CN115040097A

CN115040097A - Heart rate detection method and device, storage medium and electronic equipment

Info

Publication number: CN115040097A
Application number: CN202210584250.9A
Authority: CN
Inventors: 李家锐; 陈建福; 张云龙
Original assignee: Xiamen Comfort Science and Technology Group Co Ltd
Current assignee: Xiamen Comfort Science and Technology Group Co Ltd
Priority date: 2022-05-26
Filing date: 2022-05-26
Publication date: 2022-09-13

Abstract

The invention discloses a heart rate detection method, a heart rate detection device, a storage medium and electronic equipment based on pressure signals. And then comparing the discrete degree value with a body movement threshold value and a resting threshold value, and judging whether the sitting posture state of the current user is a resting state or a body movement state. When the sitting posture state type of the current user is judged to be the resting posture type, the fluctuation frequency of the pressure value in the first preset time period is calculated, and the real-time heart rate of the user is calculated according to the fluctuation frequency. Through the scheme, the acquisition of the real-time heart rate of the user can be completed under the condition that the user does not perceive, and the sensory experience of the user in the heart rate acquisition process is improved.

Description

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

Technical Field

The invention relates to the field of heart rate detection, in particular to a heart rate detection method and device based on a pressure signal, a storage medium and electronic equipment.

Background

With the acceleration of life rhythm, more and more office workers bear greater working pressure and mental pressure, working in a sitting posture for a long time generates many health problems for the office workers, and the research and development of a health detection product for the office workers are of great importance for monitoring the health state of users in real time.

The heart rate is an important parameter in human vital signs, and heart rate detection is an essential index in human health detection, however, the market has some defects in the devices capable of monitoring the heart rate. For example, the traditional medical electrocardiograph detection device needs a professional doctor to operate the electrocardiograph detection device, needs to install a plurality of sensor probes on the body of a detected person, and is not suitable for being used in home and office scenes.

For example, some photoelectric heart rate detection devices need to be worn on the wrist or fingertip of a user for detection, and the user who is not used to wear a wristwatch can easily feel a foreign body sensation; the photoelectric heart rate detector with the fingertip as the acting part cannot detect the heart rate at any time and any place.

Therefore, on the premise of the market demand, the equipment which can research and develop a piece of equipment and can finish heart rate acquisition and detection on the user under the condition that the user cannot feel foreign body sensation is particularly important.

Disclosure of Invention

Therefore, a technical scheme for heart rate detection based on a pressure signal needs to be provided to solve the problems that the existing heart rate detection equipment is limited in application scene and poor in user experience feeling.

To achieve the above object, in a first aspect, the present invention provides a heart rate detection method based on a pressure signal, comprising the steps of:

s1: acquiring a pressure value detected by a pressure sensor in a cushion within a first preset time period in real time, generating a pressure data set to be detected, and calculating a discrete degree value of the pressure data set to be detected;

s2: comparing the discrete degree value with a body movement threshold value and a resting threshold value, and judging whether the sitting posture state of the current user is a resting state or a body movement state;

s3: when the sitting posture state type of the current user is judged to be the resting posture type, the fluctuation frequency of the pressure value in the first preset time period is calculated, and the real-time heart rate of the user is calculated according to the fluctuation frequency.

As an alternative embodiment, step S1 further includes: and calculating a pressure average value of the pressure data set to be detected, judging whether the pressure average value is greater than a preset human body pressure threshold value, and if so, executing the step S2.

As an alternative embodiment, the method comprises:

setting a respiratory frequency range, and calculating the pressure value change generated by respiration in the pressure data set to be detected according to the respiratory frequency range;

in step S3, a respiration filtering step is further included to remove pressure value changes in the pressure data set to be measured due to respiration.

As an optional embodiment, the value range of the body motion threshold is 1% -80% of the change rate of the average value of the relative pressure; the resting threshold value is 0.01-0.99% of the change rate of the average value of the relative pressure.

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

step S1 includes:

acquiring pressure signal values acquired by a plurality of pressure sensors within a first preset time period to obtain a plurality of pressure data sets to be detected, and calculating pressure average values corresponding to the plurality of pressure data sets to be detected;

step S2 includes:

and judging whether at least one of the plurality of calculated pressure average values is larger than a preset human body pressure threshold value.

As an alternative embodiment, step S3 further includes:

standardizing the pressure value in a first preset time period; the normalization process includes: mapping the numerical values of all the data into a preset numerical range;

the fluctuation frequency is calculated based on the data after the normalization processing.

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

s51, expressing the data set to be calculated as a vector P ═ P ₁ ，P ₂ ，…，Pn]。

S52: calculating a difference vector DiffP corresponding to each item of data in P:

diffp (i) ═ P (i +1) -P (i), where i ∈ 1, 2, …, n-1;

s53: performing sign operation on the differential vector to obtain a Trend vector:

if DiffP (i) is greater than 0, Trend (i) takes a value of 1;

if DiffP (i) is less than 0, Trend (i) takes the value-1;

if DiffP (i) is equal to 0, Trend (i) takes the value 0;

s54: traversing Trend vector and performing the following operations:

if Trend (i) ≧ 0 and Trend (i +1) ≧ 0, assign Trend (i) to 1;

if Trend (i) ═ 0 and Trend (i +1) < 0, assign Trend (i) to-1;

s55: calculating the difference vector R of the Trend vector obtained in step S54, diff (Trend)

Wherein, diff (Trend) ═ Trend (i +1) -Trend (i);

i∈1，2，…，n-1；

s56: traversing the obtained difference vector R, if R (i) is equal to-2, i +1 is a peak value bit of the projection vector P, and the corresponding peak value is P (i + 1); if r (i) is 2, i +1 is a trough position of the projection vector P, and the corresponding trough is P (i + 1);

s57: subtracting the trough value adjacent to the peak value from all the peak values obtained in the step S56 to obtain a floating value, and if the value of the floating value is greater than 3, storing the time index of the peak value bit to obtain a peak index vector index (j);

s58: assuming the Index interval size as set T, the average Tavg of all data in set T is calculated, and the user real-time heart rate HeartRate is calculated based on the following formula:

HeartRate＝1/(Tavg)*60。

as an optional 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 the pressure values in the first pressure data set is 500-2000.

In a second aspect, the invention also provides a storage medium storing a computer program which, when executed by a processor, performs the method steps according to the first aspect of the invention.

In a third aspect, the present invention also provides an electronic device comprising a storage medium as in the second aspect of the present invention and a processor for executing a computer program stored in the storage medium to implement the method steps as in the first aspect of the present invention.

In a fourth aspect, the invention further provides a heart rate detection device based on a pressure signal, which comprises a body, a pressure sensor, a storage medium and a processor; the body comprises a cushion; the pressure sensor is arranged on the cushion; the storage medium is the storage medium of the second aspect of the present invention; the processor is used for receiving the signals collected by the pressure sensor and calling a computer program in the storage medium to process the signals collected by the pressure sensor.

Different from the prior art, according to the heart rate detection method, the heart rate detection device, the heart rate detection storage medium and the electronic equipment based on the pressure signals, provided by the technical scheme, the pressure value detected by the pressure sensor in the cushion within the first preset time period is acquired in real time to generate a pressure data set to be detected, and the discrete degree value of the pressure data set to be detected is calculated. And then comparing the discrete degree value with a body movement threshold value and a resting threshold value, and judging whether the sitting posture state of the current user is a resting state or a body movement state. When the sitting posture state type of the current user is judged to be the resting posture type, the fluctuation frequency of the pressure value in the first preset time period is calculated, and the real-time heart rate of the user is calculated according to the fluctuation frequency. By the aid of the scheme, the real-time heart rate of the user can be collected without the user being aware of the heart rate, and sensory experience of the user in the heart rate collecting process is improved.

Drawings

Fig. 1 is a flow chart of a heart rate detection method based on a pressure signal according to an embodiment of the present invention;

FIG. 2 is a flow chart of a method for heart rate detection based on pressure signals according to another embodiment of the present invention;

FIG. 3 is a flow chart of a method for heart rate detection based on pressure signals according to yet another embodiment of the present invention;

fig. 4 is a block diagram of an electronic device according to an embodiment of the invention;

FIG. 5 is a schematic diagram of a circuit block of a heart rate detection apparatus based on a pressure signal according to an embodiment of the present invention;

FIG. 6 is a flow chart of the sitting posture type determination and heart rate calculation according to an embodiment of the present invention;

FIG. 7 is a waveform diagram corresponding to a pressure dataset under test according to an embodiment of the present invention;

FIG. 8 is a flow chart of a sitting posture type determination and heart rate calculation according to another embodiment of the present invention;

FIG. 9 is a flow chart of the sitting posture type determination according to an embodiment of the present invention;

FIG. 10 is a flow chart of a user's real-time heart rate calculation according to an embodiment of the present invention;

FIG. 11 is a waveform illustrating the calculation of heart rate effects from a pressure data set according to an embodiment of the present invention;

description of the reference numerals:

10. an electronic device;

101. a processor;

102. a storage medium.

Detailed Description

To explain technical contents, structural features, and objects and effects of the technical solutions in detail, the following detailed description is given with reference to the accompanying drawings in conjunction with the embodiments.

In order to explain in detail possible application scenarios, technical principles, practical embodiments, and the like of the present application, the following detailed description is given with reference to the accompanying drawings in conjunction with the listed embodiments. The embodiments described herein are merely for more clearly illustrating the technical solutions of the present application, and therefore, the embodiments are only used as examples, and the scope of the present application is not limited thereby.

Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase "an embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or related to other embodiments specifically defined. In principle, in the present application, the technical features mentioned in the embodiments can be combined in any manner to form a corresponding implementable technical solution as long as there is no technical contradiction or conflict.

Unless otherwise defined, technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the use of relational terms herein is intended only to describe particular embodiments and is not intended to limit the present application.

In the description of the present application, the term "and/or" is a expression for describing a logical relationship between objects, indicating that three relationships may exist, for example, a and/or B, indicating that: there are three cases of A, B, and both A and B. In addition, the character "/" herein generally indicates that the former and latter associated objects are in a logical relationship of "or".

In this application, terms such as "first" and "second" are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.

Without further limitation, in this application, the use of "including," "comprising," "having," or other similar expressions in phrases and expressions of "including," "comprising," or "having," is intended to cover a non-exclusive inclusion, and such expressions do not exclude the presence of additional elements in a process, method, or article that includes an element, such that a process, method, or article that includes a list of elements may include not only those elements but also other elements not expressly listed or inherent to such process, method, or article.

As is understood in the examination of the guidelines, the terms "greater than", "less than", "more than" and the like in this application are to be understood as excluding the number; the expressions "above", "below", "within" and the like are understood to include the present numbers. Furthermore, the description of embodiments herein of the present application of the term "plurality" means more than two (including two), and expressions relating to "plurality" similar thereto are also to be understood, such as "plurality", etc., unless explicitly defined otherwise.

Fig. 1 is a flowchart of a heart rate detection method based on pressure signals according to an embodiment of the present invention. The method comprises the following steps:

In the embodiment of the application, the pressure signal detected by the pressure sensor is collected by taking a first preset time period as 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 the data of the pressure data set to be detected is 500-2000. Assuming that a preset time period is 10s, 1000 pressure signal values collected by a certain pressure sensor within 10s are obtained, and a sampled waveform diagram is shown in fig. 7.

Preferably, the discrete magnitude value is a variance or a standard deviation. The value range of the body movement threshold is 1% -80% of the change rate of the relative pressure average value; the resting threshold value is 0.01-0.99% of the change rate of the average value of the relative pressure. The body movement state type means that although the buttocks of the user are positioned on the seat cushion of the seat, other parts of the body are still in a moving state in unit time, and specifically reflects that the change of the pressure signal value in unit time is more severe on the signal collected by the pressure sensor, namely the dispersion degree is larger. The sitting state type is that when the buttocks of the user are positioned on the seat cushion of the seat, other parts of the body basically keep in a stationary state in unit time, and specifically reflects that the change of the pressure signal value in unit time is more gentle on the signal collected by the pressure sensor, namely the discrete degree is smaller.

According to the method, the pressure value detected by the pressure sensor in the cushion within the first preset time period is obtained in real time, the pressure data set to be detected is generated, and the discrete degree value of the pressure data set to be detected is calculated. And then comparing the discrete degree value with a body movement threshold value and a resting threshold value, and judging whether the sitting posture state of the current user is a resting state or a body movement state. When the sitting posture state type of the current user is judged to be the resting posture type, the fluctuation frequency of the pressure value in the first preset time period is calculated, and the real-time heart rate of the user is calculated according to the fluctuation frequency. By the aid of the scheme, the real-time heart rate of the user can be collected without the user being aware of the heart rate, and sensory experience of the user in the heart rate collecting process is improved.

In certain embodiments, step S1 further includes: and calculating a pressure average value of the pressure data set to be detected, judging whether the pressure average value is greater than a preset human body pressure threshold value, and if so, executing the step S2.

Preferably, the pressure sensor continuously collects the pressure signal in real time. Taking the application to a seat as an example, the pressure sensor may be disposed below a cushion of the seat, and if a user sits on the seat, the pressure signal detected by the pressure sensor may increase abruptly, which is specifically represented by that an average value of the pressure signals collected in unit time is greater than a preset human body pressure threshold, so that whether the user sits on the seat can be determined by determining whether the average value of the pressure is greater than the preset human body pressure threshold.

In certain embodiments, the method comprises: setting a respiratory frequency range, and calculating the pressure value change generated by respiration in the pressure data set to be detected according to the respiratory frequency range; in step S3, a respiration filtering step is further included to remove pressure value changes in the pressure data set to be measured due to respiration. The influence caused by pressure value change generated by respiration in the pressure data set to be detected can be effectively eliminated by introducing the respiration filtering step, and the accuracy of the heart rate calculated in real time is effectively improved.

The collection of the real-time heart rate of the user is preferably calculated by signals collected by the pressure sensor when the user is in a sitting state, because the heart rate of the user is stable when the user is in the sitting state, and the heart rate value obtained by calculation based on the pressure signal data collected at the moment is more real and accurate. Therefore, the method screens out the pressure data (namely the data larger than the preset human body pressure threshold) when the human body is seated, compares the screened out data with the body movement threshold and the sitting still threshold to screen out the data when the sitting posture type is the sitting still state type, and calculates the real-time heart rate of the user based on the data when the sitting still state type, so that the real-time heart rate calculation of the user can be more accurate.

In some embodiments, step S3 further includes: firstly, the step S301 is carried out to standardize the pressure value in a first preset time period; the normalization process includes: mapping the numerical values of all the data into a preset numerical range; the flow then proceeds to step S302 to calculate the fluctuation frequency based on the data after the normalization processing. The preset value range may be set as needed, for example, the preset value range may be set as the interval [ 4, 4 ]. The screened data is subjected to standardization processing, so that the calculation amount in subsequent processing can be effectively reduced.

In other embodiments, step S3 further includes: performing Butterworth filtering on the fluctuation frequency of the pressure value in the first preset time period, and filtering out low-frequency signals with the frequency smaller than the preset frequency; then, performing moving average filtering on the data subjected to the Butterworth filtering; then, carrying out standardization processing on the data subjected to the moving average filtering; the user heart rate is then calculated based on the normalized data.

Preferably, the magnitude of the preset frequency can be set according to actual needs, and preferably can be set to 0.5 Hz. After the moving average filtering, the method can effectively reduce the burrs of the data, increase the smoothness of the whole data and facilitate subsequent calculation.

In some embodiments, the number of the pressure sensors is multiple, and the pressure sensors are arranged on the cushion at intervals; step S1 includes: acquiring pressure signal values acquired by a plurality of pressure sensors in a first preset time period to obtain a plurality of pressure data sets to be detected, and calculating pressure average values corresponding to the plurality of pressure data sets to be detected; step S2 includes: and judging whether at least one of the plurality of calculated pressure average values is larger than a preset human body pressure threshold value.

Use the real-time rhythm of the heart through seat detection user as the example, can set up the cushion on the seat earlier, the cushion preferred can adopt flexible material, then set up a plurality of pressure sensor in the below of cushion, when the human body takes a seat, a plurality of pressure sensor can gather the pressure signal that increases precipitously in step, the average value of the pressure signal value of collection when arbitrary one pressure sensor in first preset time quantum is greater than predetermined human pressure threshold value, can judge here occasionally that the human body takes a seat. A plurality of pressure sensors judge synchronously, so that the judgment of whether the human body is seated is more accurate, and the possibility of misjudgment is avoided.

As shown in fig. 3, in some embodiments, the user's real-time heart rate is calculated based on the following steps:

s51, representing the dataset to be computed as a vector P ═ P1, P2, …, Pn;

diffp (i) ═ P (i +1) -P (i), where i ∈ 1, 2, …, n-1;

s53: performing sign operation on the difference vector to obtain a Trend vector:

if DiffP (i) is greater than 0, Trend (i) takes a value of 1;

if DiffP (i) is less than 0, Trend (i) takes the value-1;

if DiffP (i) is equal to 0, Trend (i) takes the value 0;

s54: traversing Trend vector and performing the following operations:

if Trend (i) ≧ 0 and Trend (i +1) ≧ 0, assigning Trend (i) to 1;

if Trend (i) ═ 0 and Trend (i +1) < 0, assign Trend (i) to-1;

Wherein, diff (Trend) ═ Trend (i +1) -Trend (i);

i∈1，2，…，n-1；

s58: and (3) setting the Index interval size as a set T, calculating the average value Tavg of all data in the set T, and calculating the real-time heart rate Heartrate of the user based on the following formula:

HeartRate＝1/(Tavg)*60。

through the scheme, the heart rate of the current user can be calculated in real time based on the pressure signal of the user, and the calculated heart rate can be displayed in real time in the display unit. Because the collection of pressure signal need not the user and additionally wears other specific equipment, foreign body sensation when can effectively reduce the user and detect the rhythm of the heart for the testing process is more comfortable, promotes user experience.

As shown in fig. 4, in a third aspect, the present invention further provides an electronic device 10, comprising a processor 101 and a storage medium 102, wherein the storage medium 102 is the storage medium according to the second aspect; the processor 101 is adapted to execute a computer program stored in the storage medium 102 to implement the method steps as the first aspect.

In this embodiment, the electronic device is a computer device, including but not limited to: personal computer, server, general-purpose computer, special-purpose computer, network equipment, embedded equipment, programmable equipment, intelligent mobile terminal, intelligent home equipment, wearable intelligent equipment, vehicle-mounted intelligent equipment, etc. Storage media include, but are not limited to: RAM, ROM, magnetic disk, magnetic tape, optical disk, flash memory, U disk, removable hard disk, memory card, memory stick, network server storage, network cloud storage, etc. Processors include, but are not limited to, a CPU (Central processing Unit), a GPU (image processor), an MCU (Microprocessor), and the like.

In a fourth aspect, the invention further provides a heart rate detection device based on a pressure signal, which comprises a body, a pressure sensor, a storage medium and a processor; the body comprises a cushion; the pressure sensor is arranged below the cushion; the storage medium is the storage medium of the second aspect of the present invention; the processor is used for receiving the signals collected by the pressure sensor and calling a computer program in the storage medium to process the signals collected by the pressure sensor.

Preferably, the number of the pressure sensors can be multiple, and the accuracy of judging the states of the user such as seat falling, sitting, body movement and sitting can be improved by synchronously acquiring pressure signals through the multiple sensors. As shown in fig. 5, the sensors 1-4 represent a first pressure sensor, a second pressure sensor, a third pressure sensor and a fourth pressure sensor, and the MCU is a microprocessor. The pressure data collected by the sensors 1-4 are converted by the analog-to-digital converter and then transmitted to the MCU, and the MCU invokes a heart rate calculation algorithm to calculate the real-time heart rate of the currently seated user according to the pressure signal values of the sensors 1-4.

Preferably, the sensors 1-4 have a bar shape and are arranged in an array under the seat cushion. The thickness of the cushion is below 0.2cm, and the cushion can be made of flexible material such as leather. The distance between adjacent sensors is 3-8cm, and each pressure sensor is connected with the MCU through an independent ADC interface.

As shown in fig. 6, the change of the pressure signal value collected when the user sits on the cushion is measured in real time through the 4 pressure sensor detection strips, and the processor analyzes the characteristics of the pressure change through an algorithm, so that the sitting posture state and the heart rate of the user are detected. The method specifically comprises the following steps:

(1) whether the user is seated for analysis. As shown in fig. 8, by acquiring data of the pressure sensors in a 10s window range for analysis and calculation, assuming that the step length of the window movement is 1s and the sampling period is 0.01s, 1000 pressure signal values can be acquired by each sensor. Setting pressure data sets P _ D1, P _ D2, P _ D3 and P _ D4 in every 10s of four sensors of Sensor1-Sensor 4; calculating the average value Pavg of each data set P _ Dn, and if the average value Pav of any group in P _ Dn is larger than the set human body pressure threshold value P _{Human being} The user is deemed to be seated.

(2) Whether the user is in the body motion state analysis. As shown in FIG. 9, after the method shown in FIG. 8 is used to preliminarily determine that the user is seated, the average value of the pressures in the collected pressure signals is taken to be greater than P _{Human being} The pressure data set P _ Dn (i.e. the second pressure data set described hereinbefore); for each data set P _ Dn, the variance P δ for that data set is calculated ² Characterizing the degree of dispersion, P, of the data set _{Free seat} To an out-of-seat threshold, P _{Body movement} Is the body motion threshold. The analysis of the body motion state is as follows:

when P.delta. ² ＞P _{Body movement} The user is considered to be in a physical movement state;

when P is present _{Free seat} ≤Pδ ² ≤P _{Body movement} The user is considered to be in a sitting state;

when P is delta ² ＜P _{Free seat} The update user is in an out-of-seat state.

(3) User real-time heart rate calculation. As shown in fig. 10, when it is determined that the user is in the resting state, the data processing is performed on the data set P _ Dn when the user is in the resting state to calculate the real-time heart rate of the user, which specifically includes:

firstly, performing Butterworth filtering processing on the data set P _ Dn to obtain Pbuf. This step is used to filter out low frequency signals with frequencies less than 0.5 Hz;

secondly, the step of: carrying out moving average filtering processing on the data set Pbuf obtained in the step I to obtain Pavg, so that the burrs of the data can be reduced, and the smoothness of the data can be improved;

③: carrying out data standardization filtering processing on the data set Pavg obtained in the step two to obtain Pstd so as to filter individual differentiation characteristics among data and map a data range to be within +/-4;

fourthly, the method comprises the following steps: and (4) carrying out heart rate calculation on the data set Pstd obtained in the step (three) to obtain a heart rate value.

The resulting heart rate effect graph is shown in fig. 11.

According to the heart rate detection device based on the pressure signal and the heart rate detection algorithm corresponding to the heart rate detection device, whether the user is in a sitting state, a sitting-off state, a body movement state or a sitting-still state can be judged without being perceived by the user, and the heart rate value of the user in the sitting-still state can be calculated in real time. Compared with the prior art, the heart rate detection device and the heart rate detection method have the advantages that a user does not need to carry a specific heart rate measurement device, the heart rate of the user can be detected in real time only when the user is in a sitting state, discomfort brought to the user when the heart rate is detected by a traditional photoelectric finger-clipped heart rate detector is avoided, and the problem that the comfort of the user is low when the heart rate is detected by the traditional method is solved.

As will be appreciated by one skilled in the art, the above-described embodiments may be provided as a method, apparatus, or computer program product. These embodiments may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. All or part of the steps of the methods related to the above embodiments may be implemented by a program instructing relevant hardware, where the program may be stored in a storage medium readable by a computer device, and is used to execute all or part of the steps of the methods related to the above embodiments.

The various embodiments described above are described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a computer apparatus to produce a machine, such that the instructions, which execute via the processor of the computer apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer device to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer apparatus to cause a series of operational steps to be performed on the computer apparatus to produce a computer implemented process such that the instructions which execute on the computer apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

Although the embodiments have been described, once the basic inventive concept is obtained, other variations and modifications of these embodiments can be made by those skilled in the art, so that these embodiments are only examples of the present invention, and not intended to limit the scope of the present invention, and all equivalent structures or equivalent processes that can be used in the present specification and drawings, or used directly or indirectly in other related fields are encompassed by the present invention.

Claims

1. A heart rate detection method based on pressure signals is characterized by comprising the following steps:

2. The heart rate detection method based on pressure signals as claimed in claim 1, wherein the step S1 further comprises: and calculating a pressure average value of the pressure data set to be detected, judging whether the pressure average value is greater than a preset human body pressure threshold value, and if so, executing the step S2.

3. The method for heart rate detection based on pressure signals according to claim 1,

the method comprises the following steps:

4. The heart rate detection method based on the pressure signal as claimed in claim 1, wherein the value range of the body motion threshold is 1% -80% of the change rate of the relative pressure average value; the resting threshold value is 0.01-0.99% of the change rate of the average value of the relative pressure.

5. The heart rate detection method based on the pressure signals as claimed in claim 2, wherein the number of the pressure sensors is multiple, and the pressure sensors are arranged on a cushion at intervals;

step S1 includes:

acquiring pressure signal values acquired by a plurality of pressure sensors in a first preset time period to obtain a plurality of pressure data sets to be detected, and calculating pressure average values corresponding to the plurality of pressure data sets to be detected;

step S2 includes:

6. The heart rate detecting method based on pressure signals as claimed in claim 1, further comprising in step S3:

7. Heart rate detection method based on pressure signals according to claim 1 or 3 or 6, characterized in that the real-time heart rate of the user is calculated based on the following steps:

s51 representing the dataset to be computed as the vector P ═ P ₁ ，P ₂ ，…，Pn]；

diffp (i) ═ P (i +1) -P (i), where i ∈ 1, 2, …, n-1;

if DiffP (i) is greater than 0, Trend (i) takes a value of 1;

if DiffP (i) is less than 0, Trend (i) takes a value of-1;

if DiffP (i) is equal to 0, Trend (i) takes the value 0;

s54: traversing Trend vector and performing the following operations:

if Trend (i) ≧ 0 and Trend (i +1) ≧ 0, assigning Trend (i) to 1;

if Trend (i) ═ 0 and Trend (i +1) < 0, assign Trend (i) to-1;

s55: calculating the difference vector R ═ diff (Trend) of the Trend vector obtained in step S54

Wherein, diff (Trend) ═ Trend (i +1) -Trend (i);

i∈1，2，…，n-1；

HeartRate＝1/(Tavg)*60。

8. the method as claimed in claim 1, wherein the first predetermined time period ranges from 5 to 20 seconds, the sampling period is 0.01 second, and the number of the pressure values in the first pressure data set is 500-2000.

9. A storage medium, characterized in that the storage medium stores a computer program which, when being executed by a processor, carries out the method steps of any one of claims 1 to 8.

10. An electronic device, characterized in that the electronic device comprises a storage medium according to claim 9 and a processor for executing a computer program stored in the storage medium to implement the method steps according to any of claims 1 to 8.

11. A heart rate detection device based on a pressure signal, comprising:

the body comprises a cushion;

the pressure sensor is arranged on the cushion;

a storage medium according to claim 9;

and the processor is used for receiving the signals acquired by the pressure sensor and calling a computer program in the storage medium to process the signals acquired by the pressure sensor.