Method, medium and apparatus for acquiring the delivery state of a
By acquiring continuous data values and time series, and using statistical relationships to determine the shed state of the sensor component, the problem of low accuracy in the prior art is solved and higher accuracy is achieved.
Patent Information
- Application Number
- CN202311222599.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-09-20
- Publication Date
- 2025-03-21
AI Technical Summary
The prior art is low in the accuracy of judging the shed state of the continuous blood sugar monitoring product sensor, which is disturbed by external and internal factors, resulting in misjudgment.
By obtaining the first data value, the first time series and the second time series of the time to be monitored, the shedding state of the sensor component is judged based on the statistical relationship of these data. The first time series includes data values of at least one previous time, and the second time series includes data values of a preset time period, and the error of external influence is reduced by the changing trend of these data.
The accuracy of obtaining the shed state of the sensor assembly is improved, errors caused by external factors are reduced, and it is possible to more accurately determine whether the sheds.
Smart Images
Figure CN119655749A_ABST
Abstract
Description
Technical Field
[0001] The present disclosure generally relates to the field of biomedical engineering industry, and more particularly to a method, medium and device for obtaining a detachment state of a sensor component. Background Art
[0002] Nowadays, diabetes and its chronic complications have become one of the diseases that seriously affect human health. In order to delay and reduce the chronic complications of diabetes, it is necessary to monitor blood sugar in real time. Therefore, continuous glucose monitoring (CGM) products are widely used. It can obtain the current signal value used to characterize the glucose concentration in the host tissue fluid in real time through the sensor implanted under the host's skin and send it to the background. During the use of the CGM product, if the current signal value is abnormal in the background (for example, the current signal value is slightly greater than 0 and remains for a certain time or the data discreteness of the current signal value is large), it can usually be considered that the CGM product has an abnormality during use. When the manufacturer receives feedback from the host that the CGM product is abnormal, the manufacturer is often unable to promptly determine whether the cause of the abnormality is a malfunction of the CGM product itself or the sensor falls off (for example, artificially or naturally), which is not conducive to the subsequent judgment and statistical analysis of the cause of the abnormality of the CGM product, and is not conducive to the technical improvement of the CGM product.
[0003] When an abnormality occurs in a CGM product, in order to determine whether the sensor is in a detached state, the currently more commonly used judgment method is to set a preset value, which is usually the minimum current signal value obtained by the sensor under normal working conditions, and then compare the current signal value obtained by the sensor with the preset value to determine whether the sensor is detached.
[0004] However, in the above technology, due to the influence of external factors (for example, the sensor shakes briefly due to external forces) and internal factors (for example, interference from other substances in the host tissue fluid), the current signal value obtained by the sensor may also be abnormal for a short time. In other words, the current signal value of the sensor is significantly different from the preset value, which is not necessarily caused by the sensor falling off. Therefore, the current method of judging whether the sensor is in a detached state has low accuracy. Summary of the invention
[0005] The present disclosure is proposed in view of the above-mentioned situation, and its purpose is to provide a method, medium and device for obtaining the detachment status of the sensor component, so as to improve the accuracy of obtaining the detachment status of the sensor component.
[0006] To this end, a first aspect of the present disclosure provides a method for obtaining a shedding state of a sensor component, wherein the sensor component is configured to obtain data values characterizing physiological parameters of a host, and the method comprises: obtaining a time to be monitored and a first data value corresponding to the time to be monitored; obtaining a first time series, wherein the first time series comprises at least one previous time and a second data value corresponding to the previous time, wherein the previous time is any time before the time to be monitored; obtaining a second time series, wherein the second time series comprises a preset time period and a third data value corresponding to each time within the preset time period, and the preset time period is an interval starting from the time to be monitored and having a continuous preset time; and obtaining the shedding state of the sensor component at the time to be monitored based on the statistical relationship among the first data value, the first time series, and the second time series.
[0007] In the first aspect of the present disclosure, by acquiring the time to be monitored and the corresponding first data value, acquiring a first time series including at least one previous time and a second data value corresponding to the previous time, acquiring a second time series including a preset time period and a third data value corresponding to each time in the preset time period, the preset time period is an interval starting from the time to be monitored and having a continuous preset time, and then obtaining the shedding state of the sensor component at the time to be monitored based on the statistical relationship between the first data value, the first time series and the second time series. In this case, based on the statistical relationship between the first data value, the first time series and the second time series, the relationship between the first data value at the time to be monitored, the second data value at the previous time and the third data value within the preset time period is compared, and the change trend of the data value corresponding to the time to be monitored (that is, the physiological parameter of the host) before and after the time to be monitored can be obtained. Compared with the method of obtaining the shedding state of the sensor component through the data value at a single time, the error caused by the unexpected external influence that the sensor component may be subjected to (for example, the sensor component is hit and the sensor component temporarily obtains abnormal data values, etc.) can be reduced based on the change trend, thereby improving the accuracy of obtaining the shedding state of the sensor component.
[0008] In addition, in the method involved in the first aspect of the present disclosure, optionally, the first data value, the first time series and the second time series are indicators characterizing the analyte measured by the sensor assembly. In this case, since the indicator of the analyte measured by the sensor assembly is related to the data value characterizing the physiological parameter of the host, that is, the change trend of the indicator of the analyte can reflect the working state of the sensor assembly, based on the change trend of the indicator of the analyte in the first time series and the second time series, the working state of the sensor assembly in the first time series and the second time series can be obtained to obtain the shedding state of the sensor assembly.
[0009] In addition, in the method involved in the first aspect of the present disclosure, optionally, the temperature value measured by the sensor component is obtained, and the first data value, the first time series and the second time series are the temperature values characterized by the temperature value measured by the sensor component. In this case, the sensor component is usually applied to the body surface of the host, and the change of the temperature value measured by the sensor component can reflect the working state of the sensor component to a certain extent. When the temperature value differs, the shedding state of the sensor component can be obtained in time.
[0010] In addition, in the method involved in the first aspect of the present disclosure, optionally, the statistical relationship includes the mean of the difference between the first data value and at least one of the second data values in the first time series, and at least one of the mean, variance and quantile of the third data value in the second time series. In this case, by obtaining the mean of the difference between the first data value and at least one of the second data values in the first time series, the degree of change (that is, the degree of increase or decrease) of the first data value relative to at least one of the second data values can be obtained based on the size of the mean. At the same time, by obtaining at least one of the mean, variance and quantile of the third data value in the second time series, the concentration (that is, through the mean) and the dispersion (that is, through the variance and quantile) of the third data value in the second time series can be obtained. In other words, the numerical value and volatility of the third data value can be obtained, so that the shedding state of the sensor component at the time to be monitored can be obtained based on the degree of change, concentration and dispersion.
[0011] In addition, in the method involved in the first aspect of the present disclosure, optionally, the statistical relationship also includes the difference between the mean of at least one of the second data values in the first time series and the mean of the third data value in the second time series. In this case, by obtaining the difference between the mean of at least one of the second data values in the first time series and the mean of the third data value in the second time series, since the mean can reflect the size of the data value in the time series, the degree of change of the data values in the time series before and after the time to be monitored (that is, the first time series and the second time series) can be obtained based on the size of the difference between the two means, so that the shedding state of the sensor component at the time to be monitored can be obtained based on the degree of change.
[0012] In addition, in the method involved in the first aspect of the present disclosure, optionally, the statistical relationship also includes the difference between the quantile of at least one of the second data values in the first time series and the quantile of the third data value in the second time series. In this case, since the quantile can be used as another reference for the numerical values of the data values in the first time series (that is, the second data value) and the data values in the second time series (that is, the third data value), for example, the size of the middle value (that is, the median) of the data value can be obtained based on the quantile, and by obtaining the difference in quantiles, the degree of change of the data values in the time series before and after the time to be monitored (that is, the first time series and the second time series) can be better obtained based on the size of the difference in quantiles, thereby improving the accuracy of obtaining the detachment state of the sensor component at the time to be monitored based on the degree of change.
[0013] In addition, in the method involved in the first aspect of the present disclosure, optionally, the statistical relationship includes the difference between the first data value and the mean of at least one of the second data values in the first time series, and at least one of the means, variances and quantiles of the plurality of third data values. In this case, by obtaining the mean of the second data value, the central tendency of the plurality of second data values in the first time series can be reflected, and the degree of change (that is, the degree of increase or decrease) of the first data value relative to the mean of the second data value can be obtained based on the difference between the first data value and the mean. At the same time, by obtaining at least one of the mean, variance and quantile of the third data value in the second time series, the degree of concentration (that is, through the mean) and the degree of dispersion (that is, through the variance and quantile) of the third data value in the second time series can be obtained, that is, the numerical value and volatility of the third data value can be obtained, so that the shedding state of the sensor component at the time to be monitored can be obtained based on the degree of change, the degree of concentration and the degree of dispersion.
[0014] In addition, in the method involved in the first aspect of the present disclosure, optionally, it also includes denoising the first time series and the second time series. In this case, the abnormal data values in the first time series and the second time series can be eliminated, so that the reliability of the first time series and the second time series can be improved, and then the accuracy of obtaining the detachment state of the sensor component can be improved.
[0015] The second aspect of the present disclosure provides a computer-readable storage medium, wherein the computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by a processor, the method involved in the first aspect of the present disclosure is implemented. Thus, the accuracy of obtaining the detachment state of the sensor component can be improved.
[0016] The third aspect of the present disclosure provides a computer device, the computer device comprising a processor and a memory, the processor executing a program stored in the memory to implement the method involved in the first aspect of the present disclosure, thereby improving the accuracy of obtaining the detachment state of the sensor component.
[0017] According to the present disclosure, a method, a medium, and a device for obtaining the detachment status of a sensor component can be provided, thereby improving the accuracy of obtaining the detachment status of the sensor component. BRIEF DESCRIPTION OF THE DRAWINGS
[0018] The present disclosure will now be explained in further detail, by way of example only, with reference to the accompanying drawings.
[0019] Figure 1 It is a schematic diagram showing the application scenario of the acquisition sensor component involved in the example of the present disclosure.
[0020] Figure 2 is a flowchart illustrating a method involved in examples of the present disclosure.
[0021] Figure 3A It is a first schematic diagram showing the detachment state of the sensor assembly at the time to be monitored involved in the present disclosure.
[0022] Figure 3B It is a schematic diagram showing that the acquisition sensor assembly involved in the present disclosure is not in a detached state at the time to be monitored.
[0023] Figure 3C It is a second schematic diagram showing the detachment state of the sensor assembly involved in the present disclosure at the time to be monitored.
[0024] Figure 3D It is a third schematic diagram showing the detachment state of the sensor assembly involved in the present disclosure at the time to be monitored.
[0025] Figure 4 is a flowchart illustrating a calculation method involved in the example of the present disclosure.
[0026] Figure 5 Detailed description of the invention is given in detail below. DETAILED DESCRIPTION
[0027] Hereinafter, the preferred embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. In the following description, the same symbols are assigned to the same components, and repeated descriptions are omitted. In addition, the accompanying drawings are only schematic diagrams, and the ratio of the dimensions of the components or the shapes of the components may be different from the actual ones.
[0028] It should be noted that the terms "including" and "having" and any variations thereof in the present disclosure, such as a process, method, system, product or device that includes or has a series of steps or units, are not necessarily limited to those steps or units clearly listed, but may include or have other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.
[0029] All methods described in the present disclosure may be performed in any suitable order, unless otherwise indicated herein or clearly contradictory to the context. The use of any and all examples or exemplary language (e.g., "for example") provided with respect to certain examples in the present disclosure is intended only to better illustrate the present disclosure, rather than to limit the scope of the present disclosure claimed. Any language in the specification should not be interpreted as indicating any non-claimed element that is essential to the implementation of the present disclosure.
[0030] The present disclosure provides a method for obtaining the detachment state of a sensor component (hereinafter referred to as the method), which is a method for determining whether the sensor component is in a detachment state. The method provided by the present disclosure can improve the accuracy of obtaining the detachment state of the sensor component.
[0031] The method for obtaining the detachment status of a sensor component involved in the present disclosure may also be referred to as a judgment method, a sensor component status acquisition method, a sensor component detachment identification method, or a sensor component detachment time output method, etc. The method involved in the present disclosure may be applicable to any application scenario for obtaining the detachment status of a sensor component.
[0032] In some examples, the sensor assembly involved in the present disclosure can be applied to the body surface of the host to obtain physiological parameters of the host.
[0033] In some examples, the sensor assembly may include an analyte sensor, which may be fully or partially implanted under the skin of the host, and the analyte sensor may generate specific analyte information in the body fluid based on the body fluid. For example, it reacts with the analyte in the body fluid and generates analyte information. In this case, the analyte sensor can facilitate the acquisition of analyte information (also referred to as an indicator of the analyte) in the body fluid.
[0034] In some examples, the analyte sensor may be attached to the body surface of the host to obtain analyte information.
[0035] In some examples, the sensor assembly involved in the present disclosure can also obtain physiological parameters such as sweat information or body temperature from the host's body surface.
[0036] In some examples, the detached state of the sensor assembly involved in the present disclosure may refer to the sensor assembly being completely or partially detached from the host macroscopically. In other words, the sensor assembly in the detached state may be an independent sensor assembly that is not attached to the host.
[0037] In some examples, the detached state of the sensor assembly may also mean that although the sensor assembly is still attached to the host's body surface macroscopically, the analyte sensor of the sensor assembly has completely or partially lost contact with the host's analyte and cannot obtain more accurate analyte information.
[0038] In some examples, the physiological parameters involved in the present disclosure can be obtained based on the analyte information. For example, physiological parameters such as the host's blood sugar level or blood oxygen saturation can be obtained based on the analyte information.
[0039] In the present disclosure, the analyte targeted by the sensor component can be one or more of glucose, acetylcholine, amylase, bilirubin, cholesterol, chorionic gonadotropin, creatine kinase, creatine, creatinine, DNA, fructosamine, glutamine, growth hormone, hormone, ketone body, lactate, oxygen, peroxide, prostate-specific antigen, prothrombin, RNA, thyroid-stimulating hormone or troponin.
[0040] In other examples, the sensor assembly can also monitor the concentration of drugs in body fluids. For example, the sensor assembly can also monitor the concentration of antibiotics (e.g., gentamicin or vancomycin, etc.), digitoxin, digoxin, theophylline or warfarin, etc. in body fluids.
[0041] The present disclosure is described below mainly by taking glucose as an example of the analyte. It should be noted that for other analytes, those skilled in the art can perform analysis with slight modifications.
[0042] Figure 1 It is a schematic diagram showing an application scenario of the acquisition sensor assembly 1 involved in the example of the present disclosure.
[0043] In some examples, the methods disclosed herein can be applied to Figure 1 In some examples, see Figure 1 , the sensor assembly 1 can be applied to the body surface of the host 2 or partially placed in the subcutaneous environment of the host 2 to obtain data values representing physiological parameters, and then communicate with the background 3 and send the data values to the background 3, and then the background 3 can obtain the shedding state of the sensor assembly 1 based on the data values. In some examples, the background 3 can be a smart phone, a smart tablet computer, or an electronic computer.
[0044] In some examples, the sensor assembly 1 can be configured to acquire data values representative of physiological parameters of the host 2 .
[0045] In some examples, the data values of the physiological parameters of the host 2 obtained by the sensor component 1 can form a time series. In some examples, the time series can refer to a collection of multiple data values arranged in time order. That is, the data values and the time corresponding to the data values can be obtained based on the time series. In some examples, the time series can be continuous or discontinuous.
[0046] In some examples, the sensor assembly 1 may include an implantable sensor. In some examples, the implantable sensor may be completely or partially placed in the subcutaneous environment of the host 2 to obtain data values representing physiological parameters of the host 2. In some examples, the data values representing the physiological parameters of the host 2 may be indicators of an analyte. For example, the indicator of an analyte may be the concentration, solubility, acidity, conductivity, or viscosity of the analyte.
[0047] In some examples, the implantable sensor can react with the analyte and generate a current. At this time, the current signal value of the current can be used to characterize the index of the analyte. In other words, the index of the analyte can be obtained based on the current signal value.
[0048] In some examples, the implanted sensor may also obtain the temperature of host 2.
[0049] In some examples, the sensor assembly 1 may include an electronic module, which may be electrically connected to the implanted sensor to obtain a current signal value representing an indicator of the analyte, and may be communicatively connected to the background 3 and send the current signal value to the background 3. In this case, since the current signal value is used to represent the indicator of the analyte, the background 3 can obtain the indicator of the analyte based on the current signal value.
[0050] In some examples, the sensor assembly 1 may be a glucose sensor that can obtain the glucose concentration in the body fluid (i.e., tissue fluid or blood) of the host 2. In some examples, the glucose sensor may include a glucose enzyme layer that can react with glucose in the tissue fluid or blood and generate a data value representing the glucose concentration.
[0051] In some examples, the sensor assembly 1 may include a non-implantable sensor. In other words, the sensor assembly 1 may obtain analyte information of the host 2 non-invasively.
[0052] In some examples, the non-implantable sensor may be a heart rate sensor, which may be used to obtain the heart rate of the host 2 .
[0053] In some examples, the non-implantable sensor may be an optical sensor. In this case, the analyte information of the host 2 can be obtained in a non-invasive manner through optical detection.
[0054] In some examples, the sensor assembly 1 may include a temperature sensor, which may be used to obtain the temperature of the body surface of the host 2 or the surrounding environment.
[0055] In some examples, the temperature sensor may include a thermistor. In some examples, the resistance value of the thermistor may change with temperature. Therefore, the resistance value of the thermistor may also change when the ambient temperature changes. In this case, the temperature of the body surface of the host 2 can be collected by measuring the resistance value of the thermistor.
[0056] In some examples, the sensor assembly 1 may also include a skin electrical sensor, which may be used to measure the resistance or conductivity of the host 2 skin.
[0057] In some examples, the sensor assembly 1 may include an implantable sensor and a non-implantable sensor. In other words, the sensor assembly 1 may be a sensor having an integrated structure including an implantable portion and a non-implantable portion. In some examples, the sensor assembly 1 may include a glucose sensor and a temperature sensor.
[0058] In some examples, the sensor assembly 1 may include a glucose sensor as an implanted portion and a temperature sensor as a non-implanted portion. However, the present disclosure is not limited thereto, and the glucose sensor may also be used as a non-implanted portion of the sensor assembly 1 to obtain data values representing physiological parameters of the host 2.
[0059] Figure 2 is a flowchart illustrating a method involved in examples of the present disclosure.
[0060] For some examples, see Figure 2 The method may include obtaining a time to be monitored and a first data value corresponding to the time to be monitored (step S101), obtaining a first time series and a second time series (step S103), and obtaining the detachment state of the sensor component 1 at the time to be monitored based on the statistical relationship among the first data value, the first time series and the second time series (step S105).
[0061] In some examples, in step S101, the time to be monitored may be obtained. Specifically, the time to be monitored of the sensor assembly 1 may be obtained.
[0062] In some examples, the sensor assembly 1 can be configured to obtain data values. Specifically, the sensor assembly 1 can be configured to obtain data values representing physiological parameters of the host 2.
[0063] In some examples, the sensor assembly 1 can be configured to obtain at least one data value. In some examples, the sensor assembly 1 can be configured to obtain at least one data value and a time corresponding to the data value. In other words, the sensor assembly 1 can be configured to obtain at least one time and a data value corresponding to the at least one time.
[0064] In some examples, the time to be monitored of the sensor component 1 may be any one of the at least one time mentioned above. In some examples, the time to be monitored of the sensor component 1 may also be multiple.
[0065] In some examples, in step S101, the time to be monitored and the first data value corresponding to the time to be monitored may be obtained. In some examples, the first data value may be a data value representing a physiological parameter of the host 2 at the time to be monitored.
[0066] In some examples, at least one time to be monitored may be obtained. In some examples, at least one first data value may be obtained, and the first data value may correspond to the time to be monitored.
[0067] In some examples, the first data value may be an indicator of the analyte. Specifically, the first data value may be an indicator characterizing the analyte measured by the sensor assembly 1 .
[0068] In some examples, step S101 may also include obtaining a temperature value measured by the sensor assembly 1 .
[0069] In some examples, the first data value may be a temperature value. Specifically, the first data value may be a temperature value representing the temperature measured by the sensor assembly 1 .
[0070] In some examples, in step S103 , a first time series may be acquired.
[0071] In some examples, the first time series may include at least one previous moment and a second data value corresponding to the previous moment. For example, the first time series may include 1, 10, 100, or 1000 previous moments.
[0072] In some examples, the first time series may include a previous moment and a second data value corresponding to the previous moment. In some examples, the first time series may include multiple previous moments and second data values corresponding to the multiple previous moments. That is, there may be multiple second data values.
[0073] In some examples, the previous moment may be any moment before the moment to be monitored. In some examples, the previous moment may be a previous moment adjacent to the moment to be monitored. In some examples, the moment to be monitored may be the last moment of the first time series. In some examples, the moment to be monitored may also be adjacent to the last moment of the first time series.
[0074] In some examples, the time length of the first time series may be 10 minutes to 30 minutes. For example, the time length of the first time series may be 10 minutes, 15 minutes, 20 minutes, 25 minutes, or 30 minutes.
[0075] In some examples, the first time series may be an indicator of an analyte. In some examples, the first time series may be an indicator of an analyte measured by sensor assembly 1. In some examples, the second data value may be an indicator of an analyte. In some examples, the second data value may be an indicator of an analyte measured by sensor assembly 1.
[0076] In some examples, the first time series may be a temperature value. In some examples, the first time series may be a temperature value characterizing the temperature value measured by the sensor assembly 1. In some examples, the second data value may be a temperature value. In some examples, the second data value may be a temperature value characterizing the temperature value measured by the sensor assembly 1.
[0077] In some examples, the temperature value measured by the sensor assembly 1 may be at least one of a temperature value of the body surface of the host 2 , a temperature value under the skin of the host 2 , or a temperature value of the environment surrounding the sensor assembly 1 .
[0078] In some examples, in step S103, the second time series may be obtained. In some examples, the time to be monitored may be the first time of the second time series. In some examples, the time to be monitored may also be adjacent to the first time of the second time series.
[0079] In some examples, the second time series may include a preset time period. In some examples, the preset time period may be an interval starting from the time to be monitored and having a continuous preset time.
[0080] In some examples, the preset time can be set manually, that is, the preset time can be a preset value. For example, the preset time can be set manually to 10 minutes, 15 minutes, or 20 minutes, etc.
[0081] In some examples, the second time series may include a preset time period and a third data value corresponding to each moment in the preset time period. That is, the second time series may include at least one third data value. For example, the second time series may include 1 third data value, 10 third data values, 100 third data values, or 1000 third data values.
[0082] In some examples, the second time series may be an indicator of the analyte. In some examples, the second time series may be an indicator of the analyte measured by the sensor assembly 1. In some examples, the third data value may be an indicator of the analyte. In some examples, the third data value may be an indicator of the analyte measured by the sensor assembly 1.
[0083] In some examples, the second time series may be a temperature value. In some examples, the second time series may be a temperature value characterizing the temperature value measured by the sensor assembly 1. In some examples, the third data value may be a temperature value. In some examples, the third data value may be a temperature value characterizing the temperature value measured by the sensor assembly 1.
[0084] In some examples, the time length of the second time series may be 10 minutes to 30 minutes. For example, the time length of the second time series may be 10 minutes, 15 minutes, 20 minutes, 25 minutes, or 30 minutes.
[0085] In some examples, in step S103, the method may further include performing denoising on the first time series and the second time series. In this case, the abnormal data values in the first time series and the second time series can be eliminated, thereby improving the reliability of the first time series and the second time series, and further improving the accuracy of obtaining the detachment state of the sensor assembly 1.
[0086] In some examples, the denoising process may include at least one of mean filtering, median filtering, wavelet denoising, and adaptive filtering.
[0087] In some examples, in step S105, the detachment state of the sensor assembly 1 at the time to be monitored may be acquired based on the statistical relationship among the first data value, the first time series, and the second time series.
[0088] In some examples, the first data value, the first time series, and the second time series may be indicators that characterize the analyte measured by the sensor assembly 1. In this case, since the indicator of the analyte measured by the sensor assembly 1 is related to the data value that characterizes the physiological parameter of the host 2, that is, the change trend of the indicator of the analyte can reflect the working state of the sensor assembly 1, based on the change trend of the indicator of the analyte in the first time series and the second time series, the working state of the sensor assembly 1 in the first time series and the second time series can be obtained to obtain the shedding state of the sensor assembly 1.
[0089] In some examples, the first data value, the first time series, and the second time series may be temperature values representing the temperature measured by the sensor assembly 1. In this case, the sensor assembly 1 is usually applied to the body surface of the host 2, and the change in the temperature value measured by the sensor assembly 1 can reflect the working state of the sensor assembly 1 to a certain extent. When the temperature value differs, the shedding state of the sensor assembly 1 can be obtained in time.
[0090] In some examples, the statistical relationship may include the difference between the first data value and the second data value, and the mean of the third data value. In this case, the degree of change (i.e., the degree of increase or decrease) of the first data value relative to at least one second data value can be obtained based on the difference between the first data value and the second data value, and the magnitude of the third data value can be obtained based on the mean of the third data value, so that the shedding state of the sensor assembly 1 at the time to be monitored can be obtained based on the degree of change and the magnitude of the third data value.
[0091] In some examples, the statistical relationship may include the mean of the difference between the first data value and at least one second data value in the first time series (i.e., averaging the multiple differences between the first data value and the multiple second data values), and at least one of the mean, variance, and quantile of the third data value in the second time series. In this case, by obtaining the mean of the difference between the first data value and at least one second data value in the first time series, the degree of change (i.e., the degree of increase or decrease) of the first data value relative to the at least one second data value can be obtained based on the size of the mean. At the same time, by obtaining at least one of the mean, variance, and quantile of the third data value in the second time series, the concentration (i.e., through the mean) and dispersion (i.e., through the variance and quantile) of the third data value in the second time series can be obtained. In other words, the numerical value and volatility of the third data value can be obtained, so that the shedding state of the sensor component 1 at the time to be monitored can be obtained based on the degree of change, concentration, and dispersion.
[0092] In some examples, the degree of change and the degree of dispersion can be reflected by setting thresholds. For example, it can be reflected by setting a first threshold and a second threshold. In some examples, when the difference between the first data value and the second data value is greater than or equal to the first threshold, it can be considered that the degree of change is large. In some examples, when the variance of the third data value is less than or equal to the second threshold, it can be considered that the degree of dispersion of the third data value is small (that is, the volatility of the third data value is small).
[0093] In some examples, the first threshold may have at least one, and the second threshold may have at least one. In some examples, at least one first threshold and at least one second threshold may be set based on an indicator of at least one analyte. In some examples, the first threshold and the second threshold may be set based on a temperature value.
[0094] In some examples, the first threshold and the second threshold can be adjusted based on actual needs. In some examples, the first threshold can be adjusted based on the type of the difference in data values. For example, the type of the difference in data values can include the difference between the first data value and the second data value, the average of the differences between the first data value and a plurality of second data values, or the difference between the first data value and the average of the second data values.
[0095] In some examples, the statistical relationship can include the mean of the difference between the first data value and at least one second data value in the first time series (i.e., averaging multiple differences between the first data value and multiple second data values), and the mean of the third data value in the second time series.
[0096] In some examples, the statistical relationship can include the mean of the difference between the first data value and at least one second data value in the first time series (i.e., averaging multiple differences between the first data value and multiple second data values), and the variance of a third data value in the second time series.
[0097] In some examples, the statistical relationship can include the mean of the difference between the first data value and at least one second data value in the first time series (i.e., averaging multiple differences between the first data value and multiple second data values), and the quantile of a third data value in the second time series.
[0098] In some examples, the statistical relationship may also include the difference between the mean of at least one second data value in the first time series and the mean of the third data value in the second time series. In this case, by obtaining the difference between the mean of at least one second data value in the first time series and the mean of the third data value in the second time series, since the mean can reflect the size of the data value in the time series, the degree of change of the data values in the time series before and after the time to be monitored (that is, the first time series and the second time series) can be obtained based on the size of the difference between the two means, so that the shedding state of the sensor component 1 at the time to be monitored can be obtained based on the degree of change.
[0099] In some examples, the statistical relationship may also include the difference between the quantile of at least one second data value in the first time series and the quantile of the third data value in the second time series. In this case, since the quantile can serve as another reference for the numerical values of the data values in the first time series (i.e., the second data value) and the data values in the second time series (i.e., the third data value), for example, the size of the middle value (i.e., the median) of the data value can be obtained based on the quantile, and by obtaining the difference in quantiles, the degree of change of the data values in the time series before and after the time to be monitored (i.e., the first time series and the second time series) can be better obtained based on the size of the difference in quantiles, thereby improving the accuracy of obtaining the detachment state of the sensor component 1 at the time to be monitored based on the degree of change.
[0100] In some examples, the statistical relationship may also include the difference between the interquartile range of at least one second data value in the first time series and the interquartile range of the third data value in the second time series. In this case, since the interquartile range can better reflect the degree of dispersion (i.e., volatility), the degree of change in the volatility of the data values in the time series before and after the time to be monitored (i.e., the first time series and the second time series) can be better obtained based on the size of the difference in the interquartile range, thereby improving the accuracy of obtaining the shedding state of the sensor component 1 at the time to be monitored based on the degree of change.
[0101] In some examples, the statistical relationship may include the difference between the first data value and the mean of at least one second data value in the first time series, and at least one of the means, variances, and quantiles of multiple third data values. In this case, by obtaining the mean of the second data value, the central tendency of multiple second data values in the first time series can be reflected, and the degree of change (that is, the degree of increase or decrease) of the first data value relative to the mean of the second data value can be obtained based on the difference between the means of the first data value and the second data value. At the same time, by obtaining at least one of the mean, variance, and quantile of the third data value in the second time series, the degree of concentration (that is, through the mean) and the degree of dispersion (that is, through the variance and quantile) of the third data value in the second time series can be obtained. In other words, the numerical value and volatility of the third data value can be obtained, so that the shedding state of the sensor component 1 at the time to be monitored can be obtained based on the degree of change, the degree of concentration, and the degree of dispersion.
[0102] In some examples, the statistical relationship may include a difference between the first data value and a mean of at least one second data value in the first time series, and a mean of the plurality of third data values.
[0103] In some examples, the statistical relationship may include a difference between the first data value and a mean of at least one second data value in the first time series, and a variance of a plurality of third data values.
[0104] In some examples, the statistical relationship may include a difference between the first data value and a mean of at least one second data value in the first time series, and quantiles of a plurality of third data values.
[0105] Figure 3A It is a first schematic diagram showing the detachment state of the sensor assembly 1 at the time to be monitored involved in the present disclosure. Figure 3B It is a schematic diagram showing that the acquisition sensor assembly 1 involved in the present disclosure is not in a detached state at the time to be monitored. Figure 3C It is a second schematic diagram showing the detachment state of the sensor assembly 1 involved in the present disclosure at the time to be monitored. Figure 3D It is a third schematic diagram showing the detachment state of the sensor assembly 1 involved in the present disclosure at the time to be monitored.
[0106] In some examples, such as Figure 3A and Figure 3B As shown, the first data value, the first time series and the second time series may be an index characterizing the analyte measured by the sensor assembly 1 and a temperature value characterizing the temperature value measured by the sensor assembly 1. Therefore, the shedding state of the sensor assembly 1 at the time to be monitored may be obtained based on whether the index of the analyte and the temperature value simultaneously satisfy the above statistical relationship.
[0107] In some examples, such as Figure 3C As shown, the first data value, the first time series and the second time series may be indicators characterizing the analyte measured by the sensor assembly 1. Therefore, the shedding state of the sensor assembly 1 at the time to be monitored may be obtained based on whether the indicator of the analyte satisfies the above statistical relationship.
[0108] In some examples, such as Figure 3D As shown, the first data value, the first time series and the second time series may be temperature values representing the temperature measured by the sensor assembly 1. Therefore, the shedding state of the sensor assembly 1 at the time to be monitored may be obtained based on whether the temperature value satisfies the above statistical relationship.
[0109] In the present disclosure, by acquiring the time to be monitored and the corresponding first data value, acquiring a first time series including at least one previous time and a second data value corresponding to the previous time, acquiring a second time series including a preset time period and a third data value corresponding to each time in the preset time period, the preset time period is an interval starting from the time to be monitored and having a continuous preset time, and then acquiring the shedding state of the sensor component 1 at the time to be monitored based on the statistical relationship between the first data value, the first time series and the second time series. In this case, based on the statistical relationship between the first data value, the first time series and the second time series, the relationship between the first data value at the time to be monitored, the second data value at the previous time and the third data value within the preset time period is compared, and the change trend of the data value corresponding to the time to be monitored (that is, the physiological parameter of the host 2) before and after the time to be monitored can be acquired. Compared with the method of acquiring the shedding state of the sensor component 1 through the data value at a single time, the error caused by the unexpected external influence that the sensor component 1 may be subjected to (for example, the sensor component 1 is hit and the sensor component 1 temporarily acquires abnormal data values, etc.) can be reduced based on the change trend, thereby improving the accuracy of acquiring the shedding state of the sensor component 1.
[0110] The present disclosure also relates to a calculation method for outputting the falling-off moment of the sensor assembly 1 .
[0111] Figure 4 is a flowchart illustrating a calculation method involved in the example of the present disclosure.
[0112] In some examples, the calculation method can obtain the detachment state of the sensor component 1 at the time to be monitored based on the above method.
[0113] For some examples, see Figure 4 The calculation method may include obtaining a plurality of moments to be monitored and obtaining the detachment state of the sensor assembly 1 at the plurality of moments to be monitored (step S301), and obtaining the detachment moment from the plurality of moments to be monitored (step S302).
[0114] In some examples, in step S301, a calculation time period may be obtained, and the multiple moments to be monitored may be partial moments or all moments of the calculation time period.
[0115] In some examples, in step S301, multiple time points to be monitored may be obtained and the shedding state of the sensor assembly 1 at the multiple time points to be monitored may be obtained. The shedding state may be obtained based on the above method.
[0116] In some examples, in step S302, the falling-off moment may be obtained from a plurality of moments to be monitored. In some examples, the falling-off moment may be the first moment among a plurality of moments to be detected when the sensor assembly 1 is in a falling-off state.
[0117] The examples disclosed herein also relate to a computer-readable storage medium. A person of ordinary skill in the art may understand that all or part of the steps in the method in the above examples may be completed by instructing related hardware through a program (instructions). The program (instructions) may be stored in a computer-readable memory (storage medium). The computer-readable memory may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.
[0118] The present disclosure also provides a computer device 4 .
[0119] Figure 5 2 is a block diagram showing the structure of a computer device 4 involved in the example of the present disclosure.
[0120] For some examples, see Figure 5 The computer device 4 may include a processor 40 and a memory 41. The processor 40 and the memory 41 may be connected to a communication bus respectively. The memory 41 may be a high-speed RAM memory or a non-volatile memory 41. Those skilled in the art may understand that Figure 5 The structure of the computer device 4 shown in the figure does not constitute a limitation on the present disclosure, and can be a bus structure, a star structure, or can include Figure 5 More or fewer components may be shown, or certain components may be combined, or the components may be arranged differently.
[0121] Among them, the processor 40 can be the control center of the computer device 4, the processor 40 can be a central processing unit (CPU), and the processor 40 can use various interfaces and lines to connect various parts of the entire computer device 4, and is used to run or execute software programs and / or modules stored in the memory 41, and call the program code stored in the memory 41 to execute all or part of the operations in the above-mentioned method.
[0122] In the present disclosure, it should be understood that the disclosed devices can be implemented in other ways. For example, the device implementation described above is only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical or other forms.
[0123] In some examples, the computer device 4 may be an embedded device (ie, an offline device), which may implement the above method by burning a program.
[0124] In some examples, the computer device 4 may be a cloud server or a physical server, and the cloud server or the physical server may establish a communication connection with the sensor assembly 1 through a network. Therefore, the method involved in the present disclosure may be directly set on the cloud server or the physical server.
[0125] In some examples, the computer device 4 may be a smart terminal, such as a smart mobile phone or a smart tablet computer, and the smart terminal may establish a communication connection with the sensor assembly 1 through a network. Therefore, the method involved in the present disclosure may be set in the smart terminal.
[0126] The method involved in the present disclosure can be directly set on a smart terminal, and the smart terminal (for example, a smart mobile phone or a smart tablet) can receive the communication signal of the sensor component 1 (that is, the data value obtained by the sensor component 1), and then execute the above method on the smart terminal to obtain the shedding state of the sensor component 1 at the time to be monitored. In this way, it is convenient for the user to check the shedding state of the sensor component 1.
[0127] The method involved in the present disclosure can be set on a cloud server or a physical server. After the communication signal of the sensor component 1 is transmitted to the smart terminal, it is transmitted to the cloud server or the physical server. Then, the above method is executed on the cloud server or the physical server to obtain the detachment state of the sensor component 1 at the time to be monitored. Finally, the detachment state of the sensor component 1 at the time to be monitored is transmitted to the smart terminal. At this time, the smart terminal can be used to display the detachment state of the sensor component 1 at the time to be monitored. In this case, since the computing power of the cloud server or the physical server is strong, it can support large-flow and multi-concurrency data processing, thereby supporting the processing of the detachment state of the sensor component 1 of multiple users.
[0128] The method involved in the present disclosure can be set on an embedded board, and the embedded board can be located in the sensor component 1, that is, the method involved in the present disclosure can be directly set on the sensor component 1. In this case, the loss of data transmission can be reduced, so that the accuracy can be higher.
[0129] According to the present disclosure, a method, a medium, and a device for obtaining the detachment status of the sensor component 1 can be provided, thereby improving the accuracy of obtaining the detachment status of the sensor component 1.
[0130] Although the present disclosure is specifically described above in conjunction with the accompanying drawings and examples, it is to be understood that the above description does not limit the present disclosure in any form. Those skilled in the art may modify and change the present disclosure as needed without departing from the essential spirit and scope of the present disclosure, and these modifications and changes all fall within the scope of the present disclosure.
Claims
1. A method for obtaining a shedding state of a sensor assembly, wherein the sensor assembly is configured to obtain a data value representing a physiological parameter of a host, characterized in that: The method comprises: Acquire a time to be monitored and a first data value corresponding to the time to be monitored; Acquire a first time series, the first time series comprising at least one previous moment and a second data value corresponding to the previous moment, the previous moment being any moment before the moment to be monitored; Acquire a second time series, wherein the second time series includes a preset time period and a third data value corresponding to each moment in the preset time period, and the preset time period is an interval with a continuous preset time starting from the moment to be monitored; The shedding state of the sensor component at the time to be monitored is obtained based on the statistical relationship between the first data value, the first time series and the second time series.
2. The method according to claim 1, characterized in that The first data value, the first time series, and the second time series are indicators of an analyte measured by the sensor assembly.
3. The method according to claim 1, characterized in that A temperature value measured by the sensor component is obtained, wherein the first data value, the first time series, and the second time series represent the temperature value measured by the sensor component.
4. The method according to any one of claims 1 to 3, characterized in that: The statistical relationship includes a mean of the difference between the first data value and at least one of the second data values in the first time series, and at least one of a mean, a variance and a quantile of the third data value in the second time series.
5. The method according to claim 4, characterized in that The statistical relationship also includes a difference between a mean of at least one of the second data values in the first time series and a mean of the third data values in the second time series.
6. The method according to claim 4, characterized in that The statistical relationship also includes a difference between a quantile of at least one of the second data values in the first time series and a quantile of the third data value in the second time series.
7. The method according to any one of claims 1 to 3, characterized in that: The statistical relationship includes a difference between the first data value and a mean of at least one of the second data values in the first time series, and at least one of a mean, a variance, and a quantile of a plurality of the third data values.
8. The method according to any one of claims 1 to 3, characterized in that: The method also includes performing denoising processing on the first time series and the second time series.
9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by a processor, the method according to any one of claims 1 to 8 is implemented.
10. A computer device, characterized in that: The computer device comprises a processor and a memory, and the processor executes a program stored in the memory to implement the method according to any one of claims 1 to 8.