CN113693578B - Heart rate estimation method, device, equipment, system and storage medium - Google Patents
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Abstract
The invention discloses a heart rate estimation method, a device, equipment, a system and a storage medium. The method comprises the following steps: respectively controlling each heartbeat acquisition device to acquire heartbeat physiological signals of a user; performing signal preprocessing on each heartbeat physiological signal to obtain synchronous heartbeat physiological signals; extracting a heartbeat interval sequence of each synchronous heartbeat physiological signal, wherein the heartbeat interval sequence is a sequence formed by time intervals between every two adjacent wave peaks in the synchronous heartbeat physiological signals; and carrying out weighted summation on the heartbeat interval sequences of the synchronous heartbeat physiological signals to obtain a final heartbeat interval sequence, and estimating the heart rate of the user based on the final heartbeat interval sequence. According to the technical scheme, the heart rate of the user can be estimated by combining the heartbeat physiological signals of the user acquired by the image acquisition equipment, the wearing equipment and the electrode patch, so that the detection error caused by the fact that the single sensor is not tightly attached is reduced, and the accuracy of heart rate detection in a vehicle driving scene is improved.
Description
Technical Field
The embodiment of the invention relates to the field of physiological parameter detection, in particular to a heart rate estimation method, a device, equipment, a system and a storage medium.
Background
Modern life is fast, and people use automobiles more frequently and more frequently. Meanwhile, due to neglect of physiological health problems, the frequency of fatigue driving or sudden diseases or traffic accidents of drivers is also higher and higher. Therefore, it is necessary to monitor the physiological parameters of the driving (e.g. heart rate) in real time.
In the prior art, a single sensor is often used to detect heart rate. However, due to the complex environment in the vehicle, the sensor may not be fully contacted with the user due to the action of the user in the driving process of the vehicle, so that the heart rate detection result is inaccurate, and the actual physiological heart rate change of the user cannot be reflected.
Disclosure of Invention
The embodiment of the invention provides a heart rate estimation method, a device, equipment, a system and a storage medium, which are used for realizing the estimation of the heart rate of a user by combining heart beat physiological signals of the user acquired by three sensors of image acquisition equipment, wearing equipment and electrode patches, reducing detection errors caused by the fact that single sensors are not tightly attached, and improving the accuracy of heart rate detection in a vehicle driving scene.
In a first aspect, an embodiment of the present invention provides a heart rate estimation method, including:
respectively controlling each heartbeat acquisition device to acquire heartbeat physiological signals of a user;
Performing signal preprocessing on each heartbeat physiological signal to obtain synchronous heartbeat physiological signals;
Extracting a heartbeat interval sequence of each synchronous heartbeat physiological signal, wherein the heartbeat interval sequence is a sequence formed by time intervals between every two adjacent wave peaks in the synchronous heartbeat physiological signals;
further, the heartbeat interval sequences of the synchronous heartbeat physiological signals are weighted and summed to obtain a final heartbeat interval sequence, and the heart rate of the user is estimated based on the final heartbeat interval sequence.
Collecting heartbeat physiological signals of a user through each heartbeat collecting device respectively comprises the following steps:
controlling the image acquisition equipment to acquire facial heartbeat signals representing heartbeat changes of the user;
controlling the wearable device to collect pulse signals of the user;
and controlling the electrode patch to acquire the epidermis heartbeat signal of the user.
Further, the performing signal preprocessing on each heartbeat physiological signal to obtain each synchronous heartbeat physiological signal includes:
Filtering each heartbeat physiological signal to obtain a filtered heartbeat physiological signal;
And carrying out signal wave crest alignment on each heartbeat physiological signal after filtering to obtain each synchronous heartbeat physiological signal.
Further, the filtering processing is performed on each heartbeat physiological signal to obtain a filtered heartbeat physiological signal, which includes:
Eliminating baseline drift noise and environmental noise of the facial heartbeat signal through wavelet transformation to obtain a filtered facial heartbeat signal;
The baseline drift noise of the pulse signals is eliminated through self-adaptive filtering, the environmental noise of the pulse signals with the baseline drift eliminated is removed through band-pass filtering, and the pulse signals after filtering are obtained;
And eliminating baseline drift noise of the epidermis heartbeat signal through self-adaptive filtering, and eliminating environmental noise of the epidermis heartbeat signal with the baseline drift noise through band-pass filtering to obtain a filtered epidermis heartbeat signal.
Further, the step of carrying out weighted summation on the heartbeat interval sequence of each synchronous heartbeat physiological signal to obtain a final heartbeat interval sequence comprises the following steps:
Determining the weight of a heartbeat interval sequence corresponding to each synchronous heartbeat physiological signal;
for each heartbeat physiological signal, determining the product of the weight of the heartbeat interval sequence and each heartbeat interval in the heartbeat interval sequence as a weighted heartbeat interval sequence of the synchronous heartbeat physiological signal;
and summing the weighted heartbeat intervals corresponding to the weighted heartbeat interval sequences to obtain a final heartbeat interval sequence.
Further, determining the weight of the heartbeat interval sequence of each synchronous heartbeat physiological signal comprises:
For each heartbeat interval sequence of the synchronous heartbeat physiological signals, determining the ratio of the standard deviation and the average value of each heartbeat interval in the heartbeat interval sequence, and determining the weight coefficient corresponding to the ratio by a table lookup method;
determining the sum of the weight coefficients corresponding to the heartbeat interval sequences of the synchronous heartbeat physiological signals as a total weight coefficient;
and determining the ratio of the weight coefficient to the total weight coefficient as the weight of the heartbeat interval sequence of the synchronous heartbeat physiological signal.
In a second aspect, an embodiment of the present invention further provides a heart rate estimation device, including:
the acquisition module is used for respectively controlling each heartbeat acquisition device to acquire heartbeat physiological signals of the user;
The preprocessing module is used for carrying out signal preprocessing on each heartbeat physiological signal to obtain synchronous heartbeat physiological signals;
The extraction module is used for extracting a heartbeat interval sequence of each synchronous heartbeat physiological signal, wherein the heartbeat interval sequence is a sequence formed by time intervals between every two adjacent wave peaks in the synchronous heartbeat physiological signals;
and the estimation module is used for carrying out weighted summation on the heartbeat interval sequences of the synchronous heartbeat physiological signals to obtain a final heartbeat interval sequence, and estimating the heart rate of the user based on the final heartbeat interval sequence.
In a third aspect, an embodiment of the present invention further provides a heart rate estimation device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the heart rate estimation method according to any one of the embodiments of the present invention when the processor executes the program.
In a fourth aspect, an embodiment of the present invention further provides a heart rate estimation system, including a plurality of heartbeat acquisition devices and a heart rate estimation device connected to each of the heartbeat acquisition devices; the heartbeat acquisition device includes: image acquisition device, wearing equipment and electrode paster. The image acquisition device is used for acquiring facial heartbeat signals representing heartbeat changes of the user based on the control of the heart rate estimation device; the wearable device is used for acquiring pulse signals of the user based on the control of the heart rate estimation device; the electrode patch is used for acquiring epidermis heartbeat signals of the user based on the control of the heart rate estimation device; the heart rate estimation device is used for executing the method provided by any embodiment of the invention.
In a fifth aspect, embodiments of the present invention further provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a heart rate estimation method according to any of the embodiments of the present invention.
According to the embodiment of the invention, the heart rate of the user is estimated by combining the image acquisition equipment, the wearing equipment and the electrode patch to acquire the heartbeat physiological signals of the user, so that the problem that the heart rate detection is large due to the fact that the single sensor is not tightly attached in the existing vehicle driving scene is solved, the detection error caused by the fact that the single sensor is not tightly attached is reduced, and the effect of improving the accuracy of the heart rate detection in the vehicle-mounted scene is achieved.
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, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a heart rate estimation method according to a first embodiment of the invention;
FIG. 2A is a flow chart of a heart rate estimation method according to a second embodiment of the invention;
FIG. 2B is a flowchart of a heart rate signal filtering method according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of a heart rate estimation device according to a third embodiment of the present invention;
fig. 4 is a schematic structural view of a heart rate estimation device according to a fourth embodiment of the present invention;
fig. 5 is a schematic structural diagram of a heart rate estimation system according to a fifth embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting thereof. It should be further noted that, for convenience of description, only some, but not all of the structures related to the present invention are shown in the drawings.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures. Meanwhile, in the description of the present invention, the terms "first", "second", and the like are used only to distinguish the description, and are not to be construed as indicating or implying relative importance.
Example 1
Fig. 1 is a flowchart of a heart rate estimation method according to an embodiment of the present invention, where the embodiment is applicable to a situation of estimating a heart rate of a user in real time in a vehicle driving scenario, the method may be performed by a heart rate estimation device according to an embodiment of the present invention, and the device may be implemented in a software and/or hardware manner, and the device may be integrated in a heart rate estimation apparatus.
In an embodiment of the present invention, the heart rate estimation method is applied to a heart rate estimation system, the heart rate detection system includes a plurality of heart beat acquisition devices, and the heart beat acquisition devices include: image acquisition device, wearing equipment and electrode paster. The image acquisition equipment can be a high-precision camera; the wearable device may be any device having heart rate detection function, such as a bracelet or heart rate detector; the electrode patch may be provided in the vehicle where there is prolonged skin contact with the user, such as at the steering wheel grip or on a manual gear.
As shown in fig. 1, the method specifically includes the following steps:
s110, respectively controlling each heartbeat acquisition device to acquire heartbeat physiological signals of the user.
Wherein, the heartbeat acquisition device may include: image acquisition device, wearing equipment and electrode paster. A heartbeat physiological signal refers to a signal that may characterize a heartbeat change, such as a beat signal of the epidermis, a pulse signal, or a facial beat signal that characterizes a heartbeat change.
For example, the heartbeat physiological signal collected by the image collecting device may be a facial heartbeat signal of a face of the user representing a heartbeat change, the heartbeat physiological signal collected by the wearing device may be a pulse signal of the user, and the heartbeat physiological signal collected by the electrode patch may be an electrocardiosignal conducted by the user through the epidermis.
Specifically, the heart rate estimation device respectively controls each heart rate acquisition device to acquire the heart rate physiological signals of the user. For example, after receiving the start command, the heart rate estimation device sends an acquisition command to each heart beat acquisition device, so that each heart beat acquisition device acquires a heart beat physiological signal of the user within a preset time and transmits the heart beat physiological signal to the heart rate estimation device to perform heart rate estimation of the user. For example, each heartbeat acquisition device acquires a heartbeat physiological signal of the user within a preset time, and the preset time may be 1S or 10S, which is not limited in the embodiment of the present invention.
S120, carrying out signal preprocessing on each heartbeat physiological signal to obtain synchronous heartbeat physiological signals.
Because the heartbeat physiological signals of the heartbeat acquisition devices are affected by the acquisition environment, noise exists in the heartbeat physiological signals of the same user acquired by the heartbeat acquisition devices at the same moment, and the heartbeat physiological signals of the same user acquired by the heartbeat acquisition devices can be asynchronous, therefore, the heartbeat physiological signals acquired by the heartbeat acquisition devices need to be preprocessed to eliminate the noise and synchronize.
For example, performing signal preprocessing on each of the heartbeat physiological signals to obtain synchronous heartbeat physiological signals may include: noise filtering, baseline wander cancellation and/or signal alignment, etc. are performed on each of the heartbeat physiological signals.
S130, extracting a heartbeat interval sequence of each synchronous heartbeat physiological signal.
The heartbeat interval sequence is a sequence formed by time intervals between every two adjacent wave peaks in the synchronous heartbeat physiological signal.
Specifically, the time interval between every two adjacent peaks in each synchronous heartbeat physiological signal is a heartbeat interval, namely, the time interval of two heartbeats, and the sequence formed by each heartbeat interval is determined as a heartbeat interval sequence so as to reflect the heartbeat interval of the user. The heart beat interval can be used for heart rate variability analysis and can also be used as a basis for judging whether to drive fatigue.
It should be noted that, the facial heartbeat signal and the pulse signal are signals similar to sine waves, and the heartbeat interval can be obtained by directly obtaining the time interval between every two adjacent peaks, and for the synchronous epidermis heartbeat signal, the heartbeat interval of the synchronous epidermis heartbeat signal is wanted to be obtained through template matching, so as to ensure the accuracy of extracting the heartbeat interval.
And S140, carrying out weighted summation on the heartbeat interval sequences of the synchronous heartbeat physiological signals to obtain a final heartbeat interval sequence, and estimating the heart rate of the user based on the final heartbeat interval sequence.
Specifically, for each synchronous heartbeat physiological signal, determining the weight of a heartbeat interval sequence of each synchronous heartbeat physiological signal, carrying out weighted calculation on each heartbeat interval in the heartbeat interval sequence based on the weight to obtain a weighted heartbeat interval sequence, summing the weighted heartbeat interval sequences corresponding to the three synchronous heartbeat physiological signals to obtain a final heartbeat interval sequence, and estimating the heart rate of the user based on the final heartbeat interval sequence.
For example, the method of estimating the heart rate of the user based on the final heartbeat interval sequence may be estimating the heartbeat frequency of the user per minute from the final heartbeat interval sequence or estimating the heartbeat interval variation of the user in a time per minute.
According to the technical scheme, heartbeat physiological signals of a user are acquired by respectively controlling each heartbeat acquisition device; carrying out signal preprocessing on each heartbeat physiological signal to obtain synchronous heartbeat physiological signals; extracting a heartbeat interval sequence of each synchronous heartbeat physiological signal; and carrying out weighted summation on the heartbeat interval sequences of the synchronous heartbeat physiological signals to obtain a final heartbeat interval sequence, estimating the heart rate of the user based on the final heartbeat interval sequence, reducing detection errors caused by the fact that single sensors are not tightly attached, and improving the accuracy of heart rate detection in a vehicle driving scene.
Example two
Fig. 2A is a flowchart of a heart rate estimation method according to a second embodiment of the present invention, which is optimized based on the above embodiment.
As shown in fig. 2A, the method of this embodiment specifically includes the following steps:
s210, respectively controlling each heartbeat acquisition device to acquire heartbeat physiological signals of the user.
Optionally, controlling the image acquisition device to acquire a facial heartbeat signal representing the heartbeat variation of the user; controlling the wearable device to collect pulse signals of the user; and controlling the electrode patch to acquire the epidermis heartbeat signal of the user.
Wherein, when the heart beats, oxygen-containing blood is supplied to the head and the neck to drive the face to move, the face heartbeat signal can be regarded as a facial movement signal, and the face heartbeat signal is generally difficult to see by naked eyes, but can be obtained by high-precision image acquisition equipment and a computer vision technology. The change rule of the facial heartbeat signal is basically consistent with the beating rule of the heart. The pulse signal is a pulse signal conducted by the wearable device, and the heart beat and the pulse frequency of a person are consistent under normal conditions, so that the change of the heart beat can be reflected through the frequency of the pulse, and the epidermis heart beat signal is an electrocardiosignal directly conducted through the electrode patch.
S220, carrying out signal preprocessing on each heartbeat physiological signal to obtain synchronous heartbeat physiological signals.
The face heartbeat signal is subjected to signal preprocessing to obtain a synchronous face heartbeat signal; the pulse signals are subjected to signal preprocessing to obtain synchronous pulse signals, and the epidermis heartbeat signals are subjected to signal preprocessing to obtain synchronous epidermis heartbeat signals.
Optionally, step S220 may include: step 221 to step 222:
S221, filtering processing is carried out on each heartbeat physiological signal, and the heartbeat physiological signals after filtering are obtained.
Specifically, filtering processing is performed on each heartbeat physiological signal to remove noise, so as to obtain a preprocessed heartbeat physiological signal, wherein the noise comprises: ambient noise and baseline drift noise. The baseline drift noise is noise caused by low-frequency interference such as respiration of the user.
The filtering mode of filtering each heartbeat physiological signal can be wavelet change, adaptive filtering or band-pass filtering.
S222, aligning signal peaks of the filtered heartbeat physiological signals to obtain synchronous heartbeat physiological signals.
For example, since the sampling start time of each sensor for acquiring the heartbeat physiological signal has a certain error, each filtered heartbeat physiological signal is aligned to obtain each synchronous heartbeat physiological signal. For different synchronous heartbeat physiological signals, the heartbeat interval between every two adjacent wave peaks is kept within a preset error range.
S230, extracting a heartbeat interval sequence of each synchronous heartbeat physiological signal, wherein the heartbeat interval sequence is a sequence formed by time intervals between every two adjacent wave peaks in the synchronous heartbeat physiological signals.
For each synchronous heartbeat physiological signal, the time interval between every two adjacent peaks of the synchronous heartbeat physiological signal is acquired, namely the time interval is called a heartbeat interval, and a sequence formed by the heartbeat intervals in the synchronous heartbeat physiological signal is called a heartbeat interval sequence.
S240, determining the weight of the heartbeat interval sequence of each synchronous heartbeat physiological signal.
For example, the weight distribution is performed on the heartbeat interval sequence acquired and processed by each sensor to obtain the synchronous heartbeat physiological signal. The weight distribution can be determined according to the stability of heart rate detection of each sensor, for example, a larger weight is distributed to the synchronous heartbeat physiological signals corresponding to the sensor with the smallest standard deviation, a smaller weight is distributed to the synchronous heartbeat physiological signals corresponding to the sensor with the largest standard deviation, and a centered weight is distributed to the synchronous heartbeat physiological signals corresponding to the sensor with the centered standard deviation. Or can be determined by a table look-up method based on the ratio of the standard deviation to the average number of the heartbeat interval sequence. The ratio of the weight coefficient of the synchronous heartbeat physiological signal in the sum of the weight coefficients of the three synchronous heartbeat physiological signals can also be determined according to the ratio of the weight coefficient of the synchronous heartbeat physiological signal in the sum of the weight coefficients of the three synchronous heartbeat physiological signals.
S250, for each heartbeat physiological signal, determining the product of the weight of the heartbeat interval sequence and each heartbeat interval in the heartbeat interval sequence as a weighted heartbeat interval sequence of the synchronous heartbeat physiological signal.
Specifically, for the heartbeat interval sequence corresponding to each synchronous heartbeat physiological signal, multiplying each heartbeat interval in the heartbeat interval sequence by the weight of the heartbeat interval sequence to obtain a weighted heartbeat interval sequence of the synchronous heartbeat physiological signal.
For example, if the heartbeat interval sequence of the synchronous facial heartbeat signal is R 1＝r1,r2,…,ri,…,rn and the weight of the heartbeat interval sequence corresponding to the synchronous facial heartbeat signal is a, the weighted heartbeat interval sequence of the synchronous facial heartbeat signal is R' 1＝R1×a＝ar1,ar2,…,ari,…,arn. If the heartbeat interval sequence of the synchronous pulse signal is M 1＝m1,m2,…,mi,…,mn and the weight of the heartbeat interval sequence corresponding to the synchronous pulse signal is b, the weighted heartbeat interval sequence of the synchronous facial heartbeat signal is M' 1＝bm1,bm2,…,bmi,…,bmn. If the heartbeat interval sequence of the synchronous epidermis heartbeat signal is M 1＝m1,m2,…,mi,…,mn and the weight of the heartbeat interval sequence corresponding to the synchronous epidermis heartbeat signal is c, the weighted heartbeat interval sequence of the synchronous epidermis heartbeat signal is L 1＝cl1,cl2,…,cli,…,cln.
And S260, summing the weighted heartbeat intervals corresponding to the weighted heartbeat interval sequences to obtain a final heartbeat interval sequence, and estimating the heart rate of the user based on the final heartbeat interval sequence.
Illustratively, the weighted heartbeat interval sequence of the synchronized facial heartbeat signal is R' 1＝AR1＝Ar1,Ar2,…,Ari,…,Arn; the weighted heartbeat interval sequence of the synchronous pulse signals is M' 1＝BM1Bm1,Bm2,…,Bmi,…,Bmn; the weighted epidermis heartbeat interval sequence of the synchronous pulse signals is L' 1＝CL1＝Cl1,Cl2,…,Cli,…,Cln, corresponding weighted heartbeat intervals in the weighted heartbeat interval sequences are summed to obtain a final heartbeat interval sequence as
The final heartbeat interval sequence can be stored through a data storage module and then the stored data is called by the upper computer for playback; the heart rate of the user can be estimated through the final heart beat interval sequence, and the heart beat signal and the heart rate can be displayed in real time through the connection of the wireless module and the car machine or the mobile phone. The power management module uses rechargeable battery to supply power to the device, shows current electric quantity through the pilot lamp, reminds the user to change the battery or charge when the electric quantity is insufficient.
According to the technical scheme, heartbeat physiological signals of a user are acquired by respectively controlling each heartbeat acquisition device; performing signal preprocessing on each heartbeat physiological signal to obtain synchronous heartbeat physiological signals; extracting a heartbeat interval sequence of each synchronous heartbeat physiological signal, wherein the heartbeat interval sequence is a sequence formed by time intervals between every two adjacent wave peaks in the synchronous heartbeat physiological signals; and carrying out weighted summation on the heartbeat interval sequences of the synchronous heartbeat physiological signals to obtain a final heartbeat interval sequence, estimating the heart rate of the user based on the final heartbeat interval sequence, and determining the heart rate of the user according to the distribution weight of each sensor, so that the detection error caused by the fact that single sensors are not tightly attached is reduced, and the accuracy of heart rate detection in a vehicle driving scene is improved.
Optionally, the filtering processing is performed on each heartbeat physiological signal to obtain a filtered heartbeat physiological signal, which includes:
Eliminating baseline drift noise and environmental noise of the facial heartbeat signal through wavelet transformation to obtain a filtered facial heartbeat signal;
The baseline drift noise of the pulse signals is eliminated through self-adaptive filtering, the environmental noise of the pulse signals with the baseline drift eliminated is removed through band-pass filtering, and the pulse signals after filtering are obtained;
And eliminating baseline drift noise of the epidermis heartbeat signal through self-adaptive filtering, and eliminating environmental noise of the epidermis heartbeat signal with the baseline drift noise through band-pass filtering to obtain a filtered epidermis heartbeat signal.
Specifically, as shown in fig. 2B, the process of filtering each of the heartbeat physiological signals to obtain a filtered heartbeat physiological signal may be: decomposing the face video acquired by the image sensor, extracting an interested region, extracting a face heartbeat signal from the face video, performing wavelet transformation, eliminating baseline drift noise and environmental noise of the face heartbeat signal, obtaining a filtered face heartbeat signal, and selecting the wavelet decomposition layer number within a frequency of 0.5-2Hz by considering that the normal heart rate of a human body is 60-100/min.
And for pulse signals acquired by the wearable equipment, baseline drift noise of the pulse signals is eliminated through self-adaptive filtering, and environmental noise of the pulse signals with the baseline drift eliminated is eliminated through band-pass filtering, so that the pulse signals after filtering are obtained. The bandpass frequency of the bandpass filtering may be 0.5-2Hz.
And for the epidermis heartbeat physiological signals collected by the patch, eliminating baseline drift noise of the epidermis heartbeat physiological signals through self-adaptive filtering, and eliminating environmental noise of the epidermis heartbeat physiological signals with the baseline drift noise eliminated through band-pass filtering to obtain the filtered epidermis heartbeat physiological signals. The bandpass frequency of the bandpass filtering may be 0.5-2Hz.
Optionally, determining the weight of the heartbeat interval sequence of each synchronous heartbeat physiological signal includes:
For each heartbeat interval sequence of the synchronous heartbeat physiological signals, determining the ratio of the standard deviation and the average value of each heartbeat interval in the heartbeat interval sequence, and determining the weight coefficient corresponding to the ratio by a table lookup method;
determining the sum of the weight coefficients corresponding to the heartbeat interval sequences of the synchronous heartbeat physiological signals as a total weight coefficient;
and determining the ratio of the weight coefficient to the total weight coefficient as the weight of the heartbeat interval sequence of the synchronous heartbeat physiological signal.
Specifically, for each heartbeat interval sequence of the synchronous heartbeat physiological signals, each heartbeat interval in the heartbeat interval sequence is acquired, a standard deviation sigma and an average value avg of each heartbeat interval are calculated, and the ratio of the standard deviation to the average value is determined to be W= (sigma/avg). And determining a weight coefficient corresponding to the ratio by a table lookup method, for example, the weight coefficient corresponding to the heartbeat interval sequence of the synchronous facial heartbeat signal is a, the weight coefficient corresponding to the heartbeat interval sequence of the synchronous pulse signal is b, and the weight coefficient corresponding to the heartbeat interval sequence of the synchronous epidermis heartbeat signal is c. Determining the sum of the weight coefficients corresponding to the heartbeat interval sequences of the synchronous heartbeat physiological signals as a total weight coefficient, namely, the total weight coefficient is a+b+c; determining the ratio of the weight coefficient to the total weight coefficient as the weight of the heartbeat interval sequence of the synchronous heartbeat physiological signal, namely:
the corresponding weight of the heartbeat interval sequence of the synchronous face heartbeat signal is The weight corresponding to the heartbeat interval sequence of the synchronous pulse signals is/>The weight corresponding to the heartbeat interval sequence of the synchronous epidermis heartbeat signal is/>
For example, the manner of determining the weight coefficient corresponding to the ratio by the table look-up method may be: when the ratio W 1,W1 corresponding to the facial heartbeat signals acquired by the image acquisition equipment is 0-0.1, the weight coefficient is determined to be a 1;W1 to be 0.1-0.2 through a table lookup method, and when the weight coefficient is determined to be a 2;W1 to be more than 0.2 through the table lookup method, the weight coefficient is determined to be a 3 through the table lookup method. The specific numerical value of the weight is obtained through calibration by methods such as test or index simulation.
When the ratio W 2,W2 corresponding to the pulse signals acquired by the wearable equipment is 0-0.1, the weight coefficient is determined to be b 1;W2 to be 0.1-0.2 through a table lookup method, and when the weight coefficient is determined to be b 2;W2 to be more than 0.2 through the table lookup method, the weight coefficient is determined to be b 3 through the table lookup method.
When the ratio W 3,W3 corresponding to the epidermis heartbeat signals acquired by the wearable equipment is 0-0.1, the weight coefficient is determined to be c 1;W3 to be 0.1-0.2 through a table lookup method, and when the weight coefficient is determined to be c 2;W3 to be more than 0.2 through the table lookup method, the weight coefficient is determined to be c 3 through the table lookup method.
Example III
Fig. 3 is a schematic structural diagram of a heart rate estimation device according to a third embodiment of the present invention. The embodiment may be suitable for the situation of estimating the heart rate of the user in real time in the vehicle driving scenario, and the device may be implemented in a software and/or hardware manner, and may be integrated in any device that provides the function of heart rate estimation, as shown in fig. 3, where the device for heart rate estimation specifically includes: the acquisition module 310, the preprocessing module 320, the extraction module 330, and the estimation module 340.
The acquisition module 310 is configured to control each heartbeat acquisition device to acquire a heartbeat physiological signal of the user;
the preprocessing module 320 is configured to perform signal preprocessing on each of the heartbeat physiological signals to obtain synchronous heartbeat physiological signals;
The extracting module 330 is configured to extract a heartbeat interval sequence of each of the synchronous heartbeat physiological signals, where the heartbeat interval sequence is a sequence formed by a time interval between every two adjacent peaks in the synchronous heartbeat physiological signals;
and the estimating module 340 is configured to perform weighted summation on the heartbeat interval sequences of the synchronous heartbeat physiological signals to obtain a final heartbeat interval sequence, and estimate the heart rate of the user based on the final heartbeat interval sequence.
Optionally, the acquisition module 310 includes:
the first acquisition unit is used for controlling the image acquisition equipment to acquire facial heartbeat signals representing heartbeat changes of the user;
the second acquisition unit is used for controlling the wearable device to acquire pulse signals of the user;
and the third acquisition unit is used for controlling the electrode patch to acquire the epidermis heartbeat signals of the user.
Optionally, the preprocessing module 320 includes:
The filtering unit is used for carrying out filtering processing on each heartbeat physiological signal to obtain a filtered heartbeat physiological signal;
And the synchronization unit is used for carrying out signal wave crest alignment on the filtered heartbeat physiological signals to obtain synchronous heartbeat physiological signals.
Optionally, the filtering unit is specifically configured to:
Eliminating baseline drift noise and environmental noise of the facial heartbeat signal through wavelet transformation to obtain a filtered facial heartbeat signal;
The baseline drift noise of the pulse signals is eliminated through self-adaptive filtering, the environmental noise of the pulse signals with the baseline drift eliminated is removed through band-pass filtering, and the pulse signals after filtering are obtained;
And eliminating baseline drift noise of the epidermis heartbeat signal through self-adaptive filtering, and eliminating environmental noise of the epidermis heartbeat signal with the baseline drift noise through band-pass filtering to obtain a filtered epidermis heartbeat signal.
Optionally, the estimation module includes:
the weight determining unit is used for determining the weight of the heartbeat interval sequence corresponding to each synchronous heartbeat physiological signal;
A weighting unit, configured to determine, for each of the synchronized heartbeat physiological signals, a product of a weight of the heartbeat interval sequence and each heartbeat interval in the heartbeat interval sequence as a weighted heartbeat interval sequence of the synchronized heartbeat physiological signal;
and the heart rate determining unit is used for summing the weighted heart beat intervals corresponding to the weighted heart beat interval sequences to obtain a final heart beat interval sequence.
Optionally, the weight determining unit is specifically configured to:
For each heartbeat interval sequence of the synchronous heartbeat physiological signals, determining the ratio of the standard deviation and the average value of each heartbeat interval in the heartbeat interval sequence, and determining the weight coefficient corresponding to the ratio by a table lookup method;
determining the sum of the weight coefficients corresponding to the heartbeat interval sequences of the synchronous heartbeat physiological signals as a total weight coefficient;
and determining the ratio of the weight coefficient to the total weight coefficient as the weight of the heartbeat interval sequence of the synchronous heartbeat physiological signal.
The heart rate estimation method provided by any embodiment of the invention can be executed by the product, and has the corresponding functional modules and beneficial effects of the execution method.
Example IV
Fig. 4 is a schematic structural diagram of a heart rate estimation device according to a fourth embodiment of the present invention. Fig. 4 shows a block diagram of an exemplary heart rate estimation device 12 suitable for use in implementing embodiments of the present invention. The heart rate estimation device 12 shown in fig. 4 is only an example and should not be construed as limiting the functionality and scope of use of the embodiments of the invention.
As shown in fig. 4, the heart rate estimation device 12 is in the form of a general purpose computing device. Components of heart rate estimation device 12 may include, but are not limited to: one or more processors or processors 16, a system memory 28, a bus 18 that connects the various system components, including the system memory 28 and the processors 16.
Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, and a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, micro channel architecture (MAC) bus, enhanced ISA bus, video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.
Heart rate estimation device 12 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by heart rate estimation device 12 and includes both volatile and non-volatile media, removable and non-removable media.
The system memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) 30 and/or cache memory 32. Heart rate estimation device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from or write to non-removable, nonvolatile magnetic media (not shown in FIG. 4, commonly referred to as a "hard disk drive"). Although not shown in fig. 4, a magnetic disk drive for reading from and writing to a removable non-volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading from or writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In such cases, each drive may be coupled to bus 18 through one or more data medium interfaces. Memory 28 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 embodiments of the invention.
A program/utility 40 having a set (at least one) of program modules 42 may be stored in, for example, memory 28, such program modules 42 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 42 generally perform the functions and/or methods of the embodiments described herein.
The heart rate estimation device 12 may also be in communication with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), one or more devices that enable a user to interact with the heart rate estimation device 12, and/or any device (e.g., network card, modem, etc.) that enables the heart rate estimation device 12 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 22. In addition, in the heart rate estimation device 12 of the present embodiment, the display 24 is not present as a separate body but is embedded in the mirror surface, and the display surface of the display 24 and the mirror surface are visually integrated when the display surface of the display 24 is not displayed. Also, heart rate estimation device 12 may communicate with one or more networks, such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the internet, via network adapter 20. As shown, the network adapter 20 communicates with other modules of the heart rate estimation device 12 via the bus 18. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in connection with heart rate estimation device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
The processor 16 executes various functional applications and data processing by running programs stored in the system memory 28, for example, implementing the heart rate estimation method provided by the embodiments of the present invention: respectively controlling each heartbeat acquisition device to acquire heartbeat physiological signals of a user; performing signal preprocessing on each heartbeat physiological signal to obtain synchronous heartbeat physiological signals; extracting a heartbeat interval sequence of each synchronous heartbeat physiological signal, wherein the heartbeat interval sequence is a sequence formed by time intervals between every two adjacent wave peaks in the synchronous heartbeat physiological signals; and carrying out weighted summation on the heartbeat interval sequences of the synchronous heartbeat physiological signals to obtain a final heartbeat interval sequence, and estimating the heart rate of the user based on the final heartbeat interval sequence.
Example five
Fig. 5 is a schematic structural diagram of a heart rate estimation system according to a fifth embodiment of the present invention. The heart rate detection system comprises: a plurality of heartbeat acquisition devices and a heart rate estimation device as in example four connected to each of the heartbeat acquisition devices; the heartbeat acquisition device includes: image acquisition equipment, wearing equipment and electrode patches;
The image acquisition device is used for acquiring facial heartbeat signals representing heartbeat changes of the user based on the control of the heart rate estimation device;
The wearable device is used for acquiring pulse signals of the user based on the control of the heart rate estimation device;
The electrode patch is used for acquiring epidermis heartbeat signals of the user based on the control of the heart rate estimation device;
The heart rate estimation device is used for respectively controlling each heart rate acquisition device to acquire heart rate physiological signals of a user; performing signal preprocessing on each heartbeat physiological signal to obtain synchronous heartbeat physiological signals; extracting a heartbeat interval sequence of each synchronous heartbeat physiological signal, wherein the heartbeat interval sequence is a sequence formed by time intervals between every two adjacent wave peaks in the synchronous heartbeat physiological signals; and carrying out weighted summation on the heartbeat interval sequences of the synchronous heartbeat physiological signals to obtain a final heartbeat interval sequence, and estimating the heart rate of the user based on the final heartbeat interval sequence.
According to the embodiment of the invention, the heartbeat physiological signals of the user are acquired through the image acquisition equipment, the wearing equipment and the electrode patch, so that the heart rate of the user can be estimated by combining the facial heartbeat signals, the pulse signals and the epidermis heartbeat signals representing the electrocardio changes, the detection error caused by the fact that the single sensor is not tightly attached is reduced, and the accuracy of heart rate detection in a vehicle driving scene is improved.
Example six
A sixth embodiment of the present application provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a heart rate estimation method as provided by all the embodiments of the present application: respectively controlling each heartbeat acquisition device to acquire heartbeat physiological signals of a user; performing signal preprocessing on each heartbeat physiological signal to obtain synchronous heartbeat physiological signals; extracting a heartbeat interval sequence of each synchronous heartbeat physiological signal, wherein the heartbeat interval sequence is a sequence formed by time intervals between every two adjacent wave peaks in the synchronous heartbeat physiological signals; and carrying out weighted summation on the heartbeat interval sequences of the synchronous heartbeat physiological signals to obtain a final heartbeat interval sequence, and estimating the heart rate of the user based on the final heartbeat interval sequence.
Any combination of one or more computer readable media may be employed. 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 (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 computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations of the present invention may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
Note that the above is only a preferred embodiment of the present invention and the technical principle applied. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, while the invention has been described in connection with the above embodiments, the invention is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the invention, which is set forth in the following claims.
Claims (8)
1. A heart rate estimation method, characterized by being applied to a heart rate estimation system, the heart rate estimation system comprising a plurality of heart beat acquisition devices, the heart beat acquisition devices comprising: image acquisition equipment, wearing equipment and electrode paster, wherein, electrode paster sets up in steering wheel grab handle department or manual gear, the method includes:
respectively controlling each heartbeat acquisition device to acquire heartbeat physiological signals of a user;
Performing signal preprocessing on each heartbeat physiological signal to obtain synchronous heartbeat physiological signals;
Extracting a heartbeat interval sequence of each synchronous heartbeat physiological signal, wherein the heartbeat interval sequence is a sequence formed by time intervals between every two adjacent wave peaks in the synchronous heartbeat physiological signals;
Carrying out weighted summation on the heartbeat interval sequences of the synchronous heartbeat physiological signals to obtain a final heartbeat interval sequence, and estimating the heart rate of the user based on the final heartbeat interval sequence;
The step of carrying out weighted summation on the heartbeat interval sequences of the synchronous heartbeat physiological signals to obtain a final heartbeat interval sequence comprises the following steps:
Determining the weight of a heartbeat interval sequence corresponding to each synchronous heartbeat physiological signal;
for each heartbeat physiological signal, determining the product of the weight of the heartbeat interval sequence and each heartbeat interval in the heartbeat interval sequence as a weighted heartbeat interval sequence of the synchronous heartbeat physiological signal;
summing the weighted heartbeat intervals corresponding to the weighted heartbeat interval sequences to obtain a final heartbeat interval sequence;
The determining the weight of the heartbeat interval sequence of each synchronous heartbeat physiological signal comprises the following steps:
For each heartbeat interval sequence of the synchronous heartbeat physiological signals, determining the ratio of the standard deviation and the average value of each heartbeat interval in the heartbeat interval sequence, and determining the weight coefficient corresponding to the ratio by a table lookup method;
determining the sum of the weight coefficients corresponding to the heartbeat interval sequences of the synchronous heartbeat physiological signals as a total weight coefficient;
and determining the ratio of the weight coefficient to the total weight coefficient as the weight of the heartbeat interval sequence of the synchronous heartbeat physiological signal.
2. The method of claim 1, wherein acquiring the heartbeat physiological signal of the user by each of the heartbeat acquisition devices, respectively, comprises:
controlling the image acquisition equipment to acquire facial heartbeat signals representing heartbeat changes of the user;
controlling the wearable device to collect pulse signals of the user;
and controlling the electrode patch to acquire the epidermis heartbeat signal of the user.
3. The method according to claim 2, wherein the performing signal preprocessing on each of the heartbeat physiological signals to obtain each synchronous heartbeat physiological signal includes:
Filtering each heartbeat physiological signal to obtain a filtered heartbeat physiological signal;
And carrying out signal wave crest alignment on each heartbeat physiological signal after filtering to obtain each synchronous heartbeat physiological signal.
4. A method according to claim 3, wherein said filtering each of said heartbeat physiological signals to obtain a filtered heartbeat physiological signal comprises:
Eliminating baseline drift noise and environmental noise of the facial heartbeat signal through wavelet transformation to obtain a filtered facial heartbeat signal;
The baseline drift noise of the pulse signals is eliminated through self-adaptive filtering, the environmental noise of the pulse signals with the baseline drift eliminated is removed through band-pass filtering, and the pulse signals after filtering are obtained;
And eliminating baseline drift noise of the epidermis heartbeat signal through self-adaptive filtering, and eliminating environmental noise of the epidermis heartbeat signal with the baseline drift noise through band-pass filtering to obtain a filtered epidermis heartbeat signal.
5. A heart rate estimation device, comprising:
the acquisition module is used for respectively controlling each heartbeat acquisition device to acquire heartbeat physiological signals of the user; the heartbeat acquisition equipment comprises image acquisition equipment, wearing equipment and electrode patches, wherein the electrode patches are arranged at a steering wheel grab handle or on a manual gear;
The preprocessing module is used for carrying out signal preprocessing on each heartbeat physiological signal to obtain synchronous heartbeat physiological signals;
The extraction module is used for extracting a heartbeat interval sequence of each synchronous heartbeat physiological signal, wherein the heartbeat interval sequence is a sequence formed by time intervals between every two adjacent wave peaks in the synchronous heartbeat physiological signals;
And the estimation module is used for carrying out weighted summation on the heartbeat interval sequences of the synchronous heartbeat physiological signals to obtain a final heartbeat interval sequence, and estimating the heart rate of the user based on the final heartbeat interval sequence.
The estimation module includes: the weight determining unit is used for determining the weight of the heartbeat interval sequence corresponding to each synchronous heartbeat physiological signal; a weighting unit, configured to determine, for each of the synchronized heartbeat physiological signals, a product of a weight of the heartbeat interval sequence and each heartbeat interval in the heartbeat interval sequence as a weighted heartbeat interval sequence of the synchronized heartbeat physiological signal; and the heart rate determining unit is used for summing the weighted heart beat intervals corresponding to the weighted heart beat interval sequences to obtain a final heart beat interval sequence.
The weight determining unit is specifically configured to: for each heartbeat interval sequence of the synchronous heartbeat physiological signals, determining the ratio of the standard deviation and the average value of each heartbeat interval in the heartbeat interval sequence, and determining the weight coefficient corresponding to the ratio by a table lookup method; determining the sum of the weight coefficients corresponding to the heartbeat interval sequences of the synchronous heartbeat physiological signals as a total weight coefficient; determining the ratio of the weight coefficient to the total weight coefficient as the weight of the heartbeat interval sequence of the synchronous heartbeat physiological signal
6. Heart rate estimation device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the heart rate estimation method according to any of claims 1-4 when executing the program.
7. A heart rate estimation system, the heart rate estimation system comprising: a plurality of heartbeat acquisition devices and a heart rate estimation device as claimed in claim 6 connected to each of the heartbeat acquisition devices; the heartbeat acquisition device includes: image acquisition equipment, wearing equipment and electrode patches; the image acquisition device is used for acquiring facial heartbeat signals representing heartbeat changes of the user based on the control of the heart rate estimation device;
The wearable device is used for acquiring pulse signals of the user based on the control of the heart rate estimation device;
the electrode patch is used for acquiring epidermis heartbeat signals of the user based on control of the heart rate estimation device.
8. A computer readable storage medium having stored thereon a computer program, which when executed by a processor implements a heart rate estimation method as claimed in any one of claims 1-4.
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