CN113712513A

CN113712513A - Physiological detection method and device

Info

Publication number: CN113712513A
Application number: CN202111051224.1A
Authority: CN
Inventors: 王柳锋; 车婷婷; 李占学
Original assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Current assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Priority date: 2021-09-08
Filing date: 2021-09-08
Publication date: 2021-11-30

Abstract

The disclosure provides a physiological detection method and a physiological detection device, relates to the field of data processing, and particularly relates to the field of AI medical treatment. The specific implementation scheme is as follows: the method comprises the steps of obtaining multiple types of physiological characteristics of an object to be detected in a preset time period before the current time. Based on the multiple types of physiological characteristics, change information for each type of physiological characteristic is determined. And determining the detection result of the object to be detected according to the change information of each type of physiological characteristics. The physiological characteristics of the object to be detected are acquired, the change information of the physiological characteristics of various types is determined, and the detection result of the object to be detected is determined according to the change information of the physiological characteristics, so that the physical state of the object to be detected is actually determined or estimated by continuously monitoring the physiological characteristics of the object to be detected, and the timeliness of determining the physiological state of the object to be detected can be effectively improved.

Description

Physiological detection method and device

Technical Field

The present disclosure relates to the field of AI medical treatment in the field of data processing, and in particular, to a physiological detection method and apparatus.

Background

With the continuous development of modern society, the health problems of people are gradually highlighted, so that the detection of the health is particularly important.

Currently, the existing health detection method is mainly that a user goes to a hospital or a physical examination facility, or purchases some health detection devices, such as a sphygmomanometer, a blood glucose analyzer, and the like, to perform a physical health check, so as to determine a detection result.

However, these implementations of the prior art only enable intermittent spot checks of the physical health of the user, which may result in a lack of timeliness of the detection of the physiological state.

Disclosure of Invention

The present disclosure provides a physiological detection method and apparatus.

According to a first aspect of the present disclosure, there is provided a physiological detection method comprising:

acquiring various types of physiological characteristics of an object to be detected in a preset time period before the current moment;

determining change information of each type of physiological characteristics according to the multiple types of physiological characteristics;

and determining the detection result of the object to be detected according to the change information of each type of physiological characteristics.

According to a second aspect of the present disclosure, there is provided a physiological detection device comprising:

the acquisition module is used for acquiring various types of physiological characteristics of an object to be detected in a preset time period before the current moment;

the first determination module is used for determining the change information of each type of physiological characteristics according to the multiple types of physiological characteristics;

and the second determination module is used for determining the detection result of the object to be detected according to the change information of each type of physiological characteristics.

According to a third aspect of the present disclosure, there is provided an electronic device comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of the first aspect.

According to a fourth aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of the first aspect.

According to a fifth aspect of the present disclosure, there is provided a computer program product comprising: a computer program, stored in a readable storage medium, from which at least one processor of an electronic device can read the computer program, execution of the computer program by the at least one processor causing the electronic device to perform the method of the first aspect.

The technique according to the present disclosure solves the problem of lack of timeliness for the detection of physiological states.

It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.

Drawings

The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:

FIG. 1 is a schematic diagram of an implementation of a physiological detection system provided by an embodiment of the present disclosure;

FIG. 2 is a flow chart of a physiological detection method provided by an embodiment of the present disclosure;

FIG. 3 is a second flowchart of a physiological detection method provided by an embodiment of the present disclosure;

FIG. 4 is a first schematic diagram of a physiological characteristic provided by an embodiment of the present disclosure;

FIG. 5 is a schematic diagram of physiological characteristics provided by an embodiment of the present disclosure

FIG. 6 is a schematic illustration of physiological characteristics provided by an embodiment of the present disclosure

FIG. 7 is a schematic diagram of determining a detection result according to an embodiment of the disclosure;

FIG. 8 is a schematic process flow diagram provided by an embodiment of the present disclosure;

FIG. 9 is a schematic structural diagram of a physiological detection device according to an embodiment of the present disclosure;

FIG. 10 is a block diagram of an electronic device for implementing a physiological detection method of an embodiment of the present disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

In order to better understand the technical solution of the present disclosure, the related art related to the present disclosure is further described in detail below.

With the continuous development of modern society, conditions such as materials, environment and the like are changed greatly, and some health problems are gradually highlighted, so that the detection of the health state of a human body is particularly important.

At present, when human health detection is performed in the related art, a user mainly goes to a hospital or a physical examination mechanism for physical examination by himself or the user performs physical examination through some simple health detection mechanisms, such as a sphygmomanometer, a blood glucose analyzer, and the like, so as to determine the health state of the human body, but such an implementation manner can only achieve intermittent spot inspection of the health state, cannot continuously pay attention to the health, and thus can cause lack of timeliness in determining the health state of the user.

Meanwhile, some wearable devices exist in the related art at present, and can detect the step number, heartbeat and the like of a wearing user, for example, an intelligent bracelet, but the functions of the wearable devices are usually simpler, and only some simple functions such as whether a human body has a signal or not can be realized, and whether the body state of the current user is normal or not can not be judged, so that the wearable devices cannot effectively detect the health state of the user.

It can be understood that the detection of the current hospital or physical examination institution is relatively authoritative and accurate, but it is only intermittent diagnosis, that is, only the current situation can be detected, generally the time interval is long, especially for the diseases such as cardiovascular and cerebrovascular diseases which may have acute attack, even if the possibility of attack exists in the current examination, the current examination only can remind the user of the disease, and the monitoring of the physical state in the whole time period cannot be realized.

In addition, the individual difference of each user is large, the perception of the physical state of the user is not as sensitive as a doctor, the disease can not be predicted in advance, even if some users forget to take medicines, occasionally tired and the like, the disease can be caused without taking measures in advance. If serious patients such as cardiac cerebral infarction are attacked and cannot be found in time, the serious patients have serious harm to the physical health of users.

In view of the above-mentioned technical problems of the prior art, the present disclosure proposes the following technical concepts: the detection and monitoring of various physiological signals of a human body can be realized through the detection equipment, then, the detection result of the body state of the user is output by carrying out data statistics analysis, reasoning learning and the like on the various physiological signals, and early warning in early stage of disease incidence can be realized or alarm after disease incidence can be realized, so that the timeliness of the determination of the health condition of the user can be effectively improved.

First, a system corresponding to the physiological detection method provided by the embodiment of the present disclosure is introduced with reference to fig. 1, and fig. 1 is a schematic implementation diagram of the physiological detection system provided by the embodiment of the present disclosure.

As shown in fig. 1, currently, for example, a detection device 101 may perform physiological detection on a subject needing health detection, so as to obtain physiological characteristics of the subject to be detected, where the physiological characteristics may include, for example, a heart rate, a blood pressure, and the like, and this embodiment is not particularly limited in this respect.

The server 102 may then obtain, for example, a physiological characteristic detected by the detection device 101, and after performing data analysis based on the physiological characteristic, a detection result of the object to be detected may be obtained.

The specific implementation manner of the detection device 101 in this embodiment may be selected according to actual requirements, which is not limited in this embodiment as long as the detection device 101 can implement a physiological detection function of the object to be detected. In a possible implementation manner, the server 102 in this embodiment may be understood as, for example, a cloud server, and the specific implementation manner of the cloud server may be selected according to actual requirements, which is not particularly limited in this embodiment.

On the basis of the foregoing embodiments, the physiological detection method provided by the present disclosure is described below with reference to specific embodiments, and it should be noted that the execution subject of each embodiment of the present disclosure may be the server described above, or in an alternative implementation, the execution subject of each embodiment of the present disclosure may also be a device with a data processing function, such as a processor, a microprocessor, and the like.

First, a physiological detection method provided by the present disclosure is described with reference to fig. 2, and fig. 2 is a flowchart of the physiological detection method provided by the embodiment of the present disclosure.

As shown in fig. 2, the method includes:

s201, acquiring various types of physiological characteristics of the object to be detected in a preset time period before the current time.

In this embodiment, for example, the server may acquire, by the detection device, multiple types of physiological characteristics of the object to be detected in a preset time period before the current time, where the object to be detected is an object that needs to be subjected to health condition detection currently.

The preset time period before the current time may be, for example, a total time period in which the physiological characteristic of the object to be detected is detected before the current time, for example, the user a is currently subjected to the detection of the physiological characteristic, so the user a may wear various detection devices, and the preset time period may be, for example, a time period from a time when the user wears the detection devices to the current time, that is, the physiological characteristic of the object to be detected may be continuously acquired in this embodiment, so that the monitoring of the physiological characteristic of the object to be detected is realized.

Alternatively, the preset time period before the current time may also be a time period with a preset time duration, for example, a time period within 1 day before the current time, a time period within 3 months before the current time, and the like.

In a possible implementation manner, for example, the physiological characteristics of the object to be detected may be detected by a plurality of different types of detection devices, so as to obtain a plurality of types of physiological characteristics of the object to be detected, for example, the physiological characteristics measured by one type of detection device may be used as one type of physiological characteristics.

For example, waveforms of various parts (such as blood pressure shock response) of the body of a subject to be detected can be acquired through a pulse detection device; or acquiring a waveform corresponding to the brain wave through brain wave detection equipment; for another example, the corresponding waveform of the electrocardiographic wave can be obtained through the electrocardiographic detection device; it is not listed here, and it is understood that in the actual implementation process, the corresponding signal quantization waveform can be obtained by any possible body signal detection device.

The embodiment does not particularly limit the specific implementation manner of the detection device, and multiple types of physiological characteristics can be correspondingly obtained through multiple different detection devices.

S202, determining the change information of each type of physiological characteristics according to the various types of physiological characteristics.

After determining the plurality of types of physiological characteristics, change information for each type of physiological characteristic may be determined based on the plurality of types of physiological characteristics. In a possible implementation manner of this embodiment, the change information of the physiological characteristic may include, for example, a change trend of the physiological characteristic, and a duration in which the physiological characteristic exhibits the change trend of the physiological characteristic, such as a duration of continuous rising, a duration of time sequence reduction, a duration of rapid fluctuation, and the like.

It can be understood that, in this embodiment, the physiological characteristics of the object to be detected may be continuously monitored, and then, the real-time analysis may be performed according to the monitored physiological characteristics, so as to output the change information of each type of physiological characteristics in real time.

In a possible implementation manner, before determining the change information according to multiple types of physiological characteristics, for example, the integrity of the physiological characteristics may also be determined, which may also be understood as filtering processing of the physiological characteristics, and after the integrity determination of the physiological characteristics is implemented, when it is determined that the current type of physiological characteristics is an intact signal, subsequent processing is performed, so that the processing efficiency of determining the change information may be improved.

And S203, determining the detection result of the object to be detected according to the change information of each type of physiological characteristics.

After the change information of each type of physiological characteristic is determined, the detection result of the object to be detected can be determined according to the change information of each type of physiological characteristic, for example, the detection result in this embodiment may indicate whether the physical condition of the object to be detected is healthy or unhealthy, or the detection result may also be, for example, a specific condition of the physical condition of the object to be detected, such as predicted problems that may occur to the body of the object to be detected, and possible conditions, such as hypertension, cerebral hemorrhage, and the like, so that the estimation of the physical condition of the object to be detected can be realized according to the physiological characteristic of the object to be detected, and the timeliness of determining the physical condition of the object to be detected can be effectively improved.

In this embodiment, the detection result may be a result corresponding to the real physical state of the current object to be detected, for example, the current object to be detected really suffers from diseases such as hypertension, and the detection result in this embodiment may indicate the information; or, the detection result in this embodiment may also be an estimated body state output currently for the object to be detected, for example, the current object to be detected does not have hypertension, but the physiological characteristics of the current object to be detected estimated through the analysis of the above-described process present the characteristic that hypertension may occur, so the detection result in this embodiment may be, for example, an estimated result of the body state of the object to be detected, and thus a predetermined prompt may be performed for the body state of the object to be detected, so as to realize timely determination of the body state of the object to be detected.

In one possible implementation, the change information of each type of physiological characteristic may be processed according to a series of preset rules, for example, so as to determine the detection result of the object to be detected. Or, for example, the change information of each type of physiological characteristic may be input into a preset model, so as to output the detection result of the object to be detected, where the preset model is obtained by learning according to the sample change information and the sample detection result, and the embodiment does not limit the specific implementation manner of determining the detection result, and may be selected according to actual requirements as long as the detection result of the object to be detected is determined according to the change information of the physiological characteristic.

The physiological detection method provided by the embodiment of the disclosure comprises the following steps: the method comprises the steps of obtaining multiple types of physiological characteristics of an object to be detected in a preset time period before the current time. Based on the multiple types of physiological characteristics, change information for each type of physiological characteristic is determined. And determining the detection result of the object to be detected according to the change information of each type of physiological characteristics. The method comprises the steps of obtaining various types of physiological characteristics of an object to be detected for a long time, then determining the change information of the various types of physiological characteristics, further determining the detection result of the object to be detected according to the change information of the physiological characteristics, wherein the detection result can indicate the current real body state or can also indicate the estimated body state, and further realizing the actual determination or estimation of the body state of the object to be detected by continuously monitoring the physiological characteristics of the object to be detected, so that the timeliness of determining the physiological state of the object to be detected can be effectively improved.

Based on the above embodiments, the physiological detection method provided by the embodiment of the present disclosure is further described in detail with reference to fig. 3 to 7, fig. 3 is a second flowchart of the physiological detection method provided by the embodiment of the present disclosure, fig. 4 is a first schematic diagram of physiological characteristics provided by the embodiment of the present disclosure, fig. 5 is a second schematic diagram of physiological characteristics provided by the embodiment of the present disclosure, fig. 6 is a third schematic diagram of physiological characteristics provided by the embodiment of the present disclosure, and fig. 7 is a schematic diagram of determination detection results provided by the embodiment of the present disclosure.

As shown in fig. 3, the method includes:

s301, acquiring various types of physiological characteristics of the object to be detected in a preset time period before the current time.

The implementation manner of S301 is similar to that of S201 described above, and is not described here again.

S302, for any type of physiological characteristics, determining a change trend of the physiological characteristics relative to the previous moment according to the physiological characteristics, wherein the change trend of the physiological characteristics is any one of the following: and (4) rising and lowering.

In this embodiment, for each type of physiological characteristic, the corresponding change information needs to be determined, and in a possible implementation manner, the change information in this embodiment may include, for example, a change trend of the physiological characteristic with respect to a previous time, where the change trend in this embodiment may be, for example, an increase, or may also be a decrease, or the change trend may also be a constant, which is not limited in this embodiment.

In this embodiment, a trend of the physiological characteristic at the current time with respect to a previous time may be determined according to the type of physiological characteristic, for example, for a heart rate, it may be determined whether the trend of the heart rate at the current time with respect to the heart rate at the previous time is increased, decreased, or kept unchanged.

In an actual implementation process, how long the time length between the current time and the previous time is specifically, may be selected according to an actual requirement, for example, the middle time length may be 1 second, or the middle time length may also be 0.5 second, and the like, which is not limited in this embodiment.

S303, determining the change information of the physiological characteristics according to the change trend of the physiological characteristics.

In the present embodiment, after determining the variation trend of the physiological characteristic, the variation information of the physiological characteristic may be further determined according to the determined variation.

In one possible implementation, if the trend of change of the physiological characteristic is increasing, for example, a first duration of the continuously increasing trend of the physiological characteristic may be obtained, for example, as may be understood in conjunction with fig. 4, a fluctuation situation of one type of physiological characteristic is illustrated in fig. 4, and assuming that the current time is time b in fig. 4 and the time a before time b is time a in fig. 4, it may be determined that the trend of change of the physiological characteristic is increasing relative to the previous time at time b, and the first duration of the continuously increasing trend of the physiological characteristic may be determined as a duration from time a to time b.

Assuming again that the current time is time c in fig. 4, wherein the time before time c is time b in fig. 4, it may be determined that the trend of the physiological characteristic is increasing relative to the previous time at time c, and the first duration of the continuous increase of the physiological characteristic may be determined as the duration from time a to time c in fig. 4.

And similarly for time e in fig. 4, assuming that the current time is time e, it can be determined that the trend of the physiological characteristic is increasing relative to the previous time at time e, and the first duration of the continuous increase of the physiological characteristic can be determined as the duration from time d to time e in fig. 4.

The first duration of the continuous increase in the physiological characteristic described at the present time therefore refers to the duration of the physiological characteristic that remains increased (no decrease has occurred) until the present time.

And in another possible implementation, if the trend of change of the physiological characteristic is decreasing, for example, a second duration during which the trend of change of the physiological characteristic is continuously decreasing may be obtained, for example, as may be understood in conjunction with fig. 5, where fig. 5 illustrates a fluctuation situation of one type of physiological characteristic, and assuming that the current time is time o in fig. 5 and the time before time o is time m in fig. 5, it may be determined that the trend of change of the physiological characteristic is decreasing with respect to the previous time at time o, and it may be determined that the second duration during which the trend of change of the physiological characteristic is continuously decreasing is a duration from time m to time o.

Assuming again that the current time is time n in fig. 5, wherein the time before time n is time o in fig. 5, then at time n, it can be determined that the trend of the physiological characteristic is decreasing relative to the previous time, and the second duration of the continuous decrease of the physiological characteristic can be determined as the duration from time m to time n in fig. 5.

And similarly for time y in fig. 5, assuming that the current time is time y, then the trend of the physiological characteristic is determined to be decreasing relative to the previous time at time y, and the second duration of the continuous decrease of the physiological characteristic is determined to be the duration from time x to time y in fig. 5.

The second duration of the continuous decrease of the physiological characteristic described thus far refers to the duration of the time the physiological characteristic remains decreased (no increase occurred) until the present moment.

And in the embodiment, the fluctuation frequency of the physiological characteristic in the target time length before the current time can be determined, wherein the fluctuation is increased once and calculated once, and the fluctuation is reduced once and calculated once, and the fluctuation frequency of the physiological characteristic in the target time length can be determined according to the fluctuation frequency in the target time length and the target time length.

The specific implementation manner of the target duration may be selected according to an actual requirement, for example, 3 seconds, 5 seconds, and the like, and the specific implementation of the target duration is not limited in this embodiment.

As can be understood in conjunction with fig. 6, for example, in fig. 6, which also illustrates the fluctuation of the physiological signal, the fluctuation frequency between time p and time q in fig. 6 can be obtained assuming that the current time is time q in fig. 6 and the target time length before the current time is the time length between time p and time q in fig. 6.

In a possible implementation manner, the variation information in the present embodiment may include, for example, the variation trend, the first time length or the second time length, and the fluctuation frequency described above.

In the actual implementation process, the specific implementation of the change information may also be selected and set according to actual requirements, and all the relevant information that can indicate the change of the physiological characteristic may be used as the change information in this embodiment, which is not limited in this embodiment.

S304, obtaining respective weight information of each type of physiological characteristics corresponding to the object to be detected.

In this embodiment, for the current object to be detected, for example, the weighting information corresponding to each type of physiological characteristic of the object to be detected may also be obtained.

In a possible implementation manner, if the current object to be detected is an object that has been subjected to physiological detection before, for example, the weight information of the current object to be detected may be determined in advance, so that, for example, the weight information corresponding to the current object to be detected may be directly acquired from the preset storage unit.

In another possible implementation manner, if the current object to be detected is an object that has not been subjected to physiological detection before, then there is no weight information of the object to be detected currently, for example, default weight information may be used as the weight information of the object to be detected, and then after the physiological characteristics of the object to be detected are obtained, the weight information corresponding to the current object to be detected is determined and stored, so as to be directly obtained next time.

The following describes an implementation manner of determining respective weight information of each type of physiological characteristics corresponding to an object to be detected.

In one possible implementation, for example, historical physiological characteristics and historical detection results of the object to be detected may be obtained. And then determining the respective weight information of each type of physiological characteristics corresponding to the object to be detected according to the historical physiological characteristics and the historical detection result of the object to be detected.

The historical physiological characteristics of the object to be detected can be physiological characteristics before the current moment, and the historical detection results indicate the detection results determined before the current moment. In this embodiment, for example, data analysis processing may be performed on the historical physiological characteristics and the historical detection results of the object to be detected, so as to determine the respective weight information of each type of physiological characteristic corresponding to the object to be detected, and a specific processing manner may be, for example, completed by a preset algorithm, or may also be completed by a preset model, which is not limited in this embodiment.

It is understood that the specific physical condition of each subject to be detected is different, and the physiological characteristics to which the subject is sensitive are different for each subject to be detected.

In an actual implementation process, a specific implementation manner for determining the weight value for each type of physiological characteristic of each object to be detected may be selected according to actual requirements, which is not limited in this embodiment, for example, the weight value that follows the more sensitive physiological characteristic setting of the current object to be detected is larger.

For example, for a subject a to be detected with hypertension, the physiological characteristics of the blood pressure of the subject a to be detected are relatively sensitive characteristics, and the weight of the corresponding physiological characteristics of the blood pressure of the subject a to be detected is relatively high; for example, for an object B to be detected with a heart disease, the physiological characteristic of the heart rate is a relatively sensitive characteristic, and the weight of the heart rate physiological characteristic of the corresponding object B to be detected is relatively high, and so on.

S305, obtaining threshold value information of each type of physiological characteristics corresponding to the object to be detected.

In this embodiment, for the current object to be detected, threshold information corresponding to each type of physiological characteristic of the object to be detected may also be obtained, for example.

In a possible implementation manner, if the current object to be detected is an object that has been subjected to physiological detection before, threshold information of the current object to be detected may be determined in advance, for example, so that, for example, the threshold information corresponding to the current object to be detected may be directly acquired from the preset storage unit.

In another possible implementation manner, if the current object to be detected is an object that has not been subjected to physiological detection before, then there is no threshold information of the object to be detected currently, for example, default threshold information may be used as the threshold information of the object to be detected, and then after the physiological characteristics of the object to be detected are obtained, the threshold information corresponding to the current object to be detected is determined and stored, so as to be directly obtained next time.

The following describes an implementation manner of determining respective threshold information of each type of physiological characteristic corresponding to an object to be detected.

In one possible implementation, for example, reference information may be obtained, where the reference information includes at least one of the following: historical physiological characteristics, historical detection results and actual results of at least one object, wherein the at least one object comprises an object to be detected; and then, carrying out data analysis processing according to the reference information, and determining the threshold information of each type of physiological characteristics.

The reference information in this embodiment may include historical physiological characteristics, historical detection results, and actual results of a plurality of subjects, where the actual results may be, for example, results determined by subjects after performing actual physiological diagnosis, which may be understood as, for example, results of confirmed diagnosis. The information of the plurality of objects in this embodiment may be, for example, an object that has been subjected to data processing before the cloud server, and the plurality of objects include a current object to be detected.

In this embodiment, for example, the big data analysis may be performed on the historical physiological characteristics and the historical detection results of the object to be detected, so as to determine the respective weight information of each type of physiological characteristics corresponding to the object to be detected, and the specific processing manner may be, for example, completed by a preset algorithm, or may also be completed by a preset model, which is not limited in this embodiment.

It will be appreciated that the specific physical condition of each subject to be detected is different, and the degree of sensitivity to an increase or decrease in a physiological characteristic of each subject to be detected is different.

For example, for a subject to be detected with hypertension, there is a danger if the time period during which the blood pressure of the subject to be detected may rise is longer than 2 seconds, but for a subject to be detected without hypertension, the time period threshold value during which the blood pressure of the subject to be detected can rise may be wider, so in this embodiment, the threshold value information corresponding to each physiological characteristic may be determined individually for each subject to be detected.

In an actual implementation process, a specific implementation manner for determining the weight value for each type of physiological characteristic of each object to be detected may be selected according to an actual requirement, which is not limited in this embodiment.

S306, determining an evaluation parameter corresponding to each type of physiological feature according to the variation information of each type of physiological feature and the threshold information of each type of physiological feature, wherein the threshold information comprises at least one of the following: a first threshold value corresponding to a continuously increasing change trend, a second threshold value corresponding to a continuously decreasing change trend and a third threshold value corresponding to a fluctuation frequency.

Based on the above description, it can be determined that, for the current object to be detected, change information of each type of physiological characteristic and threshold information of each type of physiological characteristic are determined, and then, for example, an evaluation parameter corresponding to each type of physiological characteristic can be determined according to the change information of each type of physiological characteristic and the threshold information of each type of physiological characteristic, for example, referring to fig. 7, for the current physiological characteristic of type 1, an evaluation parameter 1 can be determined according to the change information 1 and the threshold information 1 thereof; and for physiological characteristics of type 2, an evaluation parameter 2 may be determined from its variation information 2 and threshold information 2; and for physiological characteristics of type 3, an evaluation parameter 3 may be determined from its variation information 3 and threshold information 3.

In a possible implementation manner, the threshold information in this embodiment includes at least one of the following: a first threshold value corresponding to a continuously increasing change trend, a second threshold value corresponding to a continuously decreasing change trend and a third threshold value corresponding to a fluctuation frequency.

The evaluation parameter in this embodiment may be understood as a score of the degree of risk, for example, as understood in conjunction with fig. 4, for example, when the current time for the physiological characteristic in fig. 4 is 2 seconds, the first threshold value corresponding to the continuously increasing trend is 2 seconds, and the trend corresponding to time c in fig. 4 is increasing, and the duration of the continuously increasing trend is from time a to time c, assuming that 1 second is used, the evaluation parameter for the change information of the physiological characteristic at time c is lower, indicating that the degree of risk is not very high.

For another example, the trend of the change corresponding to time e in fig. 4 is increased, and the duration of the increase is from time d to time e, and assuming that the duration is 5 seconds, the evaluation parameter of the change information of the physiological characteristic at time e is higher, which indicates that the risk level is higher.

And it can also be understood in conjunction with fig. 5, that if the trend of the change corresponding to time n in fig. 5 is decreasing and the duration of the decrease does not exceed the second threshold, for example, then the evaluation parameter of the change information of the physiological characteristics for time n is lower, indicating that the degree of risk is not very high. And the trend of the change corresponding to the time y in fig. 5 is decreasing, and the duration of the decrease exceeds the third threshold, for example, the evaluation parameter of the change information of the physiological characteristic at the time y is higher, which indicates that the degree of risk is higher.

For another example, as can be understood in conjunction with fig. 6, referring to fig. 6, if the fluctuation frequency in the time period from the time p to the time q in fig. 6 is greater than the third threshold value, for example, it can also be determined that the evaluation parameter of the change information of the physiological characteristic at the time p is higher, which indicates that the risk level is higher.

In an actual implementation process, the evaluation parameter may include, in addition to the above-described score for the risk degree, a current specific physiological characteristic, for example, a specific indication that blood pressure is dangerous, or a specific indication that heart rate is dangerous, and the like.

S307, weighting processing is carried out according to the evaluation parameters corresponding to each type of physiological characteristics and the weight information corresponding to each type of physiological characteristics, and the detection result of the object to be detected is determined.

Based on the above description, currently, the evaluation parameters corresponding to each type of physiological characteristics and the weighting information corresponding to each type of physiological characteristics are determined, so as to determine the detection result of the object to be detected.

As may be appreciated, for example, in conjunction with fig. 7, for example, for a physiological characteristic of type 1, the detection result may be determined from its evaluation parameter 1 and weight information 1; and for physiological characteristics of type 2, the detection result can be determined according to the evaluation parameter 2 and the weight information 2 thereof; and for physiological characteristics of type 3, the detection result may be determined from its evaluation parameter 3 and the weighting information 3, the currently presented detection result may for example comprise a currently presented score and may also comprise specific predictive information, such as for what situation such a score is currently obtained specifically.

In a possible implementation manner, for example, the evaluation parameter corresponding to each type of introduced physiological features and the weighting information corresponding to each type of physiological features may be processed through a preset algorithm or a preset model, so as to determine a specific physiological problem of the current object to be detected, such as hypertension, myocardial infarction, cerebral hemorrhage, and the like; or, a predicted physiological problem and the like of the current object to be detected may be determined according to historical physiological problems of the object to be detected, for example, if hypertension exists before the object to be detected, the occurrence probability of the object to be detected may be currently evaluated, for example, corresponding evaluation scores may be given for the specific situations to indicate the possibility of the current situations.

In an actual implementation process, the implementation manner of processing the evaluation parameter and the weight information corresponding to the physiological characteristic to determine the specific situation may be selected according to an actual requirement, which is not particularly limited in this embodiment.

And S308, if the detection result of the object to be detected indicates that the object to be detected has a risk, sending warning information to the target equipment, wherein the warning information is used for prompting that the object to be detected has the risk.

In a possible implementation manner, if it is determined that the detection result of the object to be detected indicates that the object to be detected has a risk, for example, a warning message may be sent to the target device, so as to prompt that the current object to be detected has a risk, and then, for example, corresponding medical measures may be taken for the object to be detected in time, so as to avoid a corresponding dangerous situation.

The possible implementation manner that the detection result of the object to be detected indicates that the object to be detected has a risk may be that, the detection result includes a predicted physiological problem and a predicted score for the current object to be detected, where the predicted physiological problem may include, for example, hypertension, myocardial infarction, cerebral hemorrhage, and the like, the predicted score may indicate a possibility that the current object to be detected has the predicted physiological problem, for example, the predicted score may be compared with a preset score threshold, if the predicted branch is greater than or equal to the score threshold, for example, it may be determined that the detection result of the object to be detected indicates that the object to be detected has a risk, and if the predicted score is less than the score threshold, for example, it may be determined that the detection result of the object to be detected indicates that the object to be detected does not have a risk.

Or, the detection result may also be a binary implementation manner, that is, the detection result directly indicates that the object to be detected has a risk, or the detection result directly indicates that the object to be detected does not have a risk.

In another possible implementation manner, if it is determined that the detection result of the object to be detected indicates that the object to be detected does not have a risk, it may be determined that the current object to be detected does not have a physiological problem temporarily, and for example, the monitoring may be performed continuously, or it may also be determined that indication information is output when the monitoring is finished, where the indication information is used to indicate that the detection result of the object to be detected does not have a risk.

In a possible implementation manner, after the warning information is sent to the target device, for example, an actual result of the object to be detected may be obtained, where the actual result may be understood as a result obtained by the object to be detected automatically going to a medical institution for medical diagnosis or a result obtained by performing corresponding medical measures after the physiological detection method provided in the embodiment of the present disclosure outputs the detection information result.

And then, according to the actual result of the object to be detected and the output detection result, updating the threshold information and the weight information of each type of physiological characteristics of the object to be detected, so that the individualized threshold information and the individualized weight information can be determined for the object to be detected, and the accuracy of the output detection result can be improved when the detection result is determined for the current object to be detected subsequently.

The implementation manner of updating the threshold information and the weight information corresponding to the object to be detected may be, for example, updating parameters in a corresponding preset algorithm, or may also be updating parameters in a corresponding preset model, and the like.

The physiological detection method provided by the embodiment of the disclosure can effectively determine the change information of the physiological characteristics by determining the change trend of the physiological characteristics, the duration of the change trend exhibited by the physiological characteristics, and the fluctuation frequency of the physiological characteristics, then compare the change information of the physiological characteristics with corresponding threshold information to determine the evaluation parameters corresponding to the current physiological characteristics, and then determine the detection results obtained by combining the corresponding weight information and jointly obtaining the physiological characteristics of various types, wherein the threshold information and the weight information are set individually for each object to be detected, thereby effectively ensuring that the obtained detection results can indicate the physiological condition of the current object to be detected, and after determining the detection results and the actual results of the object to be detected, the threshold information and the weight information corresponding to the object to be detected can be updated according to the detection results and the actual results, therefore, the accuracy of the output physiological condition of the object to be detected can be effectively improved, the physiological detection method provided by the embodiment of the disclosure can effectively realize the prediction of the physiological condition of the object to be detected, and the method provided by the embodiment is continuously performed in real time for the object to be detected, so that the monitoring of the physical condition of the object to be detected can be realized, and the timeliness of determining the physiological condition of the object to be detected can be effectively improved.

On the basis of the foregoing embodiments, a system description is provided below with reference to fig. 8 for implementing the physiological detection method provided by the embodiment of the present disclosure, and fig. 8 is a schematic processing flow diagram provided by the embodiment of the present disclosure.

As shown in fig. 8, the AI prediction unit in fig. 8 is, for example, a processing unit for executing the above-described physiological detection method, and referring to fig. 8, each detection device may upload the detected physiological characteristics to the AI prediction unit through, for example, a network.

The detection device may include, for example, an electroencephalogram detection device in fig. 8, configured to acquire waveforms corresponding to electroencephalograms; the electrocardio detection equipment is used for acquiring the corresponding wave form of the electrocardiowave; and pulse detection equipment for obtaining waveforms of all parts of the body; and a blood detection device for detecting blood related data, and may further comprise other detection devices for obtaining corresponding physiological benefits.

Each detection device may upload the detected physiological characteristics to an AI prediction unit in the cloud, where the uploading may be, for example, uploading via a communication module of the detection device, such as a General Packet Radio Service (GPRS) module; alternatively, the detected physiological characteristics may also be uploaded to the AI prediction unit, for example, through a mobile network or a wired network of the terminal device (such as a mobile phone or a computer), or through any possible data uploading manner.

The AI prediction unit may then perform the above processing procedure according to the received physiological signal, so as to output the detection result, and then, for example, may output the detection result to the target device, for example, may send the detection result to the device of the current detection object, so as to remind the current detection object of the abnormal physical condition; alternatively, the detection result may be sent to a device of a medical staff or a family of the detection subject, so as to remind the current detection subject of risk, and besides sending the detection result, for example, various types of physiological characteristics as described above may be sent to the corresponding target device.

In addition, in this embodiment, data storage may be performed on the detection result output by the AI prediction unit and the physiological characteristics of each type described above, so that the subsequent AI prediction unit performs corresponding learning, and thus the accuracy of the detection result output by the AI prediction unit may be improved. And the data is stored, and the long-term tracking monitoring of the current object to be detected can be realized, so that the integrity and comprehensiveness of the data acquired aiming at the object to be detected can be improved.

And the processing strategy in the AI prediction unit may be updated according to the prediction result and the actual feedback of the AI prediction unit, for example, the weighting information and the threshold information may be updated correspondingly as described in the foregoing embodiments.

In the processing logic of the AI prediction unit in this embodiment to predict the detection result, the basis of the processing may be determined by a plurality of rules, that is, the multiple rule sources shown in fig. 8, where the multiple rule sources may include at least one of the following: medical science and technology, doctor's diagnosis experience, and upgrade with the technology development; obtaining a result of big data analysis so as to obtain a normal state judgment interval; and updating corresponding parameters through comparison of the predicted result and the actual result, and also can comprise synthesis of other practical medical science and the like.

The invention provides an online detection monitoring system based on artificial intelligence, which carries out data statistical analysis, reasoning and learning and the like to give corresponding suggestions by detecting and monitoring various physiological signals of a human body, and finally hopes to realize panoramic monitoring of the body state and guarantee the life as much as possible.

In summary, the present disclosure can provide an on-line monitoring method for human body status at all times, which can monitor human body signals at any time, analyze the human body status by combining the advantage analysis of artificial intelligence, perform early prediction, early warning, and later warning, and perform long-term analysis and monitoring for individuals, and finally provide reference data for medical treatment, which is closer to the actual personalized treatment scheme; when the cloud samples are enough, the diseases can be reversely analyzed through the statistical principle and the AI technology, the relevant corresponding relation is obtained, and the accuracy of later-stage prediction is increased, so that the body condition of the detected object can be timely and accurately predicted and determined.

Fig. 9 is a schematic structural diagram of a physiological detection device according to an embodiment of the present disclosure. As shown in fig. 9, the physiological detection device 900 of the present embodiment may include: an acquisition module 901, a first determination module 902, a second determination module 903, and a processing module 904.

An obtaining module 901, configured to obtain multiple types of physiological characteristics of an object to be detected in a preset time period before a current time;

a first determining module 902, configured to determine, according to the multiple types of physiological characteristics, change information of each type of physiological characteristic;

a second determining module 903, configured to determine a detection result of the object to be detected according to the change information of each type of physiological characteristic.

In a possible implementation manner, for any type of physiological characteristic, the first determining module 902 is specifically configured to:

determining a change trend of the physiological characteristic relative to a previous moment according to the physiological characteristic, wherein the change trend of the physiological characteristic is any one of the following: rising and lowering;

and determining the change information of the physiological characteristics according to the change trend of the physiological characteristics.

In a possible implementation manner, the first determining module 902 is specifically configured to:

if the change trend is increasing, determining a first duration that the physiological characteristic continuously increases, or if the change trend is decreasing, determining a second duration that the physiological characteristic continuously decreases; and the number of the first and second groups,

determining a fluctuation frequency of the physiological characteristic within a target time duration before a current time;

wherein the change information includes at least one of: the change trend, the first duration, the second duration, and the fluctuation frequency.

In a possible implementation manner, the second determining module 903 is specifically configured to:

acquiring respective weight information of each type of physiological characteristics corresponding to the object to be detected;

acquiring threshold information of each type of physiological characteristics corresponding to the object to be detected;

and determining the detection result of the object to be detected according to the change information of each type of physiological characteristics, the threshold information of each type of physiological characteristics and the respective weight information of each type of physiological characteristics.

determining an evaluation parameter corresponding to each type of physiological characteristic according to the variation information of each type of physiological characteristic and threshold information of each type of physiological characteristic, wherein the threshold information comprises at least one of the following: a first threshold value corresponding to a continuously increasing change trend, a second threshold value corresponding to a continuously decreasing change trend and a third threshold value corresponding to a fluctuation frequency;

and performing weighting processing according to the evaluation parameters corresponding to each type of physiological characteristics and the weight information corresponding to each type of physiological characteristics to determine the detection result of the object to be detected.

In a possible implementation manner, the second determining module 903 is further configured to:

before obtaining threshold information of each type of physiological characteristics corresponding to the object to be detected, obtaining reference information, wherein the reference information includes at least one of the following: historical physiological characteristics, historical detection results and actual results of at least one object, wherein the at least one object comprises the object to be detected;

and performing data analysis processing according to the reference information, and determining respective threshold information of each type of physiological characteristics.

before the weight information of each type of physiological characteristics corresponding to the object to be detected is obtained, obtaining historical physiological characteristics and historical detection results of the object to be detected;

and determining the respective weight information of each type of physiological characteristics corresponding to the object to be detected according to the historical physiological characteristics and the historical detection results of the object to be detected.

In a possible implementation manner, the apparatus further includes: a processing module 904;

the processing module 904 is configured to, after the determination of the detection result of the object to be detected, send warning information to a target device if the detection result of the object to be detected indicates that the object to be detected has a risk, where the warning information is used to prompt that the object to be detected has a risk.

In a possible implementation manner, the processing module 904 is further configured to:

after the warning information is sent to the target device, acquiring an actual result of the object to be detected;

and updating the threshold information and the weight information of each type of physiological characteristics of the object to be detected according to the actual result and the detection result of the object to be detected.

The disclosure provides a physiological detection method and a physiological detection device, which are applied to the field of AI medical treatment in the field of data processing so as to achieve the purpose of improving the timeliness of determining the physiological state of an object to be detected.

It should be noted that the head model in this embodiment is not a head model for a specific user, and cannot reflect personal information of a specific user. It should be noted that the two-dimensional face image in the present embodiment is from a public data set.

In the technical scheme of the disclosure, the collection, storage, use, processing, transmission, provision, disclosure and other processing of the personal information of the related user are all in accordance with the regulations of related laws and regulations and do not violate the good customs of the public order.

The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.

According to an embodiment of the present disclosure, the present disclosure also provides a computer program product comprising: a computer program, stored in a readable storage medium, from which at least one processor of the electronic device can read the computer program, the at least one processor executing the computer program causing the electronic device to perform the solution provided by any of the embodiments described above.

FIG. 9 illustrates a schematic block diagram of an example electronic device 900 that can be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 9, the apparatus 900 includes a computing unit 901, which can perform various appropriate actions and processes in accordance with a computer program stored in a Read Only Memory (ROM)902 or a computer program loaded from a storage unit 908 into a Random Access Memory (RAM) 903. In the RAM 903, various programs and data required for the operation of the device 900 can also be stored. The calculation unit 901, ROM 902, and RAM 903 are connected to each other via a bus 904. An input/output (I/O) interface 905 is also connected to bus 904.

A number of components in the device 900 are connected to the I/O interface 905, including: an input unit 906 such as a keyboard, a mouse, and the like; an output unit 907 such as various types of displays, speakers, and the like; a storage unit 908 such as a magnetic disk, optical disk, or the like; and a communication unit 909 such as a network card, a modem, a wireless communication transceiver, and the like. The communication unit 909 allows the device 900 to exchange information/data with other devices through a computer network such as the internet and/or various telecommunication networks.

The computing unit 901 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of the computing unit 901 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The computing unit 901 performs the various methods and processes described above, such as the physiological detection method. For example, in some embodiments, the physiological detection methods can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 908. In some embodiments, part or all of the computer program may be loaded and/or installed onto device 900 via ROM 902 and/or communications unit 909. When the computer program is loaded into the RAM 903 and executed by the computing unit 901, one or more steps of the above described physiological detection method may be performed. Alternatively, in other embodiments, the computing unit 901 may be configured to perform the physiological detection method by any other suitable means (e.g. by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on 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.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.

The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The Server can be a cloud Server, also called a cloud computing Server or a cloud host, and is a host product in a cloud computing service system, so as to solve the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service ("Virtual Private Server", or simply "VPS"). The server may also be a server of a distributed system, or a server incorporating a blockchain.

It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, and the present disclosure is not limited herein.

The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.

Claims

1. A physiological detection method, comprising:

2. The method of claim 1, wherein the determining variation information for each type of physiological characteristic from the plurality of types of physiological characteristics for any type of physiological characteristic comprises:

3. The method according to claim 2, wherein the determining the change information of the physiological characteristic according to the change trend of the physiological characteristic comprises:

4. The method according to any one of claims 1 to 3, wherein the determining the detection result of the object to be detected according to the variation information of each type of physiological characteristic comprises:

5. The method according to claim 2, wherein the determining the detection result of the object to be detected according to the variation information of each type of physiological characteristic, the threshold information of each type of physiological characteristic, and the respective weight information of each type of physiological characteristic comprises:

6. The method according to claim 4 or 5, before the obtaining of the respective threshold information of each type of physiological characteristic corresponding to the object to be detected, the method further comprises:

acquiring reference information, wherein the reference information comprises at least one of the following: historical physiological characteristics, historical detection results and actual results of at least one object, wherein the at least one object comprises the object to be detected;

7. The method according to claim 4 or 5, before the obtaining of the respective weight information of each type of physiological characteristic corresponding to the object to be detected, the method further comprises:

acquiring historical physiological characteristics and historical detection results of the object to be detected;

8. The method according to any one of claims 1 to 7, further comprising, after determining the detection result of the object to be detected:

and if the detection result of the object to be detected indicates that the object to be detected has a risk, sending warning information to target equipment, wherein the warning information is used for prompting that the object to be detected has the risk.

9. The method of claim 8, after sending the alert information to the target device, the method further comprising:

acquiring an actual result of the object to be detected;

10. A physiological detection device comprising:

11. The apparatus of claim 10, wherein the first determination module is specifically configured to, for any type of physiological characteristic:

12. The apparatus of claim 11, wherein the first determining module is specifically configured to:

13. The apparatus according to any one of claims 10-12, wherein the second determining means is specifically configured to:

14. The apparatus of claim 11, wherein the second determining means is specifically configured to:

15. The apparatus of claim 13 or 14, the second determining module further to:

16. The apparatus of claim 13 or 14, the second determining module further to:

17. The apparatus of any of claims 10-16, further comprising: a processing module;

the processing module is configured to send warning information to a target device if the detection result of the object to be detected indicates that the object to be detected has a risk after the detection result of the object to be detected is determined, where the warning information is used to prompt that the object to be detected has a risk.

18. The apparatus of claim 17, the processing module further to:

19. An electronic device, comprising:

at least one processor; and

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-9.

20. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-9.

21. A computer program product comprising a computer program which, when executed by a processor, carries out the steps of the method of any one of claims 1 to 9.