CN116421163A - Vital sign detection method and device - Google Patents

Disclosure of Invention

The embodiment of the application provides a vital sign detection method and device, so that whether vital signs exist in a vital sign region to be detected can be accurately determined.

In a first aspect, an embodiment of the present application provides a vital sign detection method, including:

acquiring data of a vital sign region to be detected based on a radar sensor;

performing data processing on the data to obtain a distance-energy mapping relation of the moving target after the static target in the vital sign region to be detected is removed, and obtaining a distance-phase mapping relation of the moving target;

Determining at least one specified parameter corresponding to the distance according to the distance-energy mapping relation and the distance-phase mapping relation; wherein the specified parameters include at least a high frequency signal duty cycle;

inputting the specified parameters corresponding to the at least one distance into a pre-constructed support vector machine model to obtain a vital sign detection result output by the support vector machine model;

the support vector machine model is used for judging whether a person to be detected is in an apnea state of the vital sign area to be detected or not in the vital sign area to be detected.

Compared with the prior art, the method and the device have the advantages that by determining the specified parameter corresponding to the at least one distance and further utilizing the determined specified parameter as the input of the support vector machine model, whether vital signs exist in the vital sign area to be detected can be accurately determined according to the specified parameter at least comprising the high-frequency signal duty ratio. Meanwhile, the support vector machine model can be used for carrying out on-site learning and recognition on application scenes in the model training process, so that the obtained support vector machine model can obtain more accurate output results in the practical application process after model training is completed.

In one possible design, the specified parameters may further include one or more of the following:

respiratory frequency band energy intensity, heart rate frequency band maximum energy intensity, heart rate frequency band energy intensity variance, heart rate frequency band maximum energy intensity variance.

According to the method and the device, one or more parameters of respiratory frequency band energy intensity, heart rate frequency band maximum energy intensity, heart rate frequency band energy intensity variance and heart rate frequency band maximum energy intensity variance are defined as specified parameters, and whether vital signs exist in a vital sign region to be detected or not can be accurately determined by using the defined specified parameters.

In one possible design, the support vector machine model is constructed by:

acquiring specified parameters of at least one distance sample and labeling labels corresponding to the at least one distance sample, wherein the labeling labels comprise that the person to be detected is in the vital sign region to be detected and the person to be detected is not in the vital sign region to be detected;

inputting specified parameters of the at least one distance sample into the support vector machine model to enable the support vector machine model to output a prediction tag of the at least one distance sample;

The support vector machine model is trained based on the losses between the predictive labels and the labeling labels.

According to the method and the device, the support vector machine model is trained by using the specified parameters of at least one distance sample and the labeling labels corresponding to at least one distance sample, and the trained support vector machine model can be enabled to be more accurate in recognizing whether vital signs exist in the vital sign region to be detected or not through the specified parameters of each distance sample.

In one possible design, when the radar sensor collects data of a vital sign region to be detected, a situation of sampling time difference exists, and a static target in the vital sign region to be detected is removed by the following method:

obtaining a first numerical value based on the energy intensity and the phase value corresponding to the current sampling moment; and obtaining a second value based on the energy intensity and the phase value corresponding to the previous time of the current sampling time;

performing modular operation on the first numerical value and the second numerical value respectively;

and removing the static target in the vital sign region to be detected by utilizing the difference value between the modulus operation result of the first numerical value and the modulus operation result of the second numerical value.

According to the method, the first numerical value and the second numerical value are subjected to modular operation, then the static target is removed based on the difference value after the modular operation, and then the problem of large energy difference caused by sampling time difference can be reduced, the energy intensity is reduced, and the static target can be accurately removed.

In one possible design, the radar sensor collects data of the vital sign area to be detected by: a multi-antenna transmission and multi-antenna reception mode;

the method further comprises the steps of:

and carrying out wave beam synthesis on the data received by a plurality of receiving ends of the radar sensor.

According to the method, data are collected in a multi-antenna transmitting and multi-antenna receiving mode, then beam synthesis is carried out on the data received by the multiple receiving ends, and then the view field angle of the receiving ends of the radar sensor can be reduced. And accurately acquiring data of the vital sign region to be detected through the reduced view field angle.

In a second aspect, an embodiment of the present application provides a vital sign detection device, the device comprising:

the acquisition module is used for acquiring data of a vital sign area to be detected based on the radar sensor;

the mapping relation determining module is used for carrying out data processing on the data to obtain a distance-energy mapping relation of the moving target after the static target in the vital sign region to be detected is removed, and obtaining a distance-phase mapping relation of the moving target;

The parameter determining module is used for determining at least one specified parameter corresponding to the distance according to the distance-energy mapping relation and the distance-phase mapping relation; wherein the specified parameters include at least a high frequency signal duty cycle;

the detection result module is used for inputting the specified parameter corresponding to the at least one distance into a pre-constructed support vector machine model to obtain a vital sign detection result output by the support vector machine model;

the support vector machine model is used for judging whether a person to be detected is in an apnea state of the vital sign area to be detected or not in the vital sign area to be detected.

In one possible design, the specified parameters may further include one or more of the following:

respiratory frequency band energy intensity, heart rate frequency band maximum energy intensity, heart rate frequency band energy intensity variance, heart rate frequency band maximum energy intensity variance.

In one possible design, the support vector machine model is constructed by:

acquiring specified parameters of at least one distance sample and labeling labels corresponding to the at least one distance sample, wherein the labeling labels comprise that the person to be detected is in the vital sign region to be detected and the person to be detected is not in the vital sign region to be detected;

Inputting specified parameters of the at least one distance sample into the support vector machine model to enable the support vector machine model to output a prediction tag of the at least one distance sample;

the support vector machine model is trained based on the losses between the predictive labels and the labeling labels.

In one possible design, when the radar sensor collects data of a vital sign area to be detected, a sampling time difference exists, and the mapping relation determining module removes a static target in the vital sign area to be detected by the following method:

obtaining a first numerical value based on the energy intensity and the phase value corresponding to the current sampling moment; and obtaining a second value based on the energy intensity and the phase value corresponding to the previous time of the current sampling time;

performing modular operation on the first numerical value and the second numerical value respectively;

and removing the static target in the vital sign region to be detected by utilizing the difference value between the modulus operation result of the first numerical value and the modulus operation result of the second numerical value.

In one possible design, the radar sensor in the acquisition module acquires data of the vital sign region to be detected by: a multi-antenna transmission and multi-antenna reception mode;

The apparatus further comprises:

and carrying out wave beam synthesis on the data received by a plurality of receiving ends of the radar sensor.

In a third aspect, an embodiment of the present application further provides an electronic device, including:

a processor;

a memory for storing the processor-executable instructions;

wherein the processor is configured to execute the instructions to implement any of the methods as provided in the first aspect of the present application.

In a fourth aspect, an embodiment of the present application also provides a computer-readable storage medium, which when executed by a processor of an electronic device, causes the electronic device to perform any of the methods as provided in the first aspect of the present application.

In a fifth aspect, an embodiment of the present application provides a computer program product comprising computer programs/instructions which when executed by a processor implement any of the methods as provided in the first aspect of the present application.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Detailed Description

In order to enable those skilled in the art to better understand the technical solutions of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

It should be noted that the terms "first," "second," and the like in the description and claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that embodiments of the present application described herein may be implemented in other sequences than those illustrated or otherwise described herein. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present application as detailed in the accompanying claims.

With the improvement of living standard, people begin to pay attention to their health status, especially to community care institutions, and more attention is paid to the monitoring of the health of the elderly. Vital signs are taken as an important index for measuring health conditions, and currently, in the process of health monitoring of a human body, a contact instrument is generally adopted to collect vital sign parameters such as respiration, heart rate and the like of the human body. But for most users the contact instrument is costly and complex to operate.

Therefore, the method and the device for detecting the vital signs are provided, after the radar sensor collects data and processes the data, the specified parameter corresponding to at least one distance is determined, and then the determined specified parameter is used as the input of a support vector machine model, so that whether the vital signs exist in the vital sign area to be detected or not can be accurately determined according to the specified parameter at least comprising the high-frequency signal duty ratio.

In order to further explain the technical solutions provided in the embodiments of the present application, the following details are described with reference to the accompanying drawings and the detailed description. Although the embodiments of the present application provide the method operational steps as shown in the following embodiments or figures, more or fewer operational steps may be included in the method, either on a routine or non-inventive basis. In steps where there is logically no necessary causal relationship, the execution order of the steps is not limited to the execution order provided by the embodiments of the present application.

The invention concept of the application is that when vital signs are detected, a single delay canceller is used for filtering static targets in the process of acquiring data by the radar sensor, so that the influence of strong reflection targets in a vital sign area to be detected on distance measurement can be reduced. However, in the above manner, the result may be wrong for both cases that the person to be detected is in the vital sign region to be detected and that the person to be detected is not in the vital sign region to be detected.

Taking the vital sign region to be detected as a bed and taking the person to be detected as a human body for illustration, as shown in fig. 1a, when the person breathes normally, the energy intensity corresponding to the acquired distance parameter has an obvious peak value, as shown in fig. 1b, and when the person pauses breathing, the peak value of the energy intensity corresponding to the acquired distance parameter can be greatly reduced. And as shown in fig. 1c, when a person is not in the vital sign area to be detected, the peak value of the energy intensity of the reflected signal corresponding to the acquired bed is close to the peak value of the energy intensity in fig. 1b, so that misjudgment of the result may occur when the person is judged to be in the apnea state and the person is not in the vital sign area to be detected.

In order to solve the above problems, when training the support vector machine model, the two situations that the person to be detected is in the vital sign region to be detected and the person to be detected is not in the vital sign region to be detected are learned, so as to obtain the corresponding decision function, and thus the two situations that the person to be detected is in the vital sign region to be detected and the person to be detected is not in the vital sign region to be detected can be accurately distinguished. Further, the training process can be more accurate by extracting more characteristic parameters.

The scheme of the application is described in the following three parts of determining the designated parameters, supporting vector machine model training and vital sign detection.

1. Determination of specified parameters

In the process of acquiring data by using the radar sensor to detect vital signs, carrying out one-dimensional Fourier transform and data processing of static target removal on a wave signal received by a receiving end of the radar sensor to obtain a distance-energy mapping relation. And then carrying out phase extraction and phase unwrapping processing on each distance to obtain a distance-phase mapping relation.

By way of example, assume that a one-dimensional fourier transform is performed on the first wave signal of each frame acquired by the radar sensor, the number of points of the fourier transform is 80, and 256 frames of data are cumulatively detected. Then static target removal is performed through the front and rear frame data, and 256 frame data are accumulated, so as to obtain a distance-energy mapping relationship as shown in fig. 1 a. And carrying out phase extraction on each distance, and carrying out phase unwrapping to obtain a distance-phase mapping relation of 80 x 256. For example, phase variations produced by heart rate and respiration may be included, and the phase of heart rate and respiration may be distinguished by an infinite impulse response (Infinite Impulse Response, IIR) filter due to the different frequency bands in which they are located.

As can be seen from the above description, after determining the distance-energy mapping relationship and the distance-phase mapping relationship, determining at least one specified parameter corresponding to the distance according to the distance-energy mapping relationship and the distance-phase mapping relationship. Wherein the specified parameters may include one or more of the following:

respiratory frequency band energy intensity BreathPower, heart rate frequency band energy intensity headpower, heart rate frequency band maximum energy intensity headidx, heart rate frequency band energy intensity variance headpowervar, heart rate frequency band maximum energy intensity variance headidxvar, high frequency signal duty cycle HighSigProp.

Exemplary, as shown in fig. 2a, the heart rate frequency band-energy mapping relationship corresponding to the point before the energy intensity peak point in fig. 1a is shown when the person to be detected is not in the vital sign region to be detected. As shown in fig. 2b, the heart rate frequency band-energy mapping relationship corresponding to the energy intensity peak point in fig. 1a is shown when the person to be detected is not in the vital sign region to be detected. As shown in fig. 2c, the heart rate frequency band-energy mapping relationship corresponding to the point subsequent to the energy intensity peak point in fig. 1a is shown when the person to be detected is not in the vital sign region to be detected.

Similarly, as shown in fig. 3a, the heart rate frequency band-energy mapping relationship corresponding to the point before the energy intensity peak point in fig. 1a is shown in the case that the person to be detected is in the apnea state in the vital sign region to be detected. As shown in fig. 3b, the heart rate frequency band-energy mapping relationship corresponding to the energy intensity peak point in fig. 1a is shown in the case that the person to be detected is in an apnea state in the vital sign region to be detected. As shown in fig. 3c, the heart rate frequency band-energy mapping relationship corresponding to the point subsequent to the energy intensity peak point in fig. 1a is shown for the case that the person to be detected is in an apnea state in the vital sign region to be detected.

In the process of detecting vital signs, even if a person to be detected is in a breath holding state, the person to be detected has weak breath, so that the energy intensity of the respiratory frequency band can be used as a specified parameter. When the person to be tested is in a breath holding state, breathing is weakened, but the heartbeat is not stopped, so that the energy intensity of the heart rate frequency band can be used as a specified parameter. And because the detected heart rate frequency band still has certain noise signals under the condition that the person to be detected is not in the vital sign region to be detected, the energy intensities and the maximum energy intensity in the heart rate frequency band are not in sequence, and therefore the disorder problem can be embodied through variance.

Optionally, according to the distance-phase mapping relation and the IIR filter, obtaining the phase change in the heart rate frequency band of any distance, then obtaining the power change through fourier transformation, and obtaining the final energy intensity heart power through accumulation of the energy intensity in the frequency band. HeartIdx is determined by the position of the highest point after Fourier transformation. BreathPower can refer to the HeartPower determination process, except that the frequency bands are different. The determination of HeartPowerVar and HeartIdxVar may be: defining a buff of 20 frames of data, storing HeartPowerBuff, heartIdxBuff of HeartPower and HeartIdx of the first 20 frames of the current moment, and calculating HeartPowerVar, heartIdxVar by a variance formula.

As shown in fig. 4a, since the energy intensity corresponding to the high frequency part (greater than 2 Hz) in the phase change is small compared with the total energy intensity in the case that the person to be detected is in the apnea of the vital sign region to be detected. However, as shown in fig. 4b, in the case where the person to be detected is not in the vital sign region to be detected, the energy intensity corresponding to the high frequency part (greater than 2 Hz) in the phase change is relatively large to the total energy intensity. The high frequency signal duty cycle can also be a specified parameter.

2. Support vector machine model training

As can be seen from the above description, after determining the specified parameters, as shown in fig. 5, the support vector machine model can be constructed by:

s501, acquiring specified parameters of at least one distance sample and labeling labels corresponding to the at least one distance sample, wherein the labeling labels comprise that a person to be detected is in an apnea of a vital sign area to be detected and the person to be detected is not in the vital sign area to be detected.

For example, data corresponding to the apnea of the person to be detected in the vital sign area to be detected is marked as 1, and data corresponding to the person not to be detected in the vital sign area to be detected is marked as 0.

S502, inputting specified parameters of at least one distance sample into the support vector machine model so that the support vector machine model outputs a prediction label of the at least one distance sample.

S503, training a support vector machine model based on losses between the prediction labels and the labeling labels.

Let the training sample set be d= { (x 1, y 1), (x 2, y 2), …, (xm, ym) }; y is _i E { -1, +1}, wherein xi represents a one-dimensional vector composed of at least one specified parameter of the ith distance sample, i is any positive integer between 1 and m, and yi represents a label corresponding to the ith distance sample.

Training a support vector machine model by using a sample set to obtain a decision function of an optimal hyperplane, wherein the dividing hyperplane can be expressed by a linear equation of a formula:

ω ^T formula x+b=0-formula one

Wherein ω= (ω1; ω2; …; ωd) is a normal vector, and the direction of the hyperplane can be determined; b is a displacement item, and the distance between the hyperplane and the origin can be determined; x is an element in the sample set.

The hyperplane can be determined from the normal vector ω and the displacement b, denoted (ω, b). The distance from any point x in the sample space to the hyperplane (ω, b) is expressed by equation two:

if the hyperplane training is correct, for (x) _i ,y _i ) E, D, making:

several training samples closest to the hyperplane may hold the equal sign in equation three, which may be referred to as "support vectors", the sum of the distances of two heterogeneous support vectors to the hyperplane is:

The above distance sum is called "interval", if the division hyperplane of the "maximum interval" is to be found, that is, constraint parameters ω and b satisfying formula three are found such that γ is maximized, that is:

s.t.y _i (ω ^T x _i +b). Gtoreq.1, i=1, 2, … m

To maximize spacing only there is a need to maximize II omega II ^-1 Equivalent to minimizing omega ² Thus equation five can also be expressed as:

s.t.y _i (ω ^T x _i +b). Gtoreq.1, i=1, 2, … m. Formula six

Obtaining a model corresponding to the division hyperplane through a formula six:

f(x)＝ω ^T equation seven of x+b

The Lagrangian multiplier method is used for equation six, i.e., lagrangian multiplier α is added to each constraint condition in equation six _i 0, the Lagrangian function may be expressed as:

let L (ω, b, α) have a bias of 0 for ω and b, it is possible to obtain:

bringing the formula nine into the formula eight can eliminate omega and b in L (omega, b, alpha), and then consider the constraint condition of the formula ten to obtain the formula ten dual problem:

α _i ≥0,i＝1,2,…,m.

after solving alpha, omega and b are obtained, and a model can be obtained:

wherein K represents the Euclidean distance from x to z, x is a sample, z is another sample at the center of the kernel function, sigma is the width of the Gaussian kernel function, and is a settable constant value.

Adding an adjustable relaxation variable ζ to a Gaussian kernel function ⁱ The problem of finding the minimum value in the formula six becomes that:

s.t.y _i (ω ^T x _i +b)≥1-ζ ⁱ i=1, 2, … m. formula fourteen

The constraint of equation eleven becomes:

the classification decision function is shown in a formula sixteen, and f (x) represents a decision function corresponding to the optimal separation hyperplane:

in summary, the decision function f (x) after the training of the support vector machine model is completed is obtained, and the relaxation variable ζ can be adjusted if the loss between the predictive label and the labeling label is excessive during the training of the support vector machine model ⁱ And (3) not less than 0 and penalty factor C, or checking whether an error sample exists in the lower training sample.

Optionally, after obtaining the decision function F (x) after the training of the support vector machine model is completed, in order to verify the classification accuracy of the decision function, an additional set of verification samples d2= { (x '1, y 1), (x' 2, y 2), …, (x'm, ym) } may be input into the decision function F (x), to obtain a statistical result f= { (x' 1, F1), (x '2, F2), …, (x'm, fm) }. A percentage result is obtained by comparing whether fi in the same sample F is equal to the original yi. If the percentage result does not meet the preset threshold value, the relaxation variable ζ can be continuously adjusted ⁱ And (3) not less than 0 and penalty factor C, or checking whether an error sample exists in the next training sample, and finally obtaining the support vector machine model after training.

3. Vital sign detection

As can be seen from the above description, referring to fig. 6, after training the support vector machine model, the embodiment of the present application provides a vital sign detection method, which includes the following steps:

s601, acquiring data of a vital sign area to be detected based on a radar sensor.

S602, performing data processing on the data to obtain a distance-energy mapping relation of the moving target after the static target in the vital sign region to be detected is removed.

Here, the processing procedure of the data may refer to the description of the processing procedure of the data in the determination of the specified parameter, and will not be described herein.

S603, judging whether the maximum energy intensity in the distance-energy mapping relation is larger than the first preset energy intensity. If yes, go to step S604, if no, go to step S605.

S604, vital signs exist in the vital sign region to be detected.

S605, determining a distance-phase mapping relation according to the distance-energy mapping relation.

S606, determining a specified parameter corresponding to at least one distance; wherein the specified parameter includes at least a high frequency signal duty cycle.

S607, inputting the specified parameter corresponding to at least one distance into a pre-constructed support vector machine model to obtain a vital sign detection result output by the support vector machine model.

S608, if the vital sign detection result is 1, the person to be detected is in apnea in the vital sign area to be detected; if the vital sign detection result is 0, the person to be detected is not in the vital sign region to be detected.

In summary, by determining the specified parameter corresponding to the at least one distance and further using the determined specified parameter as the input of the support vector machine model, the method and the device can accurately determine whether the vital sign region to be detected has the vital sign according to the specified parameter at least including the high-frequency signal duty ratio. Meanwhile, the support vector machine model can be used for carrying out on-site learning and recognition on application scenes in the model training process, so that the obtained support vector machine model can obtain more accurate output results in the practical application process after model training is completed.

In addition, in the actual application process, as shown in fig. 7, for the case that the person to be detected is not in the vital sign area to be detected, the person to be detected still can be detected in the distance interval of 60-80, but the person to be detected is not present in the actual scene. Thus, the specified parameters described above may also include the range energy intensity RangePower. In order to solve the above problem, considering that when the radar sensor collects data of a vital sign region to be detected, there is a sampling time difference, if the sampling time is different, a random phase difference is caused.

s801, obtaining a first numerical value based on the energy intensity and the phase value corresponding to the current sampling time; and obtaining a second value based on the energy intensity and the phase value corresponding to the previous time of the current sampling time;

s802, performing modular operation on the first numerical value and the second numerical value respectively;

s803, removing the static target in the vital sign region to be detected by utilizing the difference value between the modulo operation result of the first value and the modulo operation result of the second value.

For example, the difference between the modulo operation result of the first value and the modulo operation result of the second value is calculated by the equation eighteen.

This reduces the energy intensity difference caused by the phase difference. As shown in fig. 9, there is no peak in the energy intensity shown in fig. 7 after adjustment.

Furthermore, in order to more accurately detect the vital sign region to be detected, the radar sensor can also acquire the data of the vital sign region to be detected through a multi-antenna transmission and multi-antenna receiving mode, and beam synthesis is performed on the data received by a plurality of receiving ends of the radar sensor, so that the range of acquired data is more accurate. As shown in fig. 10a, a single antenna transmission and single antenna reception acquisition data range is used for the radar sensor, and as shown in fig. 10b, a multiple antenna transmission and multiple antenna reception acquisition data range is used for the radar sensor. For example, assuming that the vital sign area to be detected is a bed, a table or chair beside the bed may be collected, and the table or chair is also a false strong reflection target, thereby affecting the detection result.

Illustratively, the range over which the radar sensor collects data is determined by:

LambaRX＝[0,0.5,1,1.5]

SignalVector＝[RX0data,RX1data,RX2data,RX3data]

Angle＝0；

S＝SignalVector*SignalVector

Where lambaRX is a multiple of wavelength λ, signalVector is the signal vector of 4 radar sensor receive channels, and SteringVector is the steering vector over Angle (0 °). As shown in fig. 11, the horizontal angles before and after the range of the radar sensor acquisition data are modified in fig. 10a and 10 b. The gain of the radar sensor receiving end can be increased after modification.

Having described the vital sign detection method, apparatus of an exemplary embodiment of the present application, next, an electronic device according to another exemplary embodiment of the present application is described.

Those skilled in the art will appreciate that the various aspects of the present application may be implemented as a system, method, or program product. Accordingly, aspects of the present application may be embodied in the following forms, namely: an entirely hardware embodiment, an entirely software embodiment (including firmware, micro-code, etc.) or an embodiment combining hardware and software aspects may be referred to herein as a "circuit," module "or" system.

In some possible implementations, an electronic device according to the present application may include at least one processor, and at least one memory. Wherein the memory stores program code which, when executed by the processor, causes the processor to perform the steps in the vital sign detection method according to various exemplary embodiments of the present application described above in the present specification. For example, the processor may perform steps as in a vital sign detection method.

In exemplary embodiments, aspects of a vital sign detection method provided herein may also be implemented in the form of a program product comprising program code for causing a computer device to carry out the steps of a vital sign detection method according to the various exemplary embodiments of the present application as described herein above, when the program product is run on the computer device.

The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The 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 readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

The program product for vital sign detection of embodiments of the present application may employ a portable compact disc read only memory (CD-ROM) and include program code and may be run on an electronic device. However, the program product of the present application is not limited thereto, and in this document, a 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 readable signal medium may include a data signal propagated in baseband or as part of a carrier wave with readable program code embodied therein. 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 readable signal medium may also be any readable medium that is not a 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 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.

Program code for carrying out operations of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the consumer electronic device, partly on the consumer electronic device, as a stand-alone software package, partly on the consumer electronic device, partly on the remote electronic device, or entirely on the remote electronic device or server. In the case of remote electronic devices, the remote electronic device may be connected to the consumer electronic device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external electronic device (e.g., connected through the internet using an internet service provider).

It should be noted that although several units or sub-units of the apparatus are mentioned in the above detailed description, such a division is merely exemplary and not mandatory. Indeed, the features and functions of two or more of the elements described above may be embodied in one element in accordance with embodiments of the present application. Conversely, the features and functions of one unit described above may be further divided into a plurality of units to be embodied.

Furthermore, although the operations of the methods of the present application are depicted in the drawings in a particular order, this is not required to or suggested that these operations must be performed in this particular order or that all of the illustrated operations must be performed in order to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step to perform, and/or one step decomposed into multiple steps to perform.

It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable electronic device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable electronic device, 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 or other programmable electronic 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 or other programmable electronic device to cause a series of operational steps to be performed on the computer or other programmable device to produce a computer implemented process such that the instructions which execute on the computer or other programmable device provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

While preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the application.

It will be apparent to those skilled in the art that various modifications and variations can be made in the present application without departing from the spirit or scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims and the equivalents thereof, the present application is intended to cover such modifications and variations.