes at least one of the following: wavelet energy of each predetermined layer after the wavelet decomposition of the behavior data, an average size of the wavelet peaks, and a number of wavelet peaks. In this embodiment, the wavelet feature value includes the foregoing three parameters as an example. Generally, the acceleration sensor adopts an acceleration signal under a certain behavior at a predetermined sampling frequency. For example, the acquisition frequency is 50 times/s, and the behavior data of the human body in the current behavior is collected for 1 s, and the acceleration data is collected within 1 s. The behavior data of 50 times/s*1s=50 is the acceleration signal, and the 50 acceleration signals are correspondingly identified as the first sample signal, the second sample signal, and the 50th sample signal. As shown in Fig. 3, the XOY axis coordinate system, the X axis represents the sample signal sequence number from 1 to 50, and the ordinate represents the amplitude of one of the predetermined layers after the wavelet decomposition of the 50 sample signals, that is, the low frequency approximation coefficient of the layer. The magnitude, which is a discrete value, is represented by a solid dot. In FIG. 3, it is first found that there are a plurality of amplitude peaks in the amplitude corresponding to so many sample signals, and the first amplitude threshold is preset, as shown by a straight line parallel to the X-axis in FIG. When the amplitude corresponding to each of the previous sample point and the latter sample point exceeds the first amplitude threshold, the amplitude corresponding to the sample point is the wavelet peak of the layer, and according to this method, the 50 layers in the layer can be found. The number of all wavelet peaks in the sample signal, as well as the size of all wavelet peaks. The average size of the wavelet peaks for this layer is equal to the sum of all wavelet peaks divided by the total number of wavelet peaks for that layer. The wavelet energy of this layer is equal to the sum of the squares of the wavelet coefficients obtained by the wavelet transform of the behavior data. For the calculation method of wavelet energy, the average size of wavelet peaks and the number of wavelet peaks, please refer to the existing related description for the concept of wavelet coefficients, which will not be repeated here.

Among them, the average size of the peak of the wavelet and the number of peaks of the wavelet are characterized by small peaks. The wavelet energy distribution feature can reflect the energy distribution of the two behaviors of the upper and lower buildings on different wavelet decomposition layers. The wavelet peak characteristics can reflect the amplitude of the acceleration signal generated by the human body under these two behaviors. The combination of these two features can significantly improve the recognition accuracy of behaviors compared with the related techniques in which human behavior is recognized only by time domain characteristics such as mean square error, variance, and the like. In addition, the wavelet energy, the average size of the wavelet peaks, and the number of wavelet peaks are characteristic parameters in the frequency domain. In the embodiment of the present invention, the recognition of the human behavior is realized by a characteristic parameter in the frequency domain or a combination of at least two characteristic parameters. .

Step 13: Determine the behavior to be identified according to the wavelet feature value.

In this embodiment, using a decision tree in a data mining, a support vector machine (SVM) or a speed learning machine, a classifier model is pre-trained, and wavelets of each layer in the at least one predetermined layer are used. The energy, the average size of the wavelet peaks, and the number of wavelet peaks are input to the classifier model as the input signal of the classifier model. The wavelet energy of each layer calculated in the second, third, and fourth layers, and the average size of the wavelet peaks. And the number of wavelet peaks is input to the classifier model as an input signal, and the operation of the preset signal is performed by the classifier model to identify the to-be-identified behavior; the preset algorithm may be a decision tree, SVM or speed learning machine algorithm. That is, through the classifier model, it is possible to identify which behavior of the human body to be identified.

For the specific formula of the classifier model and its various parameters, please refer to the existing related description, which is not described here. Taking the wavelet decomposition layer number N=6 and the predetermined layer as the second to fourth layers as an example, in the training process of the classifier model, the input of the classifier model includes behavior data with behavior tags, and the behavior data passes through the N layer wavelet. After the decomposition, the wavelet energy of each layer of the second to fourth layers, the average size of the wavelet peaks, and the number of wavelet peaks, using these input signals, the parameters of the classifier model are adjusted accordingly, and the parameters of the classifier model are adjusted. When the output of the classifier model is consistent with the behavior indicated by the behavior tag, the classifier model is trained at this time. Specifically, the process of training a classifier model includes: collecting behavior data in an upstairs behavior by a sensor at a predetermined acquisition frequency, and collecting behavior data in a downstairs behavior multiple times, and characterizing the same behavior. The behavior data is recorded in the same file. For example, the acquisition frequency is For example, 50 times/s, the behavior data of the human body in the behavior of going upstairs is continuously collected for 10s, and 50*10=500 behavioral data that can be characterized as upstairs behavior are collected in these 10s, and these 500 behaviors are collected. The data is stored in a first file that records the behavior data of the upstairs collected by the human body when it is in the upstairs behavior. Continuously collect the behavior data of the human body in the behavior of going downstairs for 5s. In this 5s, collect 50*5=250 behavioral data that can be characterized as downstairs behavior, and save the 250 behavior data in the second file. The second document records the behavior data of the downstairs collected when the human body is in the downstairs behavior. It should be noted that those skilled in the art should know that if the upstairs and the downstairs are regarded as different types of behaviors, the number of files storing different behavior data should be the same as the type of behavior to be identified. It can be seen from the above that the behavior data stored in the first file and the second file is marked with a behavior label (the behavior label can distinguish the behavior corresponding to the behavior data), that is, the behavior stored in the first file. The data corresponds to the behavior of going upstairs, and the behavior data stored in the second file corresponds to the behavior of going downstairs. The N=6 layer wavelet decomposition is performed on all the behavior data stored in the two files, and the wavelet energy, the average size of the wavelet peaks, and the wavelet peak number of each layer of the behavior data in the second to fourth layers are obtained. Part or all of the behavior data with the behavior label, the part or all of the behavior data is decomposed by the N=6 layer wavelet, and the wavelet eigenvalues of each layer of the 2nd to 4th layers are used as the training data set of the classification model, and the training data set is substituted into The classifier model trains the parameters of the model. When the parameters of the classifier model are adjusted such that the output of the classifier model is consistent with the behavior indicated by the behavior tag, the classifier model is well trained. After the classifier model is trained, when a certain behavior data is collected by the acceleration sensor, it will be performed according to the aforementioned steps 11 to 13 to identify the behavior corresponding to the behavior data through the trained classifier model. For the specific training process of the parameters of the classifier model and the concept of the training data set, please refer to the existing related description, which is not described here.

In the foregoing scheme, the description is made by taking the upper floor and the lower building as an example, and in addition, considering that there is a level between the stairs and the stairs in practical applications, the embodiment can also recognize the upstairs, the downstairs, and the walk. Three subdivided action behaviors can also identify three types: slow walking, fast walking, and in situ. Subdivide action behavior.

The technical solutions of the embodiments of the present invention have the following advantages:

1) The collected behavior data is transformed into the frequency domain by wavelet decomposition, and the human behavior recognition is performed from the frequency domain angle, which can improve the recognition accuracy and reduce the false recognition probability compared with the simple human body behavior recognition from the time domain angle. ;

2) by inputting a characteristic parameter in the frequency domain or a combination of at least two characteristic parameters into the pre-trained classifier model to identify the human behavior through the pre-trained classifier model; wherein the classifier model is The training data set used in the pre-training process is at least one characteristic parameter in the frequency domain, so that the accuracy of the trained classifier model is improved, and the highly accurate classifier model is used to identify the subdivided human behavior, which can be improved. Identify accuracy and reduce the probability of misrecognition.

Based on the foregoing behavior recognition method, the embodiment of the present invention further provides a behavior recognition device. As shown in FIG. 4, the device includes: a first acquisition unit 401, a second acquisition unit 402, and a first determination unit 403; ,

The first obtaining unit 401 is configured to acquire behavior data corresponding to the behavior to be identified;

The second obtaining unit 402 is configured to acquire a wavelet feature value of the behavior data.

The first determining unit 403 is configured to determine the to-be-identified behavior according to the wavelet feature value.

The first obtaining unit 401 is further configured to: collect, in a predetermined sampling period, an acceleration signal generated by the human body when the to-be-identified behavior occurs in a predetermined sampling period; and determine that any acquired acceleration signal is Behavioral data.

The second obtaining unit 402 is further configured to perform N-layer wavelet decomposition on the behavior data to obtain an approximate coefficient of each layer, where the approximate coefficient includes a high frequency approximation coefficient and a low frequency approximation coefficient, and N is a wavelet decomposition. The number of layers is a positive integer greater than or equal to 1; a low frequency approximation coefficient of at least one predetermined layer is retained; and wavelet feature values of each of the at least one predetermined layer are determined according to the retained low frequency approximation coefficients of the at least one predetermined layer. Further, the second obtaining unit 402 inputs at least the behavior data and the predetermined wavelet decomposition layer number N=6 to a preset one. The 6-layer wavelet decomposition is performed in the wavelet transform function to obtain the high-frequency approximation coefficient and the low-frequency approximation coefficient of each layer; the low-frequency approximation coefficients of the 2nd, 3rd, and 4th layers are retained; the second layer, the third layer, and the The low-frequency approximation coefficient of the 4 layers is calculated by calculating at least one of the following characteristic parameters: the wavelet energy value of each layer in all the layers retained, the average size of the wavelet peaks, and the number of wavelet peaks; determining the calculated characteristics of each layer The parameters are the wavelet eigenvalues of the respective layers.

The first determining unit 403 is further configured to: determine that the feature parameters of all layers are input signals of the pre-trained classifier model; input the input signal to the classifier model; and pass the classifier model Performing an operation of a preset algorithm on the input signal identifies the behavior to be recognized.

In order to implement the above method, the embodiment of the present invention further provides a behavior recognition device. Since the principle and method for solving the problem are similar, the implementation process and implementation principle of the behavior recognition device can refer to the implementation process and implementation of the foregoing method. The principle description, the repetition will not be repeated.

In a practical application, the first obtaining unit 401, the second obtaining unit 402, and the first determining unit 403 may each be a central processing unit (CPU), or a digital signal processing (DSP), or It is implemented by a microprocessor (MPU, Micro Processor Unit) or a Field Programmable Gate Array (FPGA).

Those skilled in the art should understand that the implementation functions of the processing modules in the behavior recognition device shown in FIG. 4 can be understood by referring to the related description of the foregoing behavior recognition method. It should be understood by those skilled in the art that the functions of the processing units in the behavior recognition device shown in FIG. 4 can be implemented by a program running on a processor, or can be implemented by a specific logic circuit.

Those skilled in the art will appreciate that embodiments of the present invention can be provided as a method, system, Or a computer program product. Accordingly, the present invention can take the form of a hardware embodiment, a software embodiment, or a combination of software and hardware. Moreover, the invention can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) including computer usable program code.

The present invention has been described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (system), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or FIG. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing device to produce a machine for the execution of instructions for execution by a processor of a computer or other programmable data processing device. Means for implementing the functions specified in one or more of the flow or in a block or blocks of the flow chart.

The computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture comprising the instruction device. The apparatus implements the functions specified in one or more blocks of a flow or a flow and/or block diagram of the flowchart.

These computer program instructions can also be loaded onto a computer or other programmable data processing device such that a series of operational steps are performed on a computer or other programmable device to produce computer-implemented processing for execution on a computer or other programmable device. The instructions provide steps for implementing the functions specified in one or more of the flow or in a block or blocks of a flow diagram.

The above is only the preferred embodiment of the present invention and is not intended to limit the scope of the present invention.

Industrial applicability

In this embodiment, the behavior data corresponding to the behavior to be identified is acquired first, and then the behavior data is acquired. a wavelet feature value, and determining the to-be-identified behavior according to the wavelet feature value. Among them, the wavelet feature value is a characteristic parameter in the frequency domain, and the recognition of the human body segmentation action behavior from the frequency domain angle can improve the recognition accuracy and reduce the false recognition probability.

Claims

A behavior recognition method, the method comprising:

Obtaining behavior data corresponding to the behavior to be identified;

Obtaining a wavelet feature value of the behavior data;

Determining the behavior to be identified according to the wavelet feature value.
The method according to claim 1, wherein the obtaining the behavior data corresponding to the behavior to be identified comprises:

Acquiring an acceleration signal generated by the human body when the behavior to be recognized occurs in a predetermined sampling period at a predetermined sampling frequency;

The acquired acceleration signal is determined to be the behavior data.
The method according to claim 1 or 2, wherein the acquiring the wavelet feature value of the behavior data comprises:

Performing N-layer wavelet decomposition on the behavior data to obtain an approximation coefficient of each layer, wherein the approximation coefficient includes a high frequency approximation coefficient and a low frequency approximation coefficient, and N is a wavelet decomposition layer number, and is a positive integer greater than or equal to 1;

Retaining a low frequency approximation coefficient of at least one predetermined layer;

A wavelet feature value of each of the at least one predetermined layer is determined according to the low frequency approximation coefficient of the at least one predetermined layer retained.
The method of claim 3 wherein said method comprises:

At least the behavior data, the predetermined wavelet decomposition layer number N=6 is input into a preset wavelet transform function to perform 6-layer wavelet decomposition, to obtain a high-frequency approximation coefficient and a low-frequency approximation coefficient of each layer;

Retain the low frequency approximation coefficients of Layer 2, Layer 3 and Layer 4;

Calculate at least one of the following using the low frequency approximation coefficients of the second, third, and fourth layers Characteristic parameters: the wavelet energy value of each layer in all layers retained, the average size of wavelet peaks, and the number of wavelet peaks;

It is determined that the calculated characteristic parameter of each layer is the wavelet feature value of the corresponding layer.
The method according to claim 4, wherein the determining the behavior to be identified according to the wavelet feature value comprises:

Determining the characteristic parameters of all layers as input signals of the pre-trained classifier model;

Inputting the input signal to the classifier model;

Performing an operation of a preset algorithm on the input signal by the classifier model identifies the behavior to be recognized.
A behavior recognition device, the device comprising:

a first acquiring unit, configured to acquire behavior data corresponding to the behavior to be identified;

a second acquiring unit, configured to acquire a wavelet feature value of the behavior data;

The first determining unit is configured to determine the to-be-identified behavior according to the wavelet feature value.
The device according to claim 6, wherein the first obtaining unit is further configured to:

Acquiring an acceleration signal generated by the human body when the behavior to be recognized occurs in a predetermined sampling period at a predetermined sampling frequency;

The acquired acceleration signal is determined to be the behavior data.
The device according to claim 6 or 7, wherein the second obtaining unit is further configured to:

Performing N-layer wavelet decomposition on the behavior data to obtain an approximation coefficient of each layer, wherein the approximation coefficient includes a high frequency approximation coefficient and a low frequency approximation coefficient, and N is a wavelet decomposition layer number, and is a positive integer greater than or equal to 1;

Retaining a low frequency approximation coefficient of at least one predetermined layer;

A wavelet feature value of each of the at least one predetermined layer is determined according to the low frequency approximation coefficient of the at least one predetermined layer retained.
The device according to claim 8, wherein the second obtaining unit is further configured to:

At least the behavior data, the predetermined wavelet decomposition layer number N=6 is input into a preset wavelet transform function to perform 6-layer wavelet decomposition, to obtain a high-frequency approximation coefficient and a low-frequency approximation coefficient of each layer;

Retain the low frequency approximation coefficients of Layer 2, Layer 3 and Layer 4;

Using the low frequency approximation coefficients of the second layer, the third layer, and the fourth layer, at least one of the following characteristic parameters is calculated: the wavelet energy value of each layer in all the layers retained, the average size of the wavelet peak, and the number of wavelet peaks ;

It is determined that the calculated characteristic parameter of each layer is the wavelet feature value of the corresponding layer.
The device according to claim 9, wherein the first determining unit is further configured to:

Determining the characteristic parameters of all layers as input signals of the pre-trained classifier model;

Inputting the input signal to the classifier model;

Performing an operation of a preset algorithm on the input signal by the classifier model identifies the behavior to be recognized.
A computer storage medium having stored therein computer executable instructions for performing the method of any one of claims 1 to 5.