Heart rate detection method, device, storage medium and electronic device

Abstract

The present invention discloses a heart rate detection method, device, storage medium and electronic device based on pressure signals. The method first generates a pressure data set to be measured by acquiring in real time the pressure values detected by a pressure sensor in a seat cushion within a first preset time period, and calculates the degree of dispersion value of the pressure data set to be measured. Then, the degree of dispersion value is compared with a body movement threshold and a sitting still threshold to determine whether the sitting posture state type of the current user is a sitting still state or a body movement state. When it is determined that the sitting posture state type of the current user is a sitting still state type, the fluctuation frequency of the pressure values within the first preset time period is calculated, and the real-time heart rate of the user is calculated based on the fluctuation frequency. Through the above solution, the real-time heart rate of the user can be collected without the user's awareness, improving the user's sensory experience during the heart rate collection process.

Description

Technical Field

[0001] The present invention relates to the field of heart rate detection, and in particular to a heart rate detection method, device, storage medium, and electronic device based on pressure signals. Background Art

[0002] With the accelerating pace of life, more and more office workers are under greater work pressure and mental stress. Prolonged sitting at the desk causes many health problems for office workers. Therefore, it is particularly important to develop a health detection product for office workers to monitor the health status of users in real time.

[0003] Heart rate is an important parameter in human vital signs, and detecting heart rate is an essential indicator in detecting human health. However, there are some defects in so many heart rate monitoring devices on the market. For example, traditional medical electrocardiogram detection devices require professional physicians to operate with professional electrocardiogram detection devices, and multiple sensor probes need to be installed on the body of the person being detected, which is not suitable for use in home and office scenarios.

[0004] For another example, some optoelectronic heart rate detection devices need to be worn on the wrist or fingertip of the user for detection. For users who do not have the habit of wearing a wristwatch, it is easy to feel a foreign body sensation; this optoelectronic heart rate detector with the fingertip as the action part is even more unable to detect the heart rate at any time and anywhere.

[0005] Therefore, under the premise of this market demand, it is particularly important to develop a device that can collect and detect the heart rate of users without making the users feel a foreign body sensation. Summary of the Invention

[0006] Therefore, a technical solution for heart rate detection based on pressure signals is needed to solve problems such as limited application scenarios and poor user experience of existing heart rate detection devices.

[0007] To achieve the above object, in a first aspect, the present invention provides a heart rate detection method based on pressure signals, including the following steps:

[0008] S1: Obtain in real time the pressure values detected by the pressure sensor in the seat cushion within a first preset time period, generate a to-be-detected pressure data set, and calculate the dispersion value of the to-be-detected pressure data set;

[0009] S2: Compare the dispersion value with a body movement threshold and a sitting still threshold to determine whether the sitting posture state type of the current user is a sitting still state or a body movement state;

[0010] S3: When it is determined that the sitting posture state type of the current user is a sitting still state type, calculate the fluctuation frequency of the pressure values within the first preset time period, and calculate the real-time heart rate of the user according to the fluctuation frequency.

[0011] As an alternative embodiment, step S1 further includes: calculating the average pressure of the pressure dataset to be measured, determining whether the average pressure is greater than a preset human pressure threshold, and if so, performing step S2.

[0012] As an alternative embodiment, the method includes:

[0013] Setting a breathing frequency range, and calculating the change in pressure value caused by breathing in the pressure dataset to be measured according to the breathing frequency range;

[0014] Step S3 further includes a breathing filtering step for removing the change in pressure value caused by breathing in the pressure dataset to be measured.

[0015] As an alternative embodiment, the value range of the body movement threshold is 1%-80% of the change rate relative to the average pressure; the sitting still threshold is 0.01%-0.99% of the change rate relative to the average pressure.

[0016] As an alternative embodiment, the number of the pressure sensors is multiple, and the pressure sensors are arranged at intervals on the seat cushion;

[0017] Step S1 includes:

[0018] Obtaining the pressure signal values collected by multiple pressure sensors within a first preset time period, obtaining multiple pressure datasets to be measured, and calculating the average pressure corresponding to the multiple pressure datasets to be measured;

[0019] Step S2 includes:

[0020] Determining whether at least one of the calculated average pressures is greater than a preset human pressure threshold.

[0021] As an alternative embodiment, step S3 further includes:

[0022] Performing normalization processing on the pressure values within a first preset time period; the normalization processing includes: mapping the numerical magnitudes of all data within a preset numerical range;

[0023] Calculating the fluctuation frequency based on the data after normalization processing.

[0024] As an alternative embodiment, the real-time heart rate of the user is calculated based on the following steps:

[0025] S51: The dataset to be calculated is represented as a vector P = [P1, P2,..., Pn].

[0026] S52: Calculating the differential vector DiffP corresponding to each item of data in P:

[0027] DiffP(i) = P(i + 1) - P(i), where i ∈ 1, 2, …, n - 1;

[0028] S53: Perform a sign operation on the difference vector to obtain a Trend vector:

[0029] If DiffP(i) is greater than 0, then the value of Trend(i) is taken as 1;

[0030] If DiffP(i) is less than 0, then the value of Trend(i) is taken as -1;

[0031] If DiffP(i) is equal to 0, then the value of Trend(i) is taken as 0;

[0032] S54: Traverse the Trend vector and perform the following operations:

[0033] If Trend(i) = 0 and Trend(i + 1) ≥ 0, then assign Trend(i) the value of 1;

[0034] If Trend(i) = 0 and Trend(i + 1) < 0, then assign Trend(i) the value of -1;

[0035] S55: Calculate the difference vector R = Diff(Trend) of the Trend vector obtained in step S54

[0036] where Diff(Trend) = Trend(i + 1) - Trend(i);

[0037] i ∈ 1, 2, …, n - 1;

[0038] S56: Traverse the obtained difference vector R. If R(i) = -2, then i + 1 is a peak position of the projection vector P, and the corresponding peak is P(i + 1); if R(i) = 2, then i + 1 is a trough position of the projection vector P, and the corresponding trough is P(i + 1);

[0039] S57: Subtract all the trough values adjacent to the peaks obtained in step S56 from the peaks to obtain a floating value. If the value of the floating value is greater than 3, then save the time index of the peak position to obtain a peak index vector Index(j);

[0040] S58: Let the Index interval size be the set T, calculate the average value Tavg of all the data in the set T, and calculate the user's real-time heart rate HeartRate based on the following formula:

[0041] HeartRate = 1 / (Tavg) * 60.

[0042] As an alternative embodiment, the value range of the first preset time period is 5 to 20 seconds, the sampling period is 0.01 second, and the number of pressure values in the first pressure data set is 500 - 2000.

[0043] In a second aspect, the present invention further provides a storage medium storing a computer program, which when executed by a processor, implements the method steps of the first aspect of the present invention.

[0044] In a third aspect, the present invention further provides an electronic device, which includes a storage medium and a processor. The storage medium is the storage medium of the second aspect of the present invention, and the processor is configured to execute the computer program stored in the storage medium to implement the method steps of the first aspect of the present invention.

[0045] In a fourth aspect, the present invention further provides a heart rate detection device based on a pressure signal, which includes a body, a pressure sensor, a storage medium, and a processor; the body includes a seat cushion; the pressure sensor is disposed on the seat cushion; the storage medium is the storage medium of the second aspect of the present invention; and the processor is configured to receive the signal collected by the pressure sensor and call the computer program in the storage medium to process the signal collected by the pressure sensor.

[0046] Different from the prior art, the above technical solution provides a heart rate detection method, device, storage medium, and electronic device based on a pressure signal. The method first generates a pressure data set to be measured by acquiring in real time the pressure values detected by the pressure sensor in the seat cushion within a first preset time period, and calculates the dispersion value of the pressure data set to be measured. Then, the dispersion value is compared with a body movement threshold and a sitting still threshold to determine whether the sitting posture state type of the current user is a sitting still state or a body movement state. When it is determined that the sitting posture state type of the current user is a sitting still state type, the fluctuation frequency of the pressure values within the first preset time period is calculated, and the real-time heart rate of the user is calculated based on the fluctuation frequency. Through the above solution, the real-time heart rate of the user can be collected without the user's awareness, improving the user's sensory experience during the heart rate collection process. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a flowchart of a heart rate detection method based on a pressure signal according to an embodiment of the present invention;

[0048] Figure 2 It is a flowchart of a heart rate detection method based on a pressure signal according to another embodiment of the present invention;

[0049] Figure 3 It is a flowchart of a heart rate detection method based on a pressure signal according to still another embodiment of the present invention;

[0050] Figure 4Schematic diagram of a module of an electronic device according to an embodiment of the present invention;

[0051] Figure 5 Schematic diagram of a circuit module of a heart rate detection device based on pressure signals according to an embodiment of the present invention;

[0052] Figure 6 Flowchart of sitting posture state type judgment and heart rate calculation according to an embodiment of the present invention;

[0053] Figure 7 Waveform diagram corresponding to a pressure data set to be measured according to an embodiment of the present invention;

[0054] Figure 8 Flowchart of sitting posture state type judgment and heart rate calculation according to another embodiment of the present invention;

[0055] Figure 9 Flowchart of sitting posture state type judgment according to an embodiment of the present invention;

[0056] Figure 10 Flowchart of real-time heart rate calculation of a user according to an embodiment of the present invention;

[0057] Figure 11 Waveform diagram of the effect of calculating heart rate from a pressure data set according to an embodiment of the present invention;

[0058] Explanation of reference numerals:

[0059] 10. Electronic device;

[0060] 101. Processor;

[0061] 102. Storage medium. Detailed implementation manners

[0062] To describe in detail the technical content, structural features, achieved objectives and effects of the technical solution, the following will be described in detail in conjunction with specific embodiments and with reference to the accompanying drawings.

[0063] To describe in detail the possible application scenarios, technical principles, specific implementable solutions, achievable objectives and effects of the present application, the following will be described in detail in conjunction with the listed specific embodiments and with reference to the accompanying drawings. The embodiments described herein are only used to more clearly illustrate the technical solutions of the present application, and therefore are only examples and cannot be used to limit the protection scope of the present application.

[0064] References to "embodiments" in this document mean that the specific features, structures, or characteristics described in connection with the embodiments can be included in at least one embodiment of the present application. The term "embodiment" appearing at various positions in the specification does not necessarily refer to the same embodiment, nor does it particularly limit its independence or relevance to other embodiments. In principle, in the present application, as long as there is no technical contradiction or conflict, the technical features mentioned in each embodiment can be combined in any way to form corresponding implementable technical solutions.

[0065] Unless otherwise defined, the meanings of the technical terms used in this document are the same as those commonly understood by those skilled in the technical field to which this application belongs; the use of the relevant terms in this document is only for describing specific embodiments and is not intended to limit this application.

[0066] In the description of this application, the phrase "and / or" is an expression used to describe the logical relationship between objects, indicating that three relationships can exist. For example, A and / or B means: there is A, there is B, and there is both A and B at the same time. In addition, the character " / " in this document generally represents an "or" logical relationship between the associated objects before and after.

[0067] In this application, terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual quantitative, primary-secondary, or sequential relationships between these entities or operations.

[0068] Without further limitation, in this application, the use of "including", "comprising", "having" or other similar expressions in a statement is intended to cover non-exclusive inclusion. These expressions do not exclude the possibility that there may be additional elements in the process, method, or product that includes the elements, such that the process, method, or product that includes a series of elements may not only include those defined elements, but also include other elements not explicitly listed, or elements inherent to such process, method, or product.

[0069] Similar to the understanding in the "Examination Guidelines", in this application, expressions such as "greater than", "less than", "exceeding", etc. are understood not to include the number itself; expressions such as "above", "below", "within", etc. are understood to include the number itself. In addition, in the description of the embodiments of this application, the meaning of "multiple" is two or more (including two), and similar expressions related to "many", such as "multiple groups", "multiple times", etc., are also understood in this way, unless otherwise specifically defined.

[0070] As Figure 1 shown, it is a flowchart of a heart rate detection method based on pressure signals according to an embodiment of the present invention. The method includes the following steps:

[0071] S1: Obtain in real time the pressure values detected by the pressure sensors in the seat cushion within the first preset time period, generate a to-be-detected pressure data set, and calculate the dispersion value of this to-be-detected pressure data set;

[0072] S2: Compare the dispersion value with the body movement threshold and the sitting still threshold to determine whether the sitting posture state type of the current user is a sitting still state or a body movement state;

[0073] S3: When it is determined that the sitting posture state type of the current user is a sitting still state type, calculate the fluctuation frequency of the pressure values within the first preset time period, and calculate the real-time heart rate of the user based on this fluctuation frequency.

[0074] In the embodiment of the present application, the pressure signals detected by the pressure sensors are collected with the first preset time period as the sampling time. Preferably, the value range of the first preset time period is 5 to 20 seconds, the sampling period is 0.01 second, and the number of data in the to-be-detected pressure data set is 500 - 2000. Assuming a preset time period is 10s, then 1000 pressure signal values collected by a certain pressure sensor within 10s are obtained, and the sampling waveform diagram is as Figure 7 shown.

[0075] Preferably, the dispersion value is variance or standard deviation. The value range of the body movement threshold is 1% - 80% of the change rate relative to the average pressure value; the sitting still threshold is 0.01% - 0.99% of the change rate relative to the average pressure value. The body movement state type means that although the user's buttocks are on the seat cushion of the chair, other parts of the body are still in a moving state within a unit time, which is specifically reflected in the signals collected by the pressure sensors as the change of the pressure signal values within a unit time is relatively intense, that is, the dispersion degree is large. The sitting still state type means that when the user's buttocks are on the seat cushion of the chair, other parts of the body also basically remain still within a unit time, which is specifically reflected in the signals collected by the pressure sensors as the change of the pressure signal values within a unit time is relatively gentle, that is, the dispersion degree is small.

[0076] Through the method designed in the present application, first, the pressure values detected by the pressure sensors in the seat cushion within the first preset time period are obtained in real time, a to-be-detected pressure data set is generated, and the dispersion value of this to-be-detected pressure data set is calculated. Then, the dispersion value is compared with the body movement threshold and the sitting still threshold to determine whether the sitting posture state type of the current user is a sitting still state or a body movement state. When it is determined that the sitting posture state type of the current user is a sitting still state type, the fluctuation frequency of the pressure values within the first preset time period is calculated, and the real-time heart rate of the user is calculated based on this fluctuation frequency. Through the above solution, the real-time heart rate of the user can be collected without the user's awareness, improving the user's sensory experience during the heart rate collection process.

[0077] In some embodiments, step S1 further includes: calculating the average pressure of the pressure dataset to be measured, and determining whether the average pressure is greater than a preset human pressure threshold. If so, step S2 is executed.

[0078] Preferably, the pressure sensor continuously collects pressure signals in real time. Taking the application to a seat as an example, the pressure sensor can be arranged under the seat cushion. If a user sits on the seat, the pressure signal detected by the pressure sensor will increase suddenly. Specifically, the average value of the pressure signals collected per unit time is greater than the preset human pressure threshold. Therefore, it can be determined whether the current user is seated by determining whether the average pressure is greater than the preset human pressure threshold.

[0079] In some embodiments, the method includes: setting a breathing frequency range, and calculating the change in pressure value caused by breathing in the pressure dataset to be measured according to the breathing frequency range; step S3 further includes a breathing filtering step for removing the change in pressure value caused by breathing in the pressure dataset to be measured. By introducing the breathing filtering step, the influence caused by the change in pressure value caused by breathing in the pressure dataset to be measured can be effectively eliminated, and the accuracy of the real-time calculated heart rate can be effectively improved.

[0080] The acquisition of the user's real-time heart rate is preferably calculated based on the signals collected by the pressure sensor when the user is in a sitting still state, because the heart rate is relatively stable when the user is in a sitting still state, and the heart rate value calculated based on the pressure signal data collected at this time is more real and accurate. Therefore, the present invention first screens out the pressure data when the human body is seated (i.e., the data greater than the preset human pressure threshold), then compares the screened data with the body movement threshold and the sitting still threshold, screens out the data with the sitting state type of sitting still state type, and then calculates the user's real-time heart rate based on the data in the sitting still state type, which can make the calculation of the user's real-time heart rate more accurate.

[0081] In some embodiments, as Figure 2 shown, step S3 further includes: first entering step S301 to perform normalization processing on the pressure values within a first preset time period; the normalization processing includes: mapping the numerical magnitudes of all data within a preset numerical range; then entering step S302 to calculate the fluctuation frequency based on the data after the normalization processing. The preset numerical range can be set according to actual needs. For example, the preset numerical range can be set as the interval [-4, 4]. By performing normalization processing on the screened data, the amount of calculation in subsequent processing can be effectively reduced.

[0082] In some other embodiments, step S3 further includes: performing Butterworth filtering on the fluctuation frequency of the pressure values in the first preset time period to filter out low-frequency signals with a frequency less than the preset frequency; then performing moving average filtering on the data after Butterworth filtering; then performing normalization processing on the data after moving average filtering; and then calculating the user's heart rate based on the normalized data.

[0083] Preferably, the magnitude of the preset frequency can be set according to actual needs, and preferably it can be set to 0.5 Hz. After moving average filtering, the data spikes can be effectively reduced, and the overall smoothness of the data can be increased, which is convenient for subsequent calculations.

[0084] In some embodiments, the number of the pressure sensors is multiple, and the pressure sensors are arranged at intervals on the seat cushion; step S1 includes: obtaining the pressure signal values collected by the multiple pressure sensors in the first preset time period to obtain multiple pressure data sets to be measured, and calculating the pressure average values corresponding to the multiple pressure data sets to be measured; step S2 includes: determining whether at least one of the calculated multiple pressure average values is greater than a preset human pressure threshold.

[0085] Taking the example of detecting the user's real-time heart rate through the seat, a seat cushion can be set on the seat first. The seat cushion preferably can be made of a flexible material, and then multiple pressure sensors are arranged under the seat cushion. When a person sits on the seat, the multiple pressure sensors will synchronously collect the suddenly increased pressure signals. When the average value of the pressure signal values collected by any one of the pressure sensors in the first preset time period is greater than the preset human pressure threshold, it can be determined that there is a person sitting on the seat at this time. The multiple pressure sensors make the judgment of whether a person is sitting on the seat more accurate and avoid the possibility of misjudgment.

[0086] As Figure 3 shown, in some embodiments, the user's real-time heart rate is calculated based on the following steps:

[0087] S51: The data set to be calculated is represented as a vector P = [P1, P2,..., Pn];

[0088] S52: Calculate the difference vector DiffP corresponding to each item of data in P:

[0089] DiffP(i) = P(i + 1) - P(i), where i ∈ 1, 2,..., n - 1;

[0090] S53: Perform a sign operation on the difference vector to obtain the Trend vector:

[0091] If DiffP(i) is greater than 0, then the value of Trend(i) is taken as 1;

[0092] If DiffP(i) is less than 0, then the value of Trend(i) is taken as -1;

[0093] If DiffP(i) is equal to 0, then the value of Trend(i) is taken as 0;

[0094] S54: Traverse the Trend vector and perform the following operations:

[0095] If Trend(i) = 0 and Trend(i + 1) ≥ 0, then assign Trend(i) the value of 1;

[0096] If Trend(i) = 0 and Trend(i + 1) < 0, then assign Trend(i) the value of -1;

[0097] S55: Calculate the difference vector R = Diff(Trend) of the Trend vector obtained in step S54

[0098] where Diff(Trend) = Trend(i + 1) - Trend(i);

[0099] i ∈ 1, 2, …, n - 1;

[0100] S56: Traverse the obtained difference vector R. If R(i) = -2, then i + 1 is a peak position of the projection vector P, and the corresponding peak is P(i + 1); if R(i) = 2, then i + 1 is a trough position of the projection vector P, and the corresponding trough is P(i + 1);

[0101] S57: Subtract all the peaks obtained in step S56 from the trough values adjacent to the peaks to obtain floating values. If the value of the floating value is greater than 3, then save the time index of the peak position to obtain the peak index vector Index(j);

[0102] S58: Let the Index interval size be the set T, calculate the average value Tavg of all the data in the set T, and calculate the user's real-time heart rate HeartRate based on the following formula:

[0103] HeartRate = 1 / (Tavg) * 60.

[0104] Through the above solution, it is possible to realize the real-time calculation of the heart rate of the current user based on the user's pressure signal, and the calculated heart rate can be displayed in real time on the display unit. Since the acquisition of the pressure signal does not require the user to wear other specific devices additionally, it can effectively reduce the foreign body sensation when the user detects the heart rate, making the detection process more comfortable and improving the user experience.

[0105] In a second aspect, the present invention further provides a storage medium storing a computer program, which when executed by a processor implements the method steps of the first aspect of the present invention.

[0106] As Figure 4 shown, in a third aspect, the present invention further provides an electronic device 10, including a processor 101 and a storage medium 102, where the storage medium 102 is the storage medium of the second aspect; the processor 101 is configured to execute the computer program stored in the storage medium 102 to implement the method steps of the first aspect.

[0107] In this embodiment, the electronic device is a computer device, including but not limited to: personal computers, servers, general-purpose computers, special-purpose computers, network devices, embedded devices, programmable devices, intelligent mobile terminals, smart home devices, wearable intelligent devices, in-vehicle intelligent devices, etc. The storage medium includes but not limited to: RAM, ROM, magnetic disks, magnetic tapes, optical discs, flash memories, USB flash drives, external hard drives, memory cards, memory sticks, network server storage, network cloud storage, etc. The processor includes but not limited to CPU (Central Processing Unit), GPU (Graphics Processing Unit), MCU (Microprocessor), etc.

[0108] In a fourth aspect, the present invention further provides a heart rate detection device based on pressure signals, including a body, a pressure sensor, a storage medium, and a processor; the body includes a seat cushion; the pressure sensor is disposed below the seat cushion; the storage medium is the storage medium of the second aspect of the present invention; the processor is configured to receive the signals collected by the pressure sensor and call the computer program in the storage medium to process the signals collected by the pressure sensor.

[0109] Preferably, the number of pressure sensors can be multiple. By synchronously collecting pressure signals through multiple sensors, the accuracy of judging states such as a user getting up from the seat, sitting down, body movement, and sitting still can be improved. As Figure 5 shown, sensor1 - sensor4 represent the first pressure sensor, the second pressure sensor, the third pressure sensor, and the fourth pressure sensor, and the MCU is a microprocessor. The pressure data collected by sensor1 - sensor4 is first converted by an analog-to-digital converter and then transmitted to the MCU. The MCU calls a heart rate calculation algorithm to calculate the real-time heart rate of the currently seated user based on the pressure signal values of sensor1 - sensor4.

[0110] Preferably, the shapes of sensor1 - sensor4 are strip-shaped and can be arranged in an array below the seat cushion. The thickness of the seat cushion is 0.2 cm or less, and the material can be a flexible material such as leather. The distance between adjacent sensors is 3 - 8 cm, and each pressure sensor is connected to the MCU through an independent ADC interface.

[0111] As shown Figure 6 in the figure above, through the above four pressure sensor detection strips, the change of the pressure signal value collected when the user sits on the cushion is measured in real time. The processor analyzes the characteristics of the pressure change through an algorithm, so as to realize the detection of the user's sitting posture state and heart rate. Specifically, it includes:

[0112] (1) Analysis of whether the user is seated. As Figure 8 shown in the figure, by obtaining and analyzing the data of the pressure sensor within a 10s window range, assuming that the step size of the window movement is 1s and the sampling period is 0.01s, each sensor can collect 1000 pressure signal values. Set the pressure data sets P_D1, P_D2, P_D3, and P_D4 of the four sensors Sensor1 - Sensor4 within every 10s; calculate the average value Pavg for each data set P_Dn respectively. If the average value Pav of any group in P_Dn is greater than the set human pressure threshold P 人 , it is determined that the user is in a seated state.

[0113] (2) Analysis of whether the user is in a body movement state. As Figure 9 shown in the figure, after initially determining that the user is seated by using the method Figure 8 shown in the figure, take the pressure data set P_Dn (i.e., the second pressure data set described above) in the collected pressure signals where the average pressure is greater than P 人 ; calculate the variance Pδ 2 corresponding to each data set P_Dn to characterize the degree of dispersion of the data set, P 离座 is the threshold for leaving the seat, and P 体动 is the threshold for body movement. The analysis of the body movement state is as follows:

[0114] When Pδ 2 > P 体动 it is considered that the user is in a body movement state;

[0115] When P 离座 ≤ Pδ 2 ≤ P 体动 it is considered that the user is in a sitting still state;

[0116] When Pδ 2 < P 离座 the user's state of leaving the seat is updated.

[0117] (3) Calculation of the user's real-time heart rate. As Figure 10 shown in the figure, when it is determined that the user is in a sitting still state, the data set P_Dn of the user in the sitting still state is processed to calculate the user's real-time heart rate, specifically including:

[0118] ①Perform Butterworth filtering on the dataset P_Dn to obtain Pbuf. This step is used to filter out low-frequency signals with frequencies less than 0.5 Hz;

[0119] ②Perform moving average filtering on the dataset Pbuf obtained in step ① to obtain Pavg, which can reduce data spikes and increase its smoothness;

[0120] ③Perform data normalization filtering on the dataset Pavg obtained in step ② to obtain Pstd, in order to filter out the individual differential characteristics between data and map the data range to between ±4;

[0121] ④Calculate the heart rate for the dataset Pstd obtained in step ③ to obtain the heart rate value.

[0122] The final heart rate effect diagram is as Figure 11 shown.

[0123] Through the above-mentioned heart rate detection device based on pressure signals and its corresponding heart rate detection algorithm, the present invention can determine whether the user is in a sitting, standing up, body movement, or sedentary state without the user's awareness, and can calculate the heart rate value of the user when sitting still in real time. Compared with the prior art, the present invention does not require the user to carry a specific heart rate measurement device. As long as the user is in a sedentary state, the heart rate of the user can be detected in real time, avoiding the discomfort brought to the user by the traditional photoelectric finger clip heart rate detector when detecting the heart rate, and solving the problem of low user comfort in the traditional method of heart rate detection.

[0124] Those skilled in the art should understand that the above embodiments can be provided as methods, devices, or computer program products. These embodiments can take the form of completely hardware embodiments, completely software embodiments, or embodiments combining software and hardware aspects. All or part of the steps in the methods related to the above embodiments can be completed by a program instructing relevant hardware, and this program can be stored in a storage medium readable by a computer device for executing all or part of the steps of the methods of the above embodiments.

[0125] The above embodiments are described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments. It should be understood that each process and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the processes and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of the computer device to generate a machine, so that the instructions executed by the processor of the computer device generate a device for realizing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0126] These computer program instructions can also be stored in a computer device-readable memory that can direct a computer device to work in a particular manner, such that the instructions stored in the computer device-readable memory produce a manufacture including an instruction device that implements the functions specified in one or more of the processes Figure 1 or sub-processes and / or blocks Figure 1 or sub-blocks.

[0127] These computer program instructions can also be loaded onto a computer device, such that a series of operation steps are performed on the computer device to produce a computer-implemented process, and thus the instructions executed on the computer device provide steps for implementing the functions specified in one or more of the processes Figure 1 or sub-processes and / or blocks Figure 1 or sub-blocks.

[0128] Although the above-described embodiments have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concept. Therefore, the above are only embodiments of the present invention, and do not limit the patent protection scope of the present invention. Any equivalent structural or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied to other related technical fields, shall be equally included in the patent protection scope of the present invention.

Claims

1. A heart rate detection method based on pressure signals, characterized in that, Including the following steps: S1: Obtain in real time the pressure values detected by the pressure sensors in the seat cushion within a first preset time period, generate a dataset of pressures to be measured, and calculate the dispersion value of this dataset of pressures to be measured; S2: Compare the dispersion value with a body movement threshold and a sitting still threshold to determine whether the sitting posture state type of the current user is a sitting still state or a body movement state; S3: When it is determined that the sitting posture state type of the current user is a sitting still state type, calculate the fluctuation frequency of the pressure values within the first preset time period, and calculate the real-time heart rate of the user based on this fluctuation frequency; The number of the pressure sensors is multiple, and the pressure sensors are arranged at intervals on the seat cushion; the body movement state type means that although the user's buttocks are on the seat cushion of the seat, other parts of the body are still in a moving state within a unit time; The sitting still state type means that when the user's buttocks are on the seat cushion of the seat, other parts of the body are basically kept still within a unit time; Step S1 includes: Obtain the pressure signal values collected by multiple pressure sensors within a first preset time period, obtain multiple datasets of pressures to be measured, and calculate the average pressure corresponding to the multiple datasets of pressures to be measured; Step S2 includes: Judge whether at least one of the calculated average pressure values is greater than a preset human body pressure threshold.

2. The heart rate detection method based on pressure signals according to claim 1, characterized in that, The method includes: Set a breathing frequency range, and calculate the change in pressure value caused by breathing in this dataset of pressures to be measured according to the breathing frequency range; In step S3, there is also a breathing filtering step for removing the change in pressure value caused by breathing in this dataset of pressures to be measured.

3. The heart rate detection method based on pressure signals according to claim 1, wherein , the value range of the body movement threshold is 1%-80% of the change rate relative to the average pressure value; the sitting still threshold is 0.01%-0.99% of the change rate relative to the average pressure value.

4. The heart rate detection method based on pressure signals according to claim 1, wherein In step S3, there is also: Perform normalization processing on the pressure values within the first preset time period; the normalization processing includes: mapping the numerical magnitudes of all data within a preset numerical range; Calculate the fluctuation frequency based on the data after normalization processing.

5. The heart rate detection method based on pressure signals according to claim 1 or 2, characterized in that, Calculate the real-time heart rate of the user based on the following steps: S51: The dataset to be calculated is represented as a vector P = [P1, P2,..., Pn]; S52: Calculate the differential vector DiffP corresponding to each item of data in P: DiffP(i) = P(i + 1) - P(i), where i ∈ 1, 2,..., n - 1; S53: Perform a sign operation on the differential vector to obtain a Trend vector: If DiffP(i) is greater than 0, then the value of Trend(i) is taken as 1; If DiffP(i) is less than 0, then the value of Trend(i) is taken as -1; If DiffP(i) is equal to 0, then the value of Trend(i) is taken as 0; S54: Traverse the Trend vector and perform the following operations: If Trend(i) = 0 and Trend(i + 1) ≥ 0, then assign Trend(i) the value of 1; If Trend(i) = 0 and Trend(i + 1) < 0, then assign Trend(i) the value of -1; S55: Calculate the difference vector R = Diff(Trend) of the Trend vector obtained in step S54 where Diff(Trend) = Trend(i + 1) - Trend(i); i ∈ 1, 2, …, n - 1; S56: Traverse the obtained difference vector R. If R(i) = -2, then i + 1 is a peak position of the projection vector P, and the corresponding peak is P(i + 1); if R(i) = 2, then i + 1 is a trough position of the projection vector P, and the corresponding trough is P(i + 1); S57: Subtract the trough value adjacent to each peak obtained in step S56 from the peak to obtain a floating value. If the value of the floating value is greater than 3, then save the time index of the peak position to obtain the peak index vector Index(j); S58: Let the Index interval size be the set T, calculate the average value Tavg of all the data in the set T, and calculate the user's real-time heart rate HeartRate based on the following formula HeartRate = 1 / (Tavg) 60.

6. The heart rate detection method based on pressure signals according to claim 1, wherein The value range of the first preset time period is 5 to 20 seconds, the sampling period is 0.01 seconds, and the number of pressure values in the first pressure data set is 500 - 2000.

7. A storage medium, characterized in that, The storage medium stores a computer program, and when the computer program is executed by a processor, it implements the method steps described in any one of claims 1 to 6.

8. An electronic device, characterized in that, The electronic device includes a storage medium and a processor. The storage medium is the storage medium described in claim 7, and the processor is configured to execute the computer program stored in the storage medium to implement the method steps described in any one of claims 1 to 6.

9. A heart rate detection device based on pressure signals, characterized in that, Comprising: A main body including a seat cushion; A pressure sensor disposed on the seat cushion; A storage medium, which is the storage medium described in claim 7; A processor for receiving the signal collected by the pressure sensor and calling the computer program in the storage medium to process the signal collected by the pressure sensor.

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