Method and apparatus for frequency determination, non-volatile storage medium, and computer device

Abstract

Provided are a method and apparatus for frequency determination, a non-volatile storage medium, and a computer device. The method comprises: acquiring a first time domain signal, wherein the first time domain signal comprises a vital sign time domain signal (S202); performing continuous wavelet transform on the first time domain signal to give corresponding first multi-dimensional information, wherein the first multi-dimensional information comprises time, frequency, and amplitude (S204); recognizing an interference fragment from the first multi-dimensional information and setting an amplitude corresponding to the interference fragment to zero to give a second multi-dimensional information (S206); and determining a vital sign frequency corresponding to the vital sign time domain signal according to the second multi-dimensional information (S208).

Description

Frequency determination method, device, non-volatile storage medium and computer equipment

[0001] This application claims priority to the Chinese patent application filed with the China Patent Office on January 31, 2024, with application number 202410140105.0 and invention name “Frequency determination method, device, non-volatile storage medium and computer equipment”, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The present invention relates to the field of signal processing technology, and in particular to a frequency determination method, device, non-volatile storage medium, and computer equipment. Background Art

[0003] Extracting vital signs from living things is extremely important. Electrocardiogram (ECG) is one of the most commonly used vital sign monitoring technologies. It accurately detects the electrical signals of the human heartbeat and analyzes the signal waveform to extract parameters such as respiratory rate, heartbeat intervals, wave group amplitude, and R-wave amplitude characteristics. However, ECG requires electrodes to be attached to the chest and abdomen, which increases the user's sense of restraint and the risk of skin infection. Furthermore, medical staff are required to manually read the data, which poses a risk of data recording errors.

[0004] Photoplethysmograph (PPG) technology can also achieve real-time monitoring of human respiration and pulse rates. The amplitude of the PPG signal is related to the pressure applied to the skin (the contact force between the sensor and the measurement point). Therefore, PPG signal acquisition is easily interfered with by various factors, such as the sensor wearing position, the wearer's skin condition (such as sweating), and artifacts caused by movement of the wearing part. At the same time, the respiration rate calculated based on PPG is very susceptible to subtle low-frequency interference, which affects accuracy. PPG requires wearing a sensor (finger clip, ear clip, nose clip, etc.), which still cannot completely free the user from the constraints of wearing the sensor.

[0005] Ballistocardiography (BCG) technology measures the surface vibration signals caused by vital signs, enabling real-time, contactless measurement of vital signs without disrupting the user's daily routine. However, BCG signals are inherently weak and susceptible to interference from noise, such as body movement. Consequently, directly measured BCG signals are often drowned out by the noise, resulting in inaccurate vital sign information.

[0006] To address the above-mentioned problems, no effective solutions have been proposed so far. Summary of the Invention

[0007] Embodiments of the present invention provide a frequency determination method, apparatus, non-volatile storage medium, and computer equipment to at least solve the technical problem of low accuracy when determining the frequency of a vital sign signal based on a body surface vibration signal.

[0008] According to one aspect of an embodiment of the present invention, a frequency determination method is provided, including: acquiring a first time domain signal, wherein the first time domain signal includes a vital sign time domain signal; performing a continuous wavelet transform on the first time domain signal to obtain corresponding first multidimensional information, wherein the first multidimensional information includes: time, frequency, and amplitude; identifying interference segments from the first multidimensional information, and setting the amplitude corresponding to the interference segment to zero to generate second multidimensional information; and determining the vital sign frequency corresponding to the vital sign time domain signal based on the second multidimensional information.

[0009] Optionally, identifying interference segments from the first multidimensional information includes: dividing the first multidimensional information into multiple segments of information based on the time; determining the amplitude standard deviation corresponding to each of the multiple segments of information; determining the abnormal standard deviation in the amplitude standard deviation based on a pre-set standard deviation threshold; and determining the information in the multiple segments of information corresponding to the abnormal standard deviation as the interference segment.

[0010] Optionally, identifying the interference segment from the first multi-dimensional information includes: obtaining a preset amplitude threshold; and determining, based on the amplitude threshold, a portion of the first multi-dimensional information having an amplitude greater than the amplitude threshold as the interference segment.

[0011] Optionally, performing a continuous wavelet transform on the first time domain signal to obtain corresponding first multi-dimensional information includes: performing a continuous wavelet transform on the first time domain signal using a complex Gaussian wavelet as a basis function to obtain the first multi-dimensional information.

[0012] Optionally, performing a continuous wavelet transform on the first time domain signal to obtain the corresponding first multi-dimensional information includes: obtaining a frequency regular value of the vital sign time domain signal; determining a wavelet transform window length based on the frequency regular value; and performing a continuous wavelet transform on the first time domain signal according to the wavelet transform window length to obtain the first multi-dimensional information.

[0013] Optionally, obtaining the first time domain signal includes: obtaining the original time domain signal collected by the sensor; obtaining the frequency normal value of the vital sign time domain signal; and performing multi-order bandpass filtering on the original time domain signal according to the frequency normal value to obtain the first time domain signal.

[0014] Optionally, the vital sign time domain signal includes: a heartbeat vibration signal or a respiratory vibration signal; when the vital sign time domain signal is the heartbeat vibration signal, the vital sign frequency is the heart rate; when the vital sign time domain signal is the respiratory vibration signal, the vital sign frequency is the respiratory frequency.

[0015] According to another aspect of an embodiment of the present invention, a frequency determination device is also provided, including: an acquisition module, configured to acquire a first time domain signal, wherein the first time domain signal includes a vital sign time domain signal; a transformation module, configured to perform a continuous wavelet transform on the first time domain signal to obtain corresponding first multi-dimensional information, wherein the first multi-dimensional information includes: time, frequency and amplitude; an identification module, configured to identify interference segments from the first multi-dimensional information, and set the amplitude corresponding to the interference segment to zero to generate second multi-dimensional information; a determination module, configured to determine the vital sign frequency corresponding to the vital sign time domain signal based on the second multi-dimensional information.

[0016] According to another aspect of an embodiment of the present invention, a non-volatile storage medium is provided, wherein the non-volatile storage medium includes a stored program, wherein when the program is executed, the device where the non-volatile storage medium is located is controlled to execute any one of the above-mentioned frequency determination methods.

[0017] According to another aspect of an embodiment of the present invention, a computer device is provided, comprising a memory and a processor, wherein the memory is used to store programs, and the processor is used to run the programs stored in the memory, wherein the program executes any one of the above-mentioned frequency determination methods when running.

[0018] In an embodiment of the present invention, a first time domain signal is obtained, wherein the first time domain signal includes a vital sign time domain signal; a continuous wavelet transform is performed on the first time domain signal to obtain corresponding first multi-dimensional information, wherein the first multi-dimensional information includes: time, frequency and amplitude; an interference segment is identified from the first multi-dimensional information, and the amplitude corresponding to the interference segment is set to zero to generate second multi-dimensional information; based on the second multi-dimensional information, the vital sign frequency corresponding to the vital sign time domain signal is determined, thereby achieving the purpose of accurately measuring the vital sign frequency based on the time domain signal, thereby realizing the technical effect of improving the measurement accuracy of the vital sign frequency, and further solving the technical problem of low accuracy when determining the frequency of the vital sign signal based on the body surface vibration signal. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0020] FIG1 shows a hardware structure block diagram of a computer terminal for implementing a frequency determination method;

[0021] FIG2 is a schematic flow chart of a frequency determination method according to an embodiment of the present invention;

[0022] FIG3 is a time domain diagram of an original time domain signal provided according to an optional embodiment of the present invention;

[0023] FIG4 is a time domain diagram of a first time domain signal provided according to an optional embodiment of the present invention;

[0024] FIG5 is a schematic diagram of a two-dimensional matrix of first multi-dimensional information provided according to an optional embodiment of the present invention;

[0025] FIG6 is a time domain diagram of a signal of body motion interference provided in an optional embodiment of the present invention;

[0026] FIG7 is a signal spectrum diagram after deleting the body motion interference signal according to an optional embodiment of the present invention;

[0027] FIG8 is a schematic diagram of a two-dimensional matrix of second multi-dimensional information provided according to an optional embodiment of the present invention;

[0028] FIG9 is a signal spectrum diagram after the amplitude is set to zero according to an optional embodiment of the present invention;

[0029] FIG10 is a structural block diagram of a frequency determination apparatus according to an embodiment of the present invention. DETAILED DESCRIPTION

[0030] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0031] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0032] According to an embodiment of the present invention, an embodiment of a frequency determination method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0033] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal, or a similar computing device. Figure 1 shows a hardware block diagram of a computer terminal for implementing the frequency determination method. As shown in Figure 1, the computer terminal 10 may include one or more processors (processors 102a, 102b, ..., 102n are shown in the figure) (the processors may include, but are not limited to, processing devices such as microprocessors MCU or programmable logic devices FPGA), and a memory 104 for storing data. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the BUS bus), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that the structure shown in Figure 1 is merely illustrative and does not limit the structure of the above-mentioned electronic device. For example, the computer terminal 10 may also include more or fewer components than shown in Figure 1, or have a configuration different from that shown in Figure 1.

[0034] It should be noted that the one or more processors and / or other data processing circuits described above may generally be referred to herein as "data processing circuitry." The data processing circuitry may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuitry may be a single, independent processing module, or may be incorporated in whole or in part into any of the other components of the computer terminal 10. As described in the embodiments of the present application, the data processing circuitry serves as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0035] Memory 104 can be used to store software programs and modules for application software, such as the program instructions / data storage device corresponding to the frequency determination method in the embodiments of the present invention. The processor executes the software programs and modules stored in memory 104 to perform various functional applications and data processing, thereby implementing the frequency determination method for the application described above. Memory 104 can include high-speed random access memory (RAM) and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, memory 104 can further include memory remotely located from the processor, and such remote memory can be connected to computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0036] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the computer terminal 10 .

[0037] FIG2 is a flow chart of a frequency determination method according to an embodiment of the present invention. As shown in FIG2 , the method includes the following steps:

[0038] Step S202: Acquire a first time domain signal, wherein the first time domain signal includes a vital sign time domain signal. In the frequency determination method provided in this embodiment, the first time domain signal may be a human body surface vibration signal measured using ballistocardiography (BCG) technology. The body surface vibration signal is a weak force signal, which is a weak change in the external pressure on the human body surface caused by cardiac pulsation and arterial blood flow. It can reflect the heart activity and other physiological states of the human body. By analyzing and processing the body surface vibration signal, vital sign information such as respiratory rate, heart rate, position and disposition, sleep quality, and fatigue level can be extracted.

[0039] In the application scenario of non-sensory vital sign monitoring equipment, there is no physical restraint between the user and the BCG sensor, so body motion interference caused by the relative movement between the body and the sensor is difficult to avoid in the detection signal. In existing signal processing technology, when the BCG signal is subject to certain interference, the calculation results of measuring vital sign frequency are subject to large errors. However, the frequency determination method provided in this embodiment can greatly improve the vital sign frequency measured using the BCG signal in such scenarios, allowing users to obtain their own vital sign frequency in real time without physical restraint.

[0040] As an optional embodiment, the vital sign time domain signal may include a heartbeat vibration signal or a respiratory vibration signal; when the vital sign time domain signal is a heartbeat vibration signal, the vital sign frequency is the heart rate; when the vital sign time domain signal is a respiratory vibration signal, the vital sign frequency is the respiratory frequency.

[0041] Heartbeat and respiration are common vital signs that cause surface vibrations on the human body. Therefore, the first time-domain signal collected by the BCG sensor includes a heartbeat vibration signal and / or a respiratory vibration signal. The method provided in this embodiment can extract heart rate and / or respiratory rate in real time based on the following method. This optional embodiment can improve the compatibility of existing BCG vital sign monitoring technology with body motion interference signals, thereby improving the accuracy of calculating the user's heart rate and respiratory rate when the user is in a non-static state.

[0042] Optionally, the vital sign time domain signal may also include other types of regular vibration signals of the human body. For example, when the muscles of the human body are spasming, the muscles will also vibrate regularly, so there will also be a relatively stable spasm frequency. The frequency determination method provided in this application can also be used to determine the spasm frequency of the muscles. For another example, the user may be required to perform a specific action that causes surface vibration at a specific frequency, such as requiring the user to clench their fist once per second, or to control a muscle of the body to contract once per second, and then obtain a first time domain signal. The vital sign time domain signal included in the first time domain signal is the specific action signal corresponding to the specific action; and then the frequency determination method provided in this application is used to identify the frequency of the specific action signal to obtain the vital sign frequency corresponding to the specific action.

[0043] As an optional embodiment, the process of obtaining the first time domain signal may include the following steps: obtaining the original time domain signal collected by the sensor; obtaining the frequency normal value of the vital sign time domain signal; performing multi-order bandpass filtering on the original time domain signal according to the frequency normal value to obtain the first time domain signal.

[0044] The original time domain signal can be a body surface vibration signal directly collected by a BCG sensor. Optionally, an optical fiber sensor can be used to collect the body surface vibration signal. A piezoelectric polyvinylidene fluoride sensor or a piezoelectric film sensor can also be used. The sensor can be embedded in the human body's surrounding environment, such as pillows, mattresses, tables and chairs, to detect vital sign time domain signals in real time under non-contact conditions.

[0045] Among them, the frequency regular value of the vital sign time domain signal can be the frequency limit value corresponding to the vital sign. For example, when the vital sign represents the user's heartbeat, it is generally believed that the common heartbeat range of the human body is 60 to 180 times / minute. Then, the frequency regular value corresponding to the heartbeat can be set to a lower limit of 60 times / minute and an upper limit of 180 times / minute. Then, the original time domain signal is subjected to multi-order bandpass filtering based on the upper and lower limits, and the signals with a frequency below 60 times / minute and the signals with a frequency above 180 times / minute in the original time domain signal are filtered out, and the signals between the lower frequency limit and the upper frequency limit are retained to obtain the first time domain signal.

[0046] Figure 3 is a time domain graph of an original time domain signal provided according to an optional embodiment of the present invention, and Figure 4 is a time domain graph of a first time domain signal provided according to an optional embodiment of the present invention. The first time domain signal in Figure 4 is a preprocessed version of the original time domain signal provided in Figure 3 , including the multi-order bandpass filtering proposed in the aforementioned optional embodiment. Clearly, after filtering preprocessing, the time domain graph of the first time domain signal already displays relatively good main peak characteristics of vital signs.

[0047] Step S204: Perform continuous wavelet transform on the first time domain signal to obtain corresponding first multi-dimensional information, wherein the first multi-dimensional information includes: time, frequency, and amplitude.

[0048] The continuous wavelet transform solves the problem that the time domain and frequency domain information of the signal in the Fourier transform cannot be localized at the same time, and supports adaptively changing the window length based on the short-time Fourier transform. It has better time resolution characteristics in the high-frequency part of the signal and better frequency resolution characteristics in the low-frequency part. After the first time domain signal is subjected to a continuous wavelet transform, the three-dimensional information of time, frequency and amplitude of the first time domain signal can be obtained at the same time. The transformation result (i.e., the first multi-dimensional information) can be displayed in the form of a two-dimensional matrix. Figure 5 is a schematic diagram of the two-dimensional matrix of the first multi-dimensional information provided according to an optional embodiment of the present invention. As shown in Figure 5, the horizontal axis of the matrix can represent time information, the vertical axis represents frequency information (i.e., the scale information of the continuous wavelet transform), and the numerical value in the grid represents amplitude information. Based on the schematic diagram of Figure 5, the wavelet transform displays the frequency and amplitude information of the input signal (i.e., the first time domain signal) on the time axis.

[0049] The continuous wavelet transform algorithm can remove body motion interference from the first time domain signal, thereby more accurately extracting effective vital sign information, namely, extracting vital sign frequency. Compared with time domain algorithms, the continuous wavelet transform algorithm avoids the uncertainty errors caused by irregular main peak detection. At the same time, compared with traditional frequency domain algorithms, the continuous wavelet transform algorithm adds time domain information, which can more accurately identify body motion interference that appears randomly in the time domain, thereby eliminating interference fragments and improving vital sign monitoring accuracy.

[0050] As an optional embodiment, the following steps can be adopted to perform a continuous wavelet transform on the first time domain signal to obtain the corresponding first multi-dimensional information: obtain the frequency normal value of the vital sign time domain signal; determine the wavelet transform window length based on the frequency normal value; perform a continuous wavelet transform on the first time domain signal based on the wavelet transform window length to obtain the first multi-dimensional information.

[0051] In the continuous wavelet transform (CWT), the window length can be adaptively changed. The wavelet transform achieves this by selecting different wavelet basis functions and scaling factors. In the CWT, the window length depends on the length of the wavelet basis function and the value of the scaling factor. By changing the scaling factor, the wavelet basis function can be stretched or retracted, thereby changing the window length. When the scaling factor is greater than 1, the wavelet basis function is stretched, resulting in an increase in the window length, corresponding to the high-frequency portion of the signal; when the scaling factor is less than 1, the wavelet basis function is compressed, resulting in a decrease in the window length, corresponding to the low-frequency portion of the signal. Therefore, by continuously changing the scaling factor, the signal can be analyzed at multiple scales and the window length can be adaptively selected. This adaptive window length variation gives the CWT great flexibility in time-frequency analysis, enabling it to better adapt to the signal processing requirements of different frequency components.

[0052] At the same time, an appropriate wavelet transform window length can be selected based on the frequency normal value of the vital sign time domain signal. It is understandable that if the selected window length is too short, the number of vital sign signal cycles included in the first time domain signal collected within a window length is too small, or even less than one, so the recognition effect of the vital sign signal cannot be accurate; if the selected window length is too long, the signal within the window length includes signals from too long ago, and the use of such a signal to determine the user's vital sign frequency at the current moment is not real-time, that is, it cannot reflect the true value of the user's vital sign frequency at the current moment. Therefore, an appropriate window length can be selected based on the frequency normal value of the vital sign time domain signal to perform a continuous wavelet transform. For example, when the vital sign is heartbeat or breathing, the wavelet transform window length can be selected to be 10 to 16 seconds.

[0053] As an optional embodiment, performing continuous wavelet transform on the first time domain signal to obtain corresponding first multi-dimensional information can be carried out in the following manner: performing continuous wavelet transform on the first time domain signal using complex Gaussian wavelet as basis function to obtain first multi-dimensional information.

[0054] The closer the basis function is to the waveform of the first time-domain signal, the better the result of the continuous wavelet transform. The complex Gaussian wavelet is closer to the waveforms of the respiratory and heartbeat signals. Therefore, the complex Gaussian wavelet can be used as the basis function to perform a continuous wavelet transform on the first time-domain signal, achieving a better transformation effect. The complex Gaussian wavelet is a wavelet basis function in the wavelet transform, which is composed of the nth-order derivative of the complex Gaussian function. The complex Gaussian wavelet has symmetry and linear phase characteristics, and therefore can maintain the original shape of the signal after transformation. In addition, the complex Gaussian wavelet has advantages such as symmetry, regularity, and linear phase characteristics, and has a wide range of applications in signal processing.

[0055] Step S206 : Identify interference segments from the first multi-dimensional information, and set the amplitudes corresponding to the interference segments to zero to generate second multi-dimensional information.

[0056] The dotted box in Figure 5 represents the identified interference segment. Interference segments can be considered as interference components generated by the interference signal. Interference signals may be caused by the user's non-static body posture. For example, the user may roll over, turn around, and other posture changes. These interfering movements generate vibration signals that are also collected by the BCG sensor. Therefore, the influence of the interference signal must be eliminated to achieve better vital sign frequency recognition results.

[0057] In this step, the amplitude corresponding to the interference segment is set to zero instead of directly removing the interference segment in the time domain. This is to avoid large errors when calculating the vital sign frequency. Figure 6 is a time domain diagram of the signal of body motion interference provided by an optional embodiment of the present invention. The portion in the dotted box in Figure 6 is the signal fluctuation caused by body motion interference. If the signal portion generated by the body motion interference in the dotted box is directly deleted, the signal after removal will form a large step at the splicing point and affect the continuity of the signal, which can easily lead to large errors when calculating the vital sign frequency in the frequency domain. Figure 7 is a signal spectrum diagram after deleting the body motion interference signal provided by an optional embodiment of the present invention. As shown in Figure 7, after deleting the interference segment, the remaining first time domain signal is spliced ​​to obtain a signal spectrum diagram. The splicing causes pseudo-peak interference in the spectrum diagram, which makes the calculated heart rate value (63BPM) far away from the true value (88BPM), and the calculation result has a large deviation.

[0058] FIG8 is a schematic diagram of a two-dimensional matrix of the second multidimensional information provided according to an optional embodiment of the present invention. As shown in FIG8 , the amplitudes of the interfering segments selected by the dashed box are modified to zero. Similar to the traditional Fourier transform, the continuous wavelet transform also uses the corresponding relationship between amplitude and frequency when calculating in the frequency domain. Therefore, setting the amplitude to zero can not only eliminate the interfering time segments, but also does not affect the calculation accuracy of the vital sign frequency of the remaining normal segments in the frequency domain. Optionally, after setting the amplitude to zero, the amplitude of the continuous wavelet transform matrix (i.e., the second multidimensional information) after eliminating the interfering segments can be summed by row (time axis). The frequency corresponding to the row with the largest amplitude sum is the vital sign frequency being calculated, such as heart rate or respiratory rate. FIG9 is a signal spectrum diagram after the amplitude is set to zero according to an optional embodiment of the present invention. It can be seen that the frequency domain signal quality is significantly improved, and the calculated value is close to the true value, both of which are 88 BPM.

[0059] As an optional embodiment, identifying interference segments from the first multidimensional information includes: dividing the first multidimensional information into multiple segments of information based on time; determining the amplitude standard deviation corresponding to each of the multiple segments of information; determining the abnormal standard deviation in the amplitude standard deviation based on a pre-set standard deviation threshold; and determining the information corresponding to the abnormal standard deviation in the multiple segments of information as an interference segment.

[0060] Optionally, each of the multiple segments of information may include a portion of the first multi-dimensional information, and the multiple segments of information may be distinguished from each other according to the time axis interval. For each segment of information in the multiple segments of information, the amplitude standard deviation corresponding to the segment of information is calculated. It can be understood that since the vital sign signal is a signal with very regular fluctuations, the amplitude standard deviation in each segment of information should not be much different. If the amplitude standard deviation of a certain segment of information is too different from the amplitude standard deviation of other segments of information, it can be considered that there is a large component of body motion interference signal in the segment of information, so the segment of information can be marked as an interference segment. Optionally, the standard deviation threshold can be predetermined. When the average difference between the amplitude standard deviation of a certain segment of information and the amplitude standard deviation of other segments of information is greater than the standard deviation threshold, the amplitude standard deviation is determined as an abnormal standard deviation.

[0061] As an optional embodiment, identifying interference segments from the first multidimensional information includes: obtaining a preset amplitude threshold; and determining, based on the amplitude threshold, a portion of the first multidimensional information having an amplitude greater than the amplitude threshold as an interference segment.

[0062] This optional embodiment also provides a method for identifying interference segments, that is, an amplitude threshold can be determined based on experience. The amplitude of a conventional vital sign signal after wavelet transform is generally not greater than the amplitude threshold. Then, the part of the first multi-dimensional information with an amplitude greater than the amplitude threshold can be marked as the amplitude generated due to the provision of an interference signal, and therefore this part can be marked as an interference segment.

[0063] Step S208: Determine the vital sign frequency corresponding to the vital sign time domain signal according to the second multi-dimensional information.

[0064] In the above steps, a first time domain signal is obtained, wherein the first time domain signal includes a vital sign time domain signal; a continuous wavelet transform is performed on the first time domain signal to obtain corresponding first multi-dimensional information, wherein the first multi-dimensional information includes: time, frequency and amplitude; interference segments are identified from the first multi-dimensional information, and the amplitudes corresponding to the interference segments are set to zero to generate second multi-dimensional information; based on the second multi-dimensional information, the vital sign frequency corresponding to the vital sign time domain signal is determined, thereby achieving the purpose of accurately measuring the vital sign frequency based on the time domain signal, thereby realizing the technical effect of improving the measurement accuracy of the vital sign frequency, and further solving the technical problem of low accuracy when determining the frequency of the vital sign signal based on the body surface vibration signal.

[0065] The technical solution provided by this application can provide the following beneficial effects: During the data preprocessing stage, multiple bandpass filters are used to reduce noise interference outside the main frequency band. A continuous wavelet transform is applied to the preprocessed first time-domain signal, combining frequency and time domain information to eliminate motion interference fragments, thereby improving signal quality and ensuring the accuracy and robustness of vital sign information such as heart rate and respiratory rate. Furthermore, this method can be implemented on an embedded system at a low computational cost, for example, in fiber-optic vital sign monitoring devices, facilitating market promotion.

[0066] It should be noted that for the aforementioned method embodiments, for simplicity of description, they are all expressed as a series of action combinations. However, those skilled in the art should be aware that the present invention is not limited by the order of the actions described, because according to the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0067] Through the description of the above embodiments, those skilled in the art can clearly understand that the frequency determination method according to the above embodiment can be implemented by software plus the necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is a more preferred embodiment. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of various embodiments of the present invention.

[0068] According to an embodiment of the present invention, a frequency determination device for implementing the above-mentioned frequency determination method is also provided. FIG10 is a structural block diagram of the frequency determination device provided according to an embodiment of the present invention. As shown in FIG10 , the frequency determination device includes: an acquisition module 112, a transformation module 114, an identification module 116, and a determination module 118. The frequency determination device is described below.

[0069] An acquisition module 112 is configured to acquire a first time domain signal, wherein the first time domain signal includes a vital sign time domain signal;

[0070] The transform module 114 is connected to the acquisition module 112 and is configured to perform a continuous wavelet transform on the first time domain signal to obtain corresponding first multi-dimensional information, wherein the first multi-dimensional information includes: time, frequency, and amplitude;

[0071] an identification module 116 connected to the transformation module 114 and configured to identify interference segments from the first multi-dimensional information and set the amplitudes corresponding to the interference segments to zero to generate second multi-dimensional information;

[0072] The determination module 118 is connected to the identification module 116 and is configured to determine the vital sign frequency corresponding to the vital sign time domain signal according to the second multi-dimensional information.

[0073] It should be noted that the acquisition module 112, transformation module 114, identification module 116, and determination module 118 described above correspond to steps S202 to S208 in the embodiment. The examples and application scenarios implemented by these modules and corresponding steps are the same, but are not limited to the contents disclosed in the above embodiment. It should be noted that the above modules, as part of the device, can be run in the computer terminal 10 provided in the embodiment.

[0074] An embodiment of the present invention may provide a computer device. Optionally, in this embodiment, the computer device may be located in at least one of a plurality of network devices in a computer network. The computer device includes a memory and a processor.

[0075] The memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the frequency determination method and device in the embodiments of the present invention. The processor executes the software programs and modules stored in the memory to perform various functional applications and data processing, thereby implementing the above-mentioned frequency determination method. The memory may include high-speed random access memory and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory may further include memory remotely located relative to the processor, and these remote memories may be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0076] The processor can call the information and application stored in the memory through the transmission device to perform the following steps: obtain a first time domain signal, wherein the first time domain signal includes a vital sign time domain signal; perform a continuous wavelet transform on the first time domain signal to obtain corresponding first multi-dimensional information, wherein the first multi-dimensional information includes: time, frequency and amplitude; identify interference segments from the first multi-dimensional information, and set the amplitude corresponding to the interference segment to zero to generate second multi-dimensional information; determine the vital sign frequency corresponding to the vital sign time domain signal based on the second multi-dimensional information.

[0077] Optionally, the processor may also execute the program code of the following steps: identifying interference segments from the first multidimensional information, including: dividing the first multidimensional information into multiple segments based on time; determining the amplitude standard deviation corresponding to each of the multiple segments; determining the abnormal standard deviation in the amplitude standard deviation based on a pre-set standard deviation threshold; and determining the information corresponding to the abnormal standard deviation in the multiple segments as an interference segment.

[0078] Optionally, the processor may also execute the program code of the following steps: identifying interference segments from the first multidimensional information, including: obtaining a preset amplitude threshold; and determining, based on the amplitude threshold, a portion of the first multidimensional information having an amplitude greater than the amplitude threshold as an interference segment.

[0079] Optionally, the processor may also execute the program code of the following steps: performing a continuous wavelet transform on the first time domain signal to obtain corresponding first multi-dimensional information, including: performing a continuous wavelet transform on the first time domain signal using a complex Gaussian wavelet as a basis function to obtain first multi-dimensional information.

[0080] Optionally, the above-mentioned processor can also execute the program code of the following steps: performing a continuous wavelet transform on the first time domain signal to obtain the corresponding first multi-dimensional information, including: obtaining the frequency normal value of the vital sign time domain signal; determining the wavelet transform window length based on the frequency normal value; performing a continuous wavelet transform on the first time domain signal according to the wavelet transform window length to obtain the first multi-dimensional information.

[0081] Optionally, the above-mentioned processor can also execute the program code of the following steps: obtaining a first time domain signal, including: obtaining the original time domain signal collected by the sensor; obtaining the frequency normal value of the vital sign time domain signal; performing multi-order bandpass filtering on the original time domain signal according to the frequency normal value to obtain the first time domain signal.

[0082] Optionally, the above-mentioned processor can also execute the program code of the following steps: the vital sign time domain signal includes: a heartbeat vibration signal or a respiratory vibration signal; when the vital sign time domain signal is a heartbeat vibration signal, the vital sign frequency is the heart rate; when the vital sign time domain signal is a respiratory vibration signal, the vital sign frequency is the respiratory frequency.

[0083] A person skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the hardware related to the terminal device through a program, and the program can be stored in a non-volatile storage medium, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0084] The embodiment of the present invention further provides a non-volatile storage medium. Optionally, in this embodiment, the non-volatile storage medium can be used to store the program code executed by the frequency determination method provided in the above embodiment.

[0085] Optionally, in this embodiment, the non-volatile storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.

[0086] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for executing the following steps: obtaining a first time domain signal, wherein the first time domain signal includes a vital sign time domain signal; performing a continuous wavelet transform on the first time domain signal to obtain corresponding first multi-dimensional information, wherein the first multi-dimensional information includes: time, frequency and amplitude; identifying interference segments from the first multi-dimensional information, and setting the amplitude corresponding to the interference segment to zero to generate second multi-dimensional information; and determining the vital sign frequency corresponding to the vital sign time domain signal based on the second multi-dimensional information.

[0087] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: identifying interference segments from the first multi-dimensional information, including: dividing the first multi-dimensional information into multiple segments of information based on time; determining the amplitude standard deviation corresponding to each of the multiple segments of information; determining the abnormal standard deviation in the amplitude standard deviation based on a pre-set standard deviation threshold; and determining the information corresponding to the abnormal standard deviation in the multiple segments of information as an interference segment.

[0088] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: identifying interference segments from the first multidimensional information, including: obtaining a predetermined amplitude threshold; and according to the amplitude threshold, determining the portion of the first multidimensional information whose amplitude is greater than the amplitude threshold as an interference segment.

[0089] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for executing the following steps: performing a continuous wavelet transform on the first time domain signal to obtain corresponding first multidimensional information, including: performing a continuous wavelet transform on the first time domain signal using a complex Gaussian wavelet as a basis function to obtain first multidimensional information.

[0090] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for executing the following steps: performing a continuous wavelet transform on the first time domain signal to obtain corresponding first multidimensional information, including: obtaining a frequency regular value of the vital sign time domain signal; determining a wavelet transform window length based on the frequency regular value; performing a continuous wavelet transform on the first time domain signal based on the wavelet transform window length to obtain first multidimensional information.

[0091] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for executing the following steps: obtaining a first time domain signal, including: obtaining an original time domain signal collected by a sensor; obtaining a frequency normal value of the vital sign time domain signal; performing multi-order bandpass filtering on the original time domain signal according to the frequency normal value to obtain a first time domain signal.

[0092] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for executing the following steps: the vital sign time domain signal includes: a heartbeat vibration signal or a respiratory vibration signal; when the vital sign time domain signal is a heartbeat vibration signal, the vital sign frequency is the heart rate; when the vital sign time domain signal is a respiratory vibration signal, the vital sign frequency is the respiratory frequency.

[0093] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0094] In the above embodiments of the present invention, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0095] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of units can be a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0096] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple units. Some or all of the units may be selected to achieve the purpose of the present embodiment according to actual needs.

[0097] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0098] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a non-volatile storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, and other media that can store program codes.

[0099] The above are only preferred embodiments of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention. Industrial Applicability

[0100] The solution provided by the embodiment of the present application can be applied to the field of signal processing technology. In the embodiment of the present application, a first time domain signal is obtained, wherein the first time domain signal includes a vital sign time domain signal; a continuous wavelet transform is performed on the first time domain signal to obtain corresponding first multi-dimensional information, wherein the first multi-dimensional information includes: time, frequency and amplitude; interference segments are identified from the first multi-dimensional information, and the amplitude corresponding to the interference segment is set to zero to generate second multi-dimensional information; based on the second multi-dimensional information, the vital sign frequency corresponding to the vital sign time domain signal is determined, thereby achieving the purpose of accurately measuring the vital sign frequency based on the time domain signal, thereby realizing the technical effect of improving the measurement accuracy of the vital sign frequency, and further solving the technical problem of low accuracy when determining the frequency of the vital sign signal based on the body surface vibration signal.

Claims

1. A frequency determination method, comprising: Acquire a first time domain signal, wherein the first time domain signal includes a vital sign time domain signal; Performing a continuous wavelet transform on the first time domain signal to obtain corresponding first multi-dimensional information, wherein the first multi-dimensional information includes: time, frequency, and amplitude; Identifying interference segments from the first multi-dimensional information, and setting amplitudes corresponding to the interference segments to zero to generate second multi-dimensional information; A vital sign frequency corresponding to the vital sign time domain signal is determined according to the second multi-dimensional information.

2. The method according to claim 1, wherein The identifying the interference segment from the first multi-dimensional information includes: dividing the first multi-dimensional information into a plurality of segments of information based on the time; Determining the amplitude standard deviation corresponding to each of the plurality of pieces of information; Determining an abnormal standard deviation in the amplitude standard deviation according to a preset standard deviation threshold; The information corresponding to the abnormal standard deviation in the multiple pieces of information is determined as the interference segment.

3. The method according to claim 1, wherein The identifying the interference segment from the first multi-dimensional information includes: Obtaining a preset amplitude threshold; According to the amplitude threshold, a portion of the first multi-dimensional information having an amplitude greater than the amplitude threshold is determined as the interference segment.

4. The method according to claim 1, wherein The performing a continuous wavelet transform on the first time domain signal to obtain corresponding first multi-dimensional information includes: A complex Gaussian wavelet is used as a basis function to perform a continuous wavelet transform on the first time domain signal to obtain the first multi-dimensional information.

5. The method according to claim 1, wherein The performing a continuous wavelet transform on the first time domain signal to obtain corresponding first multi-dimensional information includes: Obtaining a frequency regular value of the vital sign time domain signal; Determining a wavelet transform window length according to the frequency normal value; Performing continuous wavelet transform on the first time domain signal according to the wavelet transform window length to obtain the first multi-dimensional information.

6. The method according to claim 1, wherein The acquiring of the first time domain signal includes: Obtaining the original time domain signal collected by the sensor; Obtaining a frequency regular value of the vital sign time domain signal; The original time domain signal is subjected to multi-order bandpass filtering according to the frequency normal value to obtain the first time domain signal.

7. The method according to any one of claims 1 to 6, wherein: The vital sign time domain signal includes: a heartbeat vibration signal or a breathing vibration signal; In the case where the vital sign time domain signal is the heartbeat vibration signal, the vital sign frequency is the heart rate; When the vital sign time domain signal is the respiratory vibration signal, the vital sign frequency is the respiratory frequency.

8. A frequency determination device, comprising: an acquisition module, configured to acquire a first time domain signal, wherein the first time domain signal includes a vital sign time domain signal; a transform module configured to perform a continuous wavelet transform on the first time domain signal to obtain corresponding first multi-dimensional information, wherein the first multi-dimensional information includes: time, frequency, and amplitude; an identification module configured to identify interference segments from the first multi-dimensional information, and set amplitudes corresponding to the interference segments to zero to generate second multi-dimensional information; The determination module is configured to determine the vital sign frequency corresponding to the vital sign time domain signal based on the second multi-dimensional information.

9. A non-volatile storage medium comprising a stored program, wherein: When the program is running, the device where the non-volatile storage medium is located is controlled to execute the frequency determination method according to any one of claims 1 to 7.

10. A computer device comprising a memory and a processor, wherein the memory is used to store a program, and the processor is used to run the program stored in the memory, wherein: When the program is executed, the frequency determination method according to any one of claims 1 to 7 is executed.

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