CN116776087A - Heart rate detection method and related equipment - Google Patents
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Abstract
The invention relates to the technical field of information processing, in particular to a heart rate detection method and related equipment. Wherein, the method comprises the following processing steps: acquiring a plurality of groups of heartbeat sampling data; decomposing the plurality of groups of heartbeat sampling data by adopting a preset sparse dictionary to obtain sparse representation of the plurality of groups of heartbeat sampling data; determining a part of the sparse representation, which is larger than a preset threshold value, as a heartbeat time sequence; performing outlier factor detection on the heartbeat time sequence to obtain a plurality of outlier factors in the heartbeat time sequence; removing the outlier factors to obtain a cleaned heartbeat time sequence; clustering the cleaned heartbeat time sequence; and determining a heart rate detection result according to the clustering result.
Description
[ field of technology ]
The invention relates to the technical field of information processing, in particular to a heart rate detection method and related equipment.
[ background Art ]
Virtual Reality (VR) interaction technology is an important support technology in VR technology. When the VR function is used by the user, vital sign data such as the heart rate of the user needs to be measured in order to ensure the safety of the user. In the prior art, vital sign data of a user is often obtained by adding a light point sensor in a VR interactive device. But the directly acquired heart rate data signal is weak and contains excessive noise and other disturbance data. In the prior art, an effective heart rate detection result is often extracted by removing noise and then clustering. However, in the method, the heart rate signal is weaker, the requirement on sensitivity is higher, and the situation that the clustering result is inaccurate is easy to occur.
[ invention ]
In order to solve the above problems, embodiments of the present invention provide a heart rate detection method and related devices, which can increase accuracy and robustness of heart rate detection results.
In a first aspect, an embodiment of the present invention provides a heart rate detection method, including:
acquiring a plurality of groups of heartbeat sampling data;
decomposing the plurality of groups of heartbeat sampling data by adopting a preset sparse dictionary to obtain sparse representation of the plurality of groups of heartbeat sampling data;
determining a part of the sparse representation, which is larger than a preset threshold value, as a heartbeat time sequence;
performing outlier factor detection on the heartbeat time sequence to obtain a plurality of outlier factors in the heartbeat time sequence;
removing the outlier factors to obtain a cleaned heartbeat time sequence;
clustering the cleaned heartbeat time sequence;
and determining a heart rate detection result according to the clustering result.
In the embodiment of the invention, the outlier factors are removed, so that the situation that clustering is trapped into local optimum and the clustering performance is influenced due to the interference of the outlier factors is avoided. The robustness and the accuracy of the heart rate detection method are improved.
In one possible implementation manner, decomposing the plurality of groups of heartbeat sampling data by adopting a preset sparse dictionary to obtain sparse representation of the plurality of groups of heartbeat sampling data, including:
respectively calculating the inner products of the plurality of groups of heartbeat sampling data and the inner products of all atoms in a preset sparse dictionary;
determining the atom with the largest inner product in the sparse dictionary as the atom which is most matched with the heartbeat sampling data;
subtracting the best matching atoms from the plurality of groups of heartbeat sampling data to split the plurality of groups of heartbeat sampling data into an atomic part and a residual part;
splitting the residual part continuously until the size of the residual part is lower than a preset residual threshold value or the splitting frequency is greater than a preset frequency threshold value;
and determining sparse representation of the plurality of groups of heartbeat sampling data according to the splitting result.
In one possible implementation manner, the detecting the outlier factor on the heartbeat time sequence to obtain a plurality of outlier factors in the heartbeat time sequence includes:
obtaining a corresponding heartbeat moment distance matrix according to the heartbeat moment sequence;
performing linear fitting processing on each row of data of the heartbeat moment distance matrix to obtain fitting coefficients corresponding to each row;
determining a plurality of outlier rows according to the fitting coefficients;
determining the outlier row as the outlier factor.
In one possible implementation manner, clustering the cleaned heartbeat time sequence includes:
and clustering the cleaned heartbeat time sequence by adopting a mean value clustering algorithm.
In a possible implementation manner, clustering the cleaned heartbeat time sequence by adopting a mean value clustering algorithm includes:
taking a plurality of random points in the cleaned heartbeat time sequence as centers, making a corresponding circular sliding window, wherein the radius of the circular sliding window is determined according to the standard deviation of the cleaned heartbeat time sequence;
calculating the average value in each circular sliding window;
and taking each average value as a new center, and moving each circular sliding window towards the density direction of the data in the circular window until the data density in the circular sliding window is not increased any more.
In one possible implementation, determining the heart rate detection result according to the clustering result includes:
calculating the mass center of the data in each circular sliding window;
and determining heart rate detection results according to the centroid.
In a possible implementation manner, before decomposing the plurality of groups of heartbeat sampling data by adopting a preset sparse dictionary to obtain sparse representation of the plurality of groups of heartbeat sampling data, the method further includes:
preprocessing the plurality of groups of heartbeat sampling data to obtain a plurality of groups of heartbeat sampling data after noise is removed;
and decomposing the plurality of groups of heartbeat sampling data after noise removal by adopting a preset sparse dictionary to obtain sparse representation of the plurality of groups of heartbeat sampling data.
In a second aspect, an embodiment of the present invention provides a heart rate detection apparatus, including:
the acquisition module is used for acquiring a plurality of groups of heartbeat sampling data;
the processing module is used for decomposing the plurality of groups of heartbeat sampling data by adopting a preset sparse dictionary to obtain sparse representation of the plurality of groups of heartbeat sampling data;
the processing module is further configured to determine a portion of the sparse representation greater than a preset threshold as a heartbeat time sequence;
the processing module is further used for detecting outlier factors of the heartbeat time sequence to obtain a plurality of outlier factors in the heartbeat time sequence;
the processing module is further used for removing the outlier factors to obtain a cleaned heartbeat time sequence;
the processing module is also used for clustering the cleaned heartbeat time sequence;
the processing module is further used for determining a heart rate detection result according to the clustering result.
In a third aspect, an embodiment of the present invention provides an electronic device, including:
at least one processor; and
at least one memory communicatively coupled to the processor, wherein:
the memory stores program instructions executable by the processor, the processor invoking the program instructions to perform the methods of the first through fourth aspects.
In a fourth aspect, embodiments of the present invention provide a computer-readable storage medium storing computer instructions that cause a computer to perform the methods of the first to second aspects.
It should be understood that, the method described in the fourth aspect of the embodiment of the present invention is consistent with the technical solutions of the first to second aspects of the embodiment of the present invention, and the beneficial effects obtained by each aspect and the corresponding possible implementation manner are similar, and are not repeated.
[ description of the drawings ]
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a heart rate detection method according to an embodiment of the present invention;
FIG. 2 is a flowchart of another heart rate detection method according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a heart rate detection device according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
[ detailed description ] of the invention
For a better understanding of the technical solutions of the present specification, the following detailed description of the embodiments of the present invention refers to the accompanying drawings.
It should be understood that the described embodiments are only some, but not all, of the embodiments of the present description. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are intended to be within the scope of the present invention based on the embodiments herein.
The terminology used in the embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the description. As used in this application and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
In the embodiment of the invention, the outlier factor detection is carried out on the heartbeat data before clustering, and the outlier factor is removed, so that the robustness of the detection process is improved, and the accuracy of the heart rate detection result is further improved.
Fig. 1 is a flowchart of a heart rate detection method according to an embodiment of the present invention. As shown in fig. 1, the method includes:
step 101, obtaining a plurality of groups of heartbeat sampling data. The inertial measurement unit (Inertial Measurement Unit, IMU) is arranged in the VR interaction device such as the VR interaction handle, so that IMU data acquired by the IMU can be acquired as heartbeat sampling data. Specifically, an IMU data acquisition window may be employed to obtain multiple sets of heartbeat sample data. Since the typical value of heart rate is 40 to 220 times per minute. The sampling rate of the IMU is 2 times of the lowest heart rate, the minimum sampling times per minute of the IMU is 440 times calculated by the highest heart rate, and the corresponding sampling rate is not lower than 7.333Hz. Thus, the sampling rate may be set to 50Hz to 500Hz. Preferably, the sampling rate may be set to 500Hz. To achieve stable detection performance, the window of the IMU data acquisition window requires more than 12 seconds. However, for the user, when the user test time is longer than 18 seconds, the user may feel tired, so that the user experience is reduced, and thus the size of the IMU data acquisition window may be set to 15 seconds. The IMU data frame for each window is 200 x 15, i.e., 3000 sets of data.
And 102, decomposing a plurality of groups of heartbeat sampling data by adopting a preset sparse dictionary to obtain sparse representation of the plurality of groups of heartbeat sampling data. The preset sparse dictionary may be a Gabor overcomplete dictionary. The Gabor function is a sine wave modulated Gaussian window function, and has optimal time-frequency concentration according to the Heisenberg inaccuracy measurement principle. The function is specifically as follows:wherein u, s, ω and +.>The position, scale frequency and phase characteristics of the atoms are shown, respectively. The method for establishing the Gabor overcomplete dictionary is discretization of the four parameters. Wherein s= (1/2) j Wherein j is an integer, j is greater than or equal to 0, and j is less than 8. u= (1/2) k K is an integer, k is more than or equal to-8, and k is less than 8. Omega= (1/2) m Wherein m is an integer, m is more than or equal to-8, and m is less than 8./>Wherein n is an integer, n is more than or equal to-12, and n is less than 12. The atomic number of the Gabor overcomplete dictionary thus obtained is 8×16×16×24=49152. The dimension of Gabor overcomplete dictionary is 3000 x 39152. From the following componentsThe method can improve the detection capability of the low signal-to-noise ratio signals, improve the sensitivity of the dictionary and reduce the noise in the dictionary. After the Gabor overcomplete dictionary is completed, a matching tracking method can be adopted to solve a plurality of groups of heartbeat sampling data, so that corresponding sparse representation is obtained.
In some embodiments, the specific steps of solving to obtain sparse representations of heartbeat sample data using a matching pursuit method are shown in fig. 2.
Step S1021, respectively calculating the inner products of a plurality of groups of heartbeat sampling data and the inner products of all atoms in a preset sparse dictionary.
In step S1022, the atom with the largest inner product in the sparse dictionary is determined as the atom that best matches the heartbeat sampling data.
Step S1023, subtracting the best matching atoms from the plurality of sets of heartbeat sampling data to split the plurality of sets of heartbeat sampling data into an atomic portion and a residual portion.
Step S1024, continuing to split the residual part until the size of the residual part is lower than a preset residual threshold or the splitting times are greater than a preset times threshold. For example, split to a residual portion size below 0.001, or split times greater than 100.
Step S1025, determining sparse representation of multiple groups of heartbeat sampling data according to the split result. The linear combination (atomic part and residual part) of the atoms of the heartbeat sampling data obtained in the plurality of steps is sparse representation of the heartbeat sampling data.
In some embodiments, the low frequency noise in the heartbeat sample data may also be removed prior to decomposing the heartbeat sample data. Specifically, preprocessing is performed on multiple groups of heartbeat sampling data to obtain multiple groups of heartbeat sampling data after noise is removed. Specifically, the preprocessing can be performed by means of differential and barrel filtering. When the displacement in IMU data (heartbeat sampling data) is typically a low frequency characteristic, the heart rate signal is a high frequency characteristic. The effect of low frequency disturbances can thus be removed by differentiation, while the effect of gravitational acceleration can also be reduced. The formula δimu=imu can be used i -IMU i-1 To perform differential processing. Wherein the IMU i -IMU i-1 Respectively, i-1 th set of sampling data and i-1 th set of sampling data in IMU data. And then carrying out band-pass filtering on the data after the difference so as to extract heartbeat sampling data with the frequency in the range of 0.5Hz to 5 Hz. And then decomposing the plurality of groups of heartbeat sampling data after noise removal by adopting a preset sparse dictionary to obtain sparse representation of the plurality of groups of heartbeat sampling data.
And step 103, determining the part of the sparse representation larger than a preset threshold value as a heartbeat time sequence. The sequence number of the point greater than the preset L2 norm (preset threshold) may be first determined to form the heartbeat time sequence. And if no point larger than the preset L2 norm exists in the sparse representation, determining that no heartbeat signal exists in the heartbeat sampling data.
Step 104, performing outlier factor detection on the heartbeat time sequence to obtain a plurality of outlier factors in the heartbeat time sequence. The corresponding heartbeat moment distance matrix can be obtained according to the heartbeat moment sequence. Specifically, the heartbeat time sequence may be set to be t1 to tn, where the first action heartbeat time sequence of the heartbeat time distance matrix is subtracted by t1, the second action heartbeat time of the heartbeat time distance matrix is subtracted by t2, and so on, so as to obtain the heartbeat time distance matrix of n rows. And the elements on the main diagonal of the distance matrix at the time of the heartbeat are 0. And then, carrying out linear fitting processing on each row of data of the heartbeat moment distance matrix to obtain fitting coefficients corresponding to each row. Wherein the fitting coefficient is divided into a first order coefficient a and a constant term b. A plurality of outlier rows is then determined based on the fitting coefficients. Here, since the coefficient b of the normal heartbeat time sequence should be close to 0 wirelessly, the larger the absolute value of the coefficient b, the more likely it is an outlier factor. The absolute values of the coefficients b of the respective rows may be arranged in order from large to small. The first 20% is then determined to be the outlier row. And determining the outlier row as an outlier factor.
Step 105, removing the plurality of outlier factors to obtain the cleaned heartbeat time sequence.
And 106, clustering the cleaned heartbeat time sequence. The mean value clustering method is sensitive to the outlier, and a better clustering effect can be obtained for the heartbeat time sequence after the outlier is removed by using the mean value clustering method. Therefore, the average value clustering algorithm can be adopted to cluster the cleaned heartbeat time sequence. Specifically, a plurality of random points in the cleaned heartbeat time sequence are taken as the center, a corresponding circular sliding window is made, and the radius of the circular sliding window is determined according to the standard deviation of the cleaned heartbeat time sequence. Alternatively, the standard deviation of the heartbeat sequence may be calculated and the radius is 3 times the standard deviation. The mean value in each circular sliding window is then calculated. And taking each average value as a new center, and moving each circular sliding window towards the density direction of the data in the circular window until the data density in the circular sliding window is not increased any more. Wherein, there may be a phenomenon that a plurality of circular sliding windows overlap, at this time, the number of data points in each circular sliding window may be determined, and the circular sliding window containing the most data points is reserved.
Step 107, determining a heart rate detection result according to the clustering result. The above steps obtain a plurality of non-overlapping circular sliding windows, at this time, the centroid of the data in each circular sliding window can be calculated, and the heart rate detection result is determined according to the execution.
Corresponding to the above heart rate detection method, an embodiment of the present invention provides a heart rate detection device, as shown in fig. 3, where the heart rate detection device includes an acquisition module 301 and a processing module 302.
The acquiring module 301 is configured to acquire multiple sets of heartbeat sampling data.
The processing module 302 is configured to decompose the plurality of groups of heartbeat sampling data by using a preset sparse dictionary, so as to obtain sparse representation of the plurality of groups of heartbeat sampling data.
The processing module 302 is further configured to determine a portion in the sparse representation greater than a preset threshold as a heartbeat time sequence.
The processing module 302 is further configured to perform outlier factor detection on the heartbeat time sequence, so as to obtain a plurality of outliers factors in the heartbeat time sequence.
The processing module 302 is further configured to exclude the plurality of outliers to obtain a cleaned heartbeat time sequence.
The processing module 302 is further configured to cluster the cleaned heartbeat time sequence.
The processing module 302 is further configured to determine a heart rate detection result according to the clustering result.
The mobile terminal provided by the embodiment shown in fig. 3 may be used to implement the technical solutions of the method embodiments shown in fig. 1-2 of the present specification, and the implementation principles and technical effects may be further referred to in the related descriptions of the method embodiments.
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, where, as shown in fig. 4, the electronic device may include at least one processor and at least one memory communicatively connected to the processor, where: the memory stores program instructions executable by the processor, which invokes the program instructions to perform the heart rate detection method provided in the embodiments shown in fig. 1-2 of the present specification.
As shown in fig. 4, the electronic device is in the form of a general purpose computing device. Components of an electronic device may include, but are not limited to: one or more processors 410, a communication interface 420, and a memory 430, a communication bus 440 that connects the various system components (including the memory 430, the communication interface 420, and the processing unit 410).
The communication bus 440 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include industry Standard architecture (Industry Standard Architecture; hereinafter ISA) bus, micro channel architecture (Micro Channel Architecture; hereinafter MAC) bus, enhanced ISA bus, video electronics standards Association (Video Electronics Standards Association; hereinafter VESA) local bus, and peripheral component interconnect (Peripheral Component Interconnection; hereinafter PCI) bus.
Electronic devices typically include a variety of computer system readable media. Such media can be any available media that can be accessed by the electronic device and includes both volatile and nonvolatile media, removable and non-removable media.
Memory 430 may include computer system readable media in the form of volatile memory, such as random access memory (Random Access Memory; hereinafter: RAM) and/or cache memory. The electronic device may further include other removable/non-removable, volatile/nonvolatile computer system storage media. Memory 430 may include at least one program product having a set (e.g., at least one) of program modules configured to carry out the functions of the embodiments of the present description.
A program/utility having a set (at least one) of program modules may be stored in the memory 430, such program modules including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment. Program modules typically carry out the functions and/or methods of the embodiments described herein.
The processor 410 executes programs stored in the memory 430 to perform various functional applications and data processing, such as implementing the heart rate detection method provided in the embodiments shown in fig. 1-2 of the present specification.
Embodiments of the present disclosure provide a computer readable storage medium storing computer instructions that cause a computer to perform the heart rate detection method provided by the embodiments of fig. 1-2 of the present disclosure.
Any combination of one or more computer readable media may be utilized as the above-described computer readable storage media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a Read-Only Memory (ROM), an erasable programmable Read-Only Memory (Erasable Programmable Read Only Memory; EPROM) or flash Memory, an optical fiber, a portable compact disc Read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The foregoing describes specific embodiments of the present disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present specification. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present specification, the meaning of "plurality" means at least two, for example, two, three, etc., unless explicitly defined otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and additional implementations are included within the scope of the preferred embodiment of the present specification in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present specification.
Depending on the context, the word "if" as used herein may be interpreted as "at … …" or "at … …" or "in response to a determination" or "in response to detection". Similarly, the phrase "if determined" or "if detected (stated condition or event)" may be interpreted as "when determined" or "in response to determination" or "when detected (stated condition or event)" or "in response to detection (stated condition or event), depending on the context.
It should be noted that the devices according to the embodiments of the present disclosure may include, but are not limited to, a personal Computer (Personal Computer; hereinafter referred to as a PC), a personal digital assistant (Personal Digital Assistant; hereinafter referred to as a PDA), a wireless handheld device, a Tablet Computer (Tablet Computer), a mobile phone, an MP3 display, an MP4 display, and the like.
In the several embodiments provided in this specification, it should be understood that the disclosed systems, apparatuses, and methods may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the elements is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple elements or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
In addition, each functional unit in each embodiment of the present specification may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in hardware plus software functional units.
The integrated units implemented in the form of software functional units described above may be stored in a computer readable storage medium. The software functional unit is stored in a storage medium, and includes several instructions for causing a computer device (which may be a personal computer, a connector, or a network device, etc.) or a Processor (Processor) to perform part of the steps of the methods described in the embodiments of the present specification. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a Read-Only Memory (hereinafter referred to as ROM), a random access Memory (Random Access Memory) and various media capable of storing program codes such as a magnetic disk or an optical disk.
The foregoing description of the preferred embodiments is provided for the purpose of illustration only, and is not intended to limit the scope of the disclosure, since any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the disclosure are intended to be included within the scope of the disclosure.
Claims (10)
1. A heart rate detection method, comprising:
acquiring a plurality of groups of heartbeat sampling data;
decomposing the plurality of groups of heartbeat sampling data by adopting a preset sparse dictionary to obtain sparse representation of the plurality of groups of heartbeat sampling data;
determining a part of the sparse representation, which is larger than a preset threshold value, as a heartbeat time sequence;
performing outlier factor detection on the heartbeat time sequence to obtain a plurality of outlier factors in the heartbeat time sequence;
removing the outlier factors to obtain a cleaned heartbeat time sequence;
clustering the cleaned heartbeat time sequence;
and determining a heart rate detection result according to the clustering result.
2. The method of claim 1, wherein decomposing the plurality of sets of heartbeat sample data using a predetermined sparse dictionary to obtain a sparse representation of the plurality of sets of heartbeat sample data comprises:
respectively calculating the inner products of the plurality of groups of heartbeat sampling data and the inner products of all atoms in a preset sparse dictionary;
determining the atom with the largest inner product in the sparse dictionary as the atom which is most matched with the heartbeat sampling data;
subtracting the best matching atoms from the plurality of groups of heartbeat sampling data to split the plurality of groups of heartbeat sampling data into an atomic part and a residual part;
splitting the residual part continuously until the size of the residual part is lower than a preset residual threshold value or the splitting frequency is greater than a preset frequency threshold value;
and determining sparse representation of the plurality of groups of heartbeat sampling data according to the splitting result.
3. The method of claim 1, wherein performing outlier factor detection on the heartbeat time sequence to obtain a plurality of outlier factors in the heartbeat time sequence comprises:
obtaining a corresponding heartbeat moment distance matrix according to the heartbeat moment sequence;
performing linear fitting processing on each row of data of the heartbeat moment distance matrix to obtain fitting coefficients corresponding to each row;
determining a plurality of outlier rows according to the fitting coefficients;
determining the outlier row as the outlier factor.
4. The method of claim 1, wherein clustering the cleaned sequence of heartbeat moments comprises:
and clustering the cleaned heartbeat time sequence by adopting a mean value clustering algorithm.
5. The method of claim 4, wherein clustering the cleaned heartbeat time series using a mean value clustering algorithm comprises:
taking a plurality of random points in the cleaned heartbeat time sequence as centers, making a corresponding circular sliding window, wherein the radius of the circular sliding window is determined according to the standard deviation of the cleaned heartbeat time sequence;
calculating the average value in each circular sliding window;
and taking each average value as a new center, and moving each circular sliding window towards the density direction of the data in the circular window until the data density in the circular sliding window is not increased any more.
6. The method of claim 5, wherein determining heart rate detection results based on the clustering results comprises:
calculating the mass center of the data in each circular sliding window;
and determining heart rate detection results according to the centroid.
7. The method of claim 1, wherein prior to decomposing the plurality of sets of heartbeat sample data using a predetermined sparse dictionary to obtain a sparse representation of the plurality of sets of heartbeat sample data, the method further comprises:
preprocessing the plurality of groups of heartbeat sampling data to obtain a plurality of groups of heartbeat sampling data after noise is removed;
and decomposing the plurality of groups of heartbeat sampling data after noise removal by adopting a preset sparse dictionary to obtain sparse representation of the plurality of groups of heartbeat sampling data.
8. A heart rate detection apparatus, comprising:
the acquisition module is used for acquiring a plurality of groups of heartbeat sampling data;
the processing module is used for decomposing the plurality of groups of heartbeat sampling data by adopting a preset sparse dictionary to obtain sparse representation of the plurality of groups of heartbeat sampling data;
the processing module is further configured to determine a portion of the sparse representation greater than a preset threshold as a heartbeat time sequence;
the processing module is further used for detecting outlier factors of the heartbeat time sequence to obtain a plurality of outlier factors in the heartbeat time sequence;
the processing module is further used for removing the outlier factors to obtain a cleaned heartbeat time sequence;
the processing module is also used for clustering the cleaned heartbeat time sequence;
the processing module is further used for determining a heart rate detection result according to the clustering result.
9. An electronic device, comprising:
at least one processor; and
at least one memory communicatively coupled to the processor, wherein:
the memory stores program instructions executable by the processor, the processor invoking the program instructions to perform the method of any of claims 1-7.
10. A computer readable storage medium storing computer instructions for causing a computer to perform the method of any one of claims 1 to 7.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117426761A (en) * | 2023-12-21 | 2024-01-23 | 深圳市景新浩科技有限公司 | Control method, device and equipment for self-adaptive pressure release speed of electronic sphygmomanometer |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117426761A (en) * | 2023-12-21 | 2024-01-23 | 深圳市景新浩科技有限公司 | Control method, device and equipment for self-adaptive pressure release speed of electronic sphygmomanometer |
| CN117426761B (en) * | 2023-12-21 | 2024-03-22 | 深圳市景新浩科技有限公司 | Control method, device and equipment for self-adaptive pressure release speed of electronic sphygmomanometer |
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