WO2021068107A1 - Identity recognition method based on ballistocardiogram signal, electronic device, and storage medium - Google Patents

Abstract

An identity recognition method based on a ballistocardiogram signal. The method comprises: collecting a ballistocardiogram signal of a first user; extracting signal features from the ballistocardiogram signal as identity feature information of the first user; and determining the identity of the first user according to the identity feature information of the first user.

Description

Identity recognition method, electronic equipment and storage medium based on heart shock signal Technical field

This application belongs to the field of identity recognition, and in particular relates to an identity recognition method, electronic equipment and storage medium based on a heart shock signal.

Background technique

With the popularization of smart electronic devices, more and more devices can be used to collect the physiological characteristics of users and do further analysis. For example, certain devices can be used to collect human body motion signals and used for further health analysis or user needs in other occasions. When this type of equipment is used to perform health analysis on a user, it is necessary to associate the user's data with the user's identity, otherwise it is easy to cause data confusion.

When associating user data with the user's identity, it is necessary to identify the user's identity. In the prior art, there are many technologies for identifying a person's identity based on biological characteristics, such as fingerprint-based, human face-based, and so on. However, the inventor of the present application found that after introducing a special identification device into a health device, the user needs to do too many operations, which will lead to a poor user experience. Moreover, there are many elderly people who are not very familiar with high-tech products and require high learning costs. Too many operations are prone to errors. For example, in the scenario of collecting user heartbeat data, if the user does not take the initiative to do the identification or forgets to do the identification, the problem of data confusion may still be caused. At the same time, because the health equipment also needs to add additional sensors, the equipment complexity and manufacturing cost of the equipment are increased.

At the same time, the inventor of this application also found that deceptive cracking methods for fingerprints, human faces and other identification methods have appeared on the market. For user data security, a new identification method is required as the old identification method. Way of supplement.

Summary of the invention

This article aims to provide an identity recognition method, electronic equipment and storage medium based on heart shock signals.

An embodiment of the present application provides an identity recognition method based on a cardiac shock signal, including: collecting a cardiac shock signal of a first user; extracting signal characteristics from the cardiac shock signal as the identity characteristics of the first user Information; the identity of the first user is determined according to the identity characteristic information of the first user.

Optionally, the signal features include: heartbeat frequency, frequency domain features, and/or time domain features; extracting signal features from the cardiac shock signal includes: performing frequency domain analysis and/or time domain analysis on the cardiac shock signal Domain analysis.

Another embodiment of the present application provides an electronic device, including: a memory, a processor, and a program executable by the processor stored in the memory, wherein, when the program is executed, the processing The device executes any of the aforementioned identification methods.

Another embodiment of the present application provides a storage medium on which a program executable by a processor is stored, wherein, when the program is executed, the processor executes any one of the aforementioned identification methods.

Utilize the aforementioned identification method and related electronic equipment and storage media. Simple identification of identity features can be directly performed by collecting and analyzing the heart impact signal of the target user. This method can be used in health equipment that includes the ability to collect cardiac shock signals. While the physical health of the user is detected, simple identification can be performed directly by using the aforementioned method, and then the association of the identity of the target user and its physical data can be completed, thereby avoiding data confusion. Since the health device applying the identity recognition method can detect the user while completing the user's identity recognition without feeling the user, the user experience of using the health device is relatively good. Since no additional special identification device is required, the equipment cost and equipment complexity of the health equipment applying this method are relatively low.

Description of the drawings

Fig. 1 shows a schematic flow chart of an identity recognition method based on a cardiac shock signal according to an embodiment of the present application.

Fig. 2A shows a schematic flow chart of an identity recognition method based on a cardiac shock signal according to another embodiment of the present application.

FIG. 2B shows a schematic diagram of the waveform of the cardiac shock signal.

FIG. 2C shows a schematic waveform diagram of the autocorrelation result of the cardiac shock signal.

Figure 2D shows a schematic diagram of the spectrum analysis result of the cardiac shock signal.

Fig. 2E shows a schematic partial flowchart of the identity recognition method shown in Fig. 2A.

Fig. 2F shows a schematic partial flowchart of the identity recognition method shown in Fig. 2A.

FIG. 3A shows a schematic flowchart of an identity recognition method according to another embodiment of the present application.

Figure 3B shows a schematic diagram of the data flow established by the identity feature database.

Fig. 4 shows a schematic flowchart of an identity recognition method according to another embodiment of the present application.

Detailed ways

The following is a specific embodiment to illustrate the implementation of the “identity recognition method, electronic device and storage medium based on a cardiac shock signal” disclosed in the present invention. Those skilled in the art can understand the content disclosed in this specification. The advantages and effects of the invention. The present invention can be implemented or applied through other different specific embodiments, and various details in this specification can also be based on different viewpoints and applications, and various modifications and changes can be made without departing from the spirit of the present invention. In addition, the drawings of the present invention are merely schematic illustrations, and are not drawn according to actual size, and are stated in advance. The following embodiments will further describe the related technical content of the present invention in detail, but the disclosed content is not intended to limit the protection scope of the present invention.

Another embodiment of the present application provides an identity recognition method based on a cardiac shock signal, including: collecting a cardiac shock signal of a first user; extracting signal characteristics from the cardiac shock signal as the identity of the first user Characteristic information; the identity of the first user is determined according to the identity characteristic information of the first user.

Optionally, the signal features include: heartbeat frequency, frequency domain features, and/or time domain features; extracting signal features from the cardiac shock signal includes: performing frequency domain analysis and/or time domain analysis on the cardiac shock signal Domain analysis.

Another embodiment of the present application provides an electronic device, including: a memory, a processor, and a program executable by the processor stored in the memory, wherein, when the program is executed, the processing The device executes any of the aforementioned identification methods.

Another embodiment of the present application provides a storage medium on which a program executable by a processor is stored, wherein, when the program is executed, the processor executes any one of the aforementioned identification methods.

Utilize the aforementioned identification method and related electronic equipment and storage media. Simple identification can be directly performed by collecting the heart impact signal of the target user and analyzing it. This method can be used in health equipment that includes the ability to collect cardiac shock signals. The health device can be used to detect the health status of the target user, and at the same time, simple identity recognition can be performed by using the aforementioned identity recognition method and device. Inadvertently, complete the association of the target user's identity and its physical data, thereby avoiding data confusion. Since the health device applying the identity recognition method can detect the user while completing the user's identity recognition inadvertently, the user experience of using the health device is relatively good. At the same time, the identity recognition method does not require the user to perform a special identity recognition operation, so that the user's operation steps are simplified and the user experience is better. Moreover, the equipment cost and equipment complexity of the health equipment using the aforementioned identity recognition method and device can also be relatively low.

The technical solutions in the embodiments of the present application will be described clearly and completely in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative work shall fall within the protection scope of this application.

It should be understood that the terms "first", "second", "third" and "fourth" in the claims, specification and drawings of this application are used to distinguish different objects, rather than describing a specific order . The terms "comprising" and "comprising" used in the specification and claims of this application indicate the existence of the described features, wholes, steps, operations, elements and/or components, but do not exclude one or more other features, wholes The existence or addition of, steps, operations, elements, components, and/or their collections.

It should also be understood that the terms used in the specification of this application are only for the purpose of describing specific embodiments, and are not intended to limit the application. As used in the specification and claims of this application, unless the context clearly indicates otherwise, the singular forms of "a", "an" and "the" are intended to include plural forms. It should be further understood that the term "and/or" used in the specification and claims of this application refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

Fig. 1 shows a schematic flow chart of an identity recognition method based on a cardiac shock signal according to an embodiment of the present application.

As shown in FIG. 1, the method 1000 may include steps: S110, S120, and S130. among them:

In S110, a heart impact related sensor or a sensor array can be used to collect the user's heart impact signal. For example, first, the sensor or sensor array can be brought into close contact with the user's body preset area, where the preset area can be the user's limbs or torso; then, the user's pressure on the sensor and sensor array caused by factors such as breathing and heartbeat can be collected The signal is the body motion signal; then the breathing signal is separated from the body motion signal to obtain the cardiac shock signal.

In S120, signal analysis can be performed on the cardiac shock signal obtained in S110, and the signal feature of the cardiac shock signal can be extracted as the user's identity feature information. Optionally, the identity feature information may include at least one of a heartbeat frequency, a frequency domain feature, and a time domain feature. S120 may include performing time domain analysis and/or frequency domain analysis on the cardiac shock signal obtained in S110.

As shown in FIG. 1, optionally, the time-domain feature in S120 may include: a wave crest of the cardiac shock signal, where the wave crest may appear periodically in each heartbeat cycle. For example, the time domain feature may include the peak value of a wave crest in the cardiac shock signal and the time interval between two adjacent wave crests. Generally speaking, there are multiple different wave crests in one heartbeat cycle, and each wave crest appears periodically at the same phase in multiple heartbeat cycles. And because in multiple heartbeat cycles, the waveform of the cardiac shock signal is similar. Therefore, in multiple heartbeat cycles, for every two peaks of the multiple peaks with the same phase, the waveforms and amplitudes are relatively similar.

It can be defined here that in multiple heartbeat cycles, the average value of multiple peaks with the same phase is the peak average. Among the multiple peaks in a heartbeat cycle, each peak corresponds to a peak mean, and the peak mean with the largest value can be defined as the maximum peak mean. It is possible to define the peak in each heartbeat cycle corresponding to the maximum peak average value as the main peak, and the other peaks as secondary peaks.

Optionally, the time domain feature may include the peak value of the main peak and the peak value of the secondary peak. Further, in S120, it may further include: determining the heartbeat cycle according to the cardiac shock signal; determining the main peak and the secondary peak among the multiple peaks of the cardiac shock signal; determining the peak value of the main peak and the peak value of the secondary peak. Optionally, the time-domain feature may also include the peak average value corresponding to the main peak, that is, the maximum peak average value, and the peak average value corresponding to each secondary peak. Further, in S120, it may also include: determining the maximum peak average value and the peak average value corresponding to each secondary peak according to the cardiac shock signal.

As shown in FIG. 1, optionally, the time-domain feature in S120 may also include the peak ratio of each secondary peak to the main peak in each heartbeat cycle. Further, the time-domain feature may include: the ratio of the average value of the peak value corresponding to each secondary peak to the average value of the maximum peak value. Optionally, S120 may further include determining the peak ratio of each secondary peak to the main peak in the same heartbeat cycle according to the cardiac shock signal. And optionally, S120 may include determining the ratio of the average peak value corresponding to each secondary peak to the average maximum peak value according to the cardiac shock signal.

As shown in Fig. 1, optionally, in S120, the frequency domain feature may include the fundamental wave component and the harmonic component of the cardiac shock signal, wherein the fluctuation frequency of the fundamental wave component is the heartbeat frequency. Further, the frequency domain characteristics may include the amplitude and phase of the fundamental wave component of the cardiac shock signal and the amplitude and phase of each harmonic component. Furthermore, the frequency domain feature may also include the amplitude ratio of each harmonic component to the fundamental wave component.

As shown in Figure 1, further, S120 may include: determining the heartbeat frequency according to the heartbeat signal; performing frequency-domain analysis on the heartbeat signal based on the heartbeat frequency to obtain the fundamental wave component and harmonic component of the heartbeat signal relative to the heartbeat frequency , Including the amplitude and phase of the fundamental component and the amplitude and phase of the harmonic component. Further, S120 may further include: calculating the amplitude ratio of each harmonic component to the fundamental wave component based on the fundamental wave component and the harmonic component.

As shown in FIG. 1, optionally, S120 may further include: using the heartbeat frequency, frequency domain feature, and time domain feature obtained from the cardiac shock signal as the user's identity feature information.

As shown in Fig. 1, in S130, the user identity feature information obtained in S120 can be used to identify the user. Further, in S130, the user's identity feature information and the identity feature record of at least one user in the identity feature database can also be used to determine the identity of the user.

Optionally, the identity characteristic record may include: at least one piece of identity characteristic information of the at least one user, where at least one piece of identity characteristic information of the at least one user may be obtained by using step S110 and step S120. Optionally, the identity feature record may further include: an identity feature model created based on at least one piece of identity feature information of the at least one user.

Fig. 2A shows a schematic flow chart of an identity recognition method based on a cardiac shock signal according to another embodiment of the present application. FIG. 2B shows a schematic diagram of the waveform of the cardiac shock signal. FIG. 2C shows a schematic waveform diagram of the autocorrelation result of the cardiac shock signal. Figure 2D shows a schematic diagram of the spectrum analysis result of the cardiac shock signal. Fig. 2E shows a schematic partial flowchart of the identity recognition method shown in Fig. 2A. Fig. 2F shows a schematic partial flowchart of the identity recognition method shown in Fig. 2A.

As shown in FIG. 2A, the method 2000 may include: S210, S220, S230, S240, S250, and S260.

As shown in FIG. 2A, in S210, a sensor or a sensor array may be used to collect the user's body motion signal H(t). For example, the sensor or sensor array can be brought into close contact with the user's body preset area, where the preset area can include the user's limbs and any area on the torso; then, the pressure signal caused by the user on the sensor and the sensor array can be collected as the user The body motion signal H(t). Optionally, the time length of the sampling of the body motion signal H(t) is not less than three seconds.

As shown in FIG. 2A, in S220, H(t) can be processed to obtain the user's cardiac shock signal b(t). Fig. 2B is a schematic diagram of the waveform of the cardiac shock signal b(t). For example, in S220, high-pass filtering can be performed on H(t), and the breathing signal in it can be filtered out to obtain the cardiac shock signal b(t). The cutoff frequency of the high-pass filter can be 50 Hz. Optionally, S220 may also include low-pass filtering, band-pass filtering, or other waveform processing operations.

As shown in FIG. 2A, in S230, the heartbeat frequency f _h can be obtained according to the cardiac shock signal b(t). The heartbeat frequency can be calculated according to the time difference between the peaks of the heart shock signal b(t), or the heartbeat frequency can be calculated by other methods. S230 may further include a heartbeat period T _h is calculated in accordance with the heartbeat frequency f _{h. Optionally, in S230, the heartbeat period T h} may also be obtained according to the heart beat signal b(t). The heartbeat frequency f _h recalculated according to the heartbeat period T _h.

As shown in Figure 2A, in S230, a sliding autocorrelation operation can also be performed on the cardiac shock signal b(t) to obtain the autocorrelation result c _t (τ); then the first extreme point of _{c t (τ)} argument value as heartbeat period T _h, or the difference between the argument of c _t (τ) of two adjacent extrema as heartbeat period T _h; further calculates the heartbeat frequency f _h the heartbeat period T _h. Among them, _{the waveform diagram of the autocorrelation result c t} (τ) is shown in Fig. 2C, and the sliding autocorrelation calculation can be performed according to the following formula.

Where f _s is the sampling rate, that is, the number of sampling points in one second. Equation (1) is a sliding autocorrelation calculation with a sliding window of 2 seconds and a sliding range of 1 second.

As shown in FIG. 2A, S230 may also be a sliding autocorrelation operation using other parameters. In S230, the sliding autocorrelation calculation may be performed according to the complete cardiac shock signal b(t), or the sliding autocorrelation calculation may be performed according to the partial cardiac shock signal b(t). The sliding autocorrelation calculation can be performed according to a segment of the cardiac shock signal b(t) from the starting point, and the sliding autocorrelation calculation can be performed according to the middle segment of the cardiac shock signal b(t).

As shown in FIG. 2A, in S240, the peak value of the peak of the cardiac shock signal b(t) is detected. The peak value of the wave crest may include the peak value of the main peak and the peak value of the secondary peak of the detected cardiac shock signal b(t). Further, in S240, the peak value of the main peak of the cardiac shock signal b(t) and the peak values of the three higher peaks of the secondary peaks can be detected.

As shown in Figure 2B, it is a schematic diagram of the waveform of the cardiac shock signal. Among them, T _h is the heartbeat cycle, P ₀ is the main peak in a heartbeat cycle T _{h , and P 1} , P ₂ , and P ₃ are the three secondary peaks with the highest average peak value respectively. Among them, the secondary peaks P ₁ , P ₂ , and P ₃ are sequentially arranged in the order of phase from low to high within the heartbeat cycle T _h starting from the main peak P _0. The secondary peaks P ₁ , P ₂ , and P ₃ can be defined as the first peak, the second peak and the third peak, respectively.

As shown in Figure 2B, generally speaking, P ₁ , P ₂ , and P _{3 are} relatively evenly distributed in the heartbeat cycle T _{h starting from the main peak P 0} , that is, the phases corresponding to the _{secondary peaks P 1} , P ₂ , and P _{3 are in order.} The times are about 1/4T _h , 1/2T _h , and 3/4T _{h respectively} .

As shown in FIG. 2A, optionally, S240 may include collecting and detecting _{the peak value of the main peak P 0} of each heartbeat cycle in the cardiac shock signal b(t), and the three highest sub-peaks P ₁ , P ₂ , and P ₃ Peak. Optionally, S240 may also include calculating the peak ratio _{of the secondary peaks P 1} , P ₂ , P ₃ and the main peak P _{0 of each heartbeat cycle.} Further, S240 may also include calculating the maximum peak average value corresponding to the _{main peak P 0} and the three peak average values corresponding to the _{secondary peaks P 1} , P ₂ , and P _3. Furthermore, S240 may also include the ratio of the average of the three peaks corresponding to the _{secondary peaks P 1} , P ₂ , and P _{3 to the average of the maximum peak.}

As shown in FIG. 2E, optionally S240 may further include: determining the main peak and the secondary peak among the multiple peaks in the cardiac shock signal b(t). Further, S240 may include: S241, S242, and S243. among them:

In S241, calculate the peak average value. _{For example, within a heartbeat cycle T h} of the cardiac shock signal b(t), select several peaks with the largest peaks (for example, four peaks with the largest peaks); calculate the peak average of each of the several peaks , Get the average value of several crests.

In S242, among the several peak averages, the one with the largest value can be determined as the maximum peak average.

In S243, it can be determined that the peaks in each heartbeat period T _h corresponding to the maximum peak average value are the main peaks, and the other peaks are determined as secondary peaks. Further, in the heartbeat cycle T _h starting from each main peak, the peaks with higher peaks can be the first peak, second peak, third peak according to the phase from small to large...

As shown in FIG. 2F, optionally, S240 may further include: S245, S246, and S247. among them:

In S245, the peak values of the multiple peaks with the larger peak value of the cardiac shock signal b(t) can be calculated.

In S246, the largest value among the peaks of the multiple peaks can be selected as the largest peak.

In S247, it can be determined that the peak in phase with the largest peak is the main peak. Specifically, it is determined that a peak that is an integer multiple of the _{heartbeat period Th from the maximum peak is the main peak.} It can be determined that other peaks are secondary peaks.

As shown in FIG. 2A, in S250, frequency domain analysis can be performed on the cardiac shock signal b(t) to obtain the spectral analysis result of the cardiac shock signal b(t). As shown in Figure 2D, it is the spectrum analysis result of the cardiac shock signal b(t). Among them, Z ₀ , Z ₁ , Z ₂ and Z ₃ are the fundamental wave component amplitude, the second harmonic component amplitude, the third harmonic component amplitude and the fourth harmonic component amplitude of the cardiac shock signal b(t), respectively value. Among them, the frequency corresponding to the fundamental component is the heartbeat frequency f _h , and the frequencies corresponding to the amplitude of the second harmonic component, the amplitude of the third harmonic component, and the amplitude of the fourth harmonic component are 2f _h , 3f _h and 4f _{h, respectively} .

As shown in Figure 2A, optionally, S250 may include performing Fourier analysis on the cardiac shock signal b(t) to obtain the fundamental wave component amplitude and the second harmonic component amplitude of the cardiac shock signal b(t) , The amplitude of the third harmonic component and the amplitude of the fourth harmonic component. Further, S250 may also include performing Fourier analysis on the cardiac shock signal b(t) to obtain the amplitudes of other harmonic components of the cardiac shock signal b(t). Furthermore, S250 may also include calculating the ratio between the amplitude of each harmonic component of the cardiac shock signal b(t) and the amplitude of the fundamental wave component. Furthermore, S250 may also include obtaining the phase of each harmonic component of the cardiac shock signal b(t) through calculation.

As shown in FIG. 2A, in S260, the heartbeat frequency f _h and at least one of the time domain feature and the frequency domain feature in S240 and S250 can be used as the user's identity feature information.

FIG. 3A shows a schematic flowchart of an identity recognition method according to another embodiment of the present application. Figure 3B shows a schematic diagram of the data flow established by the identity feature database.

As shown in FIG. 3A, the method 3000 includes: S310 and S320.

As shown in FIG. 3A, in S310, the cardiac shock signal of the user can be collected and the user's identity feature information can be extracted from the cardiac shock signal.

As shown in FIG. 3A, in S320, the user's identity feature information is compared with the feature record in the identity feature database to determine the user's identity.

Optionally, it may also include S330, storing the user's identity feature information in the identity feature database.

Optionally, the identity characteristic database contains the identity characteristic record of at least one user. Wherein, the identity characteristic record may include at least one set of identity characteristic information of the at least one user and identity data of the at least one user. Further, the at least one set of identity feature information can be obtained based on at least one set of heart shock signals of the at least one user; the identity data can include the user's name, gender, age, and the like.

Furthermore, the identity feature library may also include a model created based on a set of identity feature information. For example, multiple sets of identity feature information of the same user can be used as training data to create and train a model to obtain model data. In S320, the user's identity can also be determined according to the user's identity feature information and data model. Optionally, the sample information of the cardiac shock signal can also be used as training data to train the model.

As shown in Figure 3B, a schematic diagram of a data flow established for the feature library. In the solution shown in FIG. 3B, the user's identity feature information and model are obtained according to the user's heart shock data, and the identity feature information, model, and identity information are stored together in the feature database.

Fig. 4 shows a schematic flowchart of an identity recognition method according to another embodiment of the present application.

As shown in FIG. 4, the method 4000 includes: S410, S420, and S430.

In S410, the cardiac shock signal of the user can be collected, and the identity feature information can be extracted from the cardiac shock signal.

In S420, the model can be trained using identity feature information, or the model can be trained using heart shock signal data.

In S430, the model data is obtained.

The present application also includes an embodiment, an electronic device including: a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein, when the computer program is executed, the processor is any one of the foregoing Identification method.

Optionally, the electronic device may be a health device, and includes a sensor or a sensor array for collecting a user's body motion signal.

The present application further includes an embodiment, a storage medium that stores a program executable by the processor, wherein, when the program is executed, the processor executes any of the aforementioned identification methods.

Utilize the aforementioned identification method as well as electronic equipment and storage media. Simple identification of identity features can be directly performed by collecting and analyzing the heart impact signal of the target user. This method can be used in medical equipment that includes the ability to collect cardiac shock signals. By using this method for simple identification, the target user's identity and its physical data are associated with each other, thereby avoiding data confusion. Since the health device applying the identity recognition method can detect the user while completing the user's identity recognition inadvertently, the user experience of using the health device is relatively good. Since the medical device using the identity recognition method does not need to introduce an additional identity recognition device, the structure of the medical device is relatively simple and the cost is relatively low.

Those skilled in the art can understand that the technical solution of the present application can be implemented as a system, a method or a computer program product. Therefore, this application can be expressed in the form of a complete hardware embodiment, a complete software embodiment (including firmware, resident software, microcode, etc.), or a combination of software and hardware. They can generally be referred to as " "Circuit", "Module" or "System". In addition, the present application may be in the form of a computer program product, which is embedded in any tangible expression medium, and the tangible expression medium has computer usable program code embedded in the medium.

The application is described with reference to the flowcharts and/or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the application. It can be understood that each block in the flowcharts and/or block diagrams, and combinations of multiple blocks in the flowcharts and/or block diagrams can be executed by computer program instructions. These computer program instructions can be provided to the processors of general purpose computers, special purpose computers, or other programmable data processing devices, so that the instructions executed by the processors of the computer or other programmable data processing devices can be used to implement flowcharts and/ Or a device with functions/actions specified in one or more blocks of the block diagram.

These computer program instructions can also be stored in a computer-readable medium that can instruct a computer or other programmable data processing device to implement functions in a specific manner, so that the instructions stored in the computer-readable medium can be generated including implementing flowcharts and/or An instruction device for a function/action specified in one or more blocks in the block diagram.

Computer program instructions can also be loaded on a computer or other programmable data processing device to cause a series of operation steps to be executed on the computer or other programmable device to produce a computer-implemented process, so that the computer or other programmable device The instructions executed above provide a process for implementing the function/action specified in one or more blocks in the flowchart and/or block diagram.

The flowcharts and block diagrams in the drawings illustrate the possible implementation architecture, functions, and operations of the system, method, and computer program product according to multiple embodiments of the present application. In this regard, each block in the flowchart or block diagram may represent a module, section, or part of code, which includes one or more executable instructions for implementing specific logic functions. It should also be noted that in some alternative implementations, the functions noted in the block may occur out of the order noted in the drawings. For example, depending on the functionality involved, two blocks shown in succession may actually be executed approximately simultaneously, or the blocks may sometimes be executed in the reverse order. It may also be noted that each block in the block diagram and/or flowchart diagram, as well as the block diagram and/or the block diagram and/or flow diagram, can be implemented by a special purpose hardware-based system that performs specific functions or actions, or a combination of special purpose hardware and computer instructions The flow diagram is a combination of multiple boxes in the diagram.

In the above-mentioned embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in an embodiment, reference may be made to related descriptions of other embodiments. The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the various technical features in the above-mentioned embodiments are not described. However, as long as there is no contradiction in the combination of these technical features, they should all be combined. It is considered as the range described in this specification.

The embodiments of the present application are described in detail above, and specific examples are used in this article to illustrate the principles and implementation manners of the present application. The descriptions of the above embodiments are only used to help understand the methods and core ideas of the present application. At the same time, the changes or deformations made by those skilled in the art based on the ideas of the application, the specific implementation and the scope of application of the application, are all within the protection scope of the application. In summary, the content of this specification should not be construed as a limitation on this application.

Claims (17)

An identity recognition method based on heart shock signals, including: Collect the heart shock signal of the first user; Extracting signal features from the cardiac shock signal as the identity feature information of the first user; The identity of the first user is determined according to the identity characteristic information of the first user. The method according to claim 1, wherein the signal characteristic comprises: a heartbeat frequency, a frequency domain characteristic and/or a time domain characteristic; Extracting signal features from the cardiac shock signal includes: performing frequency domain analysis and/or time domain analysis on the cardiac shock signal. The method according to claim 2, wherein the frequency domain characteristics include: fundamental components and harmonic components of the heartbeat frequency; Performing frequency domain analysis on the cardiac shock signal includes: Acquiring the heartbeat frequency; Perform frequency domain analysis based on the heartbeat frequency on the cardiac shock signal. The method according to claim 3, wherein the frequency domain feature further comprises: the ratio of the amplitude of the harmonic component to the amplitude of the fundamental component. The method according to claim 3, wherein the harmonic components include: second harmonic components, third harmonic components, and/or fourth harmonic components. The method according to claim 2, wherein the time-domain feature comprises: a peak value of a wave crest, the wave crest periodically occurring in the same phase of each heartbeat cycle; Performing time-domain analysis on the cardiac shock signal includes: collecting the peak value of the wave crest in the cardiac shock signal. The method according to claim 6, wherein the peak value of the wave crest comprises: The peak mean value is the mean value of the peak values of multiple peaks with the same phase relative to each heartbeat cycle. The method according to claim 6, wherein the wave peak includes a main peak and a secondary peak, wherein: The peak in each heartbeat cycle corresponding to the maximum peak average value is the main peak, and the other peaks are the secondary peaks, wherein the peak average value is the average value of the peak values of multiple peaks with the same phase, and in the cardiac shock signal, the value is the largest The peak mean value of is the maximum peak mean value; Performing time-domain analysis on the cardiac shock signal includes: determining the main peak among multiple peaks of the cardiac shock signal. 8. The method according to claim 8, wherein the time domain feature further comprises: the ratio of the peak value of the secondary peak to the peak value of the main peak. 8. The method according to claim 8, wherein the secondary peaks include three secondary peaks with the highest peak after each main peak, which are the first peak, the second peak, and the third peak in order. The method according to claim 8, wherein determining the main peak among the multiple peaks of the cardiac shock signal comprises: Extracting the heartbeat cycle from the cardiac shock signal; Determining the maximum peak average value according to the cardiac shock signal; It is determined that within the heartbeat period, the peak corresponding to the maximum peak average value is the main peak. The method according to claim 8, wherein determining the main peak among the multiple peaks of the cardiac shock signal comprises: Extracting the heartbeat cycle from the cardiac shock signal; Extracting a largest wave crest from the plurality of wave crests, where the largest wave crest is a wave crest with the largest peak among the plurality of wave crests; It is determined that in the heartbeat period, the peak with the same phase as the maximum peak is the main peak. The method according to claims 1-12, wherein determining the identity of the first user according to the identity characteristic information of the first user comprises: The identity of the first user is determined according to the identity characteristic information of the first user and the identity characteristic record of at least one user in the identity characteristic database. The method according to claim 13, wherein: The identity feature record of the at least one user includes: at least one piece of identity feature information of the at least one user; The method also includes: Collecting the heart shock signal of the at least one user; Extracting a signal feature from the cardiac shock signal as a piece of identity feature information of the at least one user; The identity feature database is created according to the identity feature record of the at least one user. The method of claim 14, wherein: The identity feature record of the at least one user further includes: an identity feature model of the at least one user; The method also includes: Establish an identity characteristic model of the at least one user according to at least one piece of identity characteristic information of the at least one user. An electronic device, comprising: a memory, a processor, and the processor executable program stored on the memory, and when the program is executed, the processor executes at least one of claims 1-15 The identification method described. A storage medium that stores a program executable by a processor, wherein, when the program is executed, when the program is executed, the processor executes the identity recognition method of at least one of claims 1-15 .

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