Electronic equipment and method for controlling same

Abstract

The invention provides electronic equipment and a method for controlling the electronic equipment. The method comprises: acquiring sensor data indicative of a concentration of an analyte in a host; displaying a user interface (UI) element indicating a value of the sensor data; determining whether to perform post-processing on the sensor data based on the sensor data; performing post-processing on the sensor data based on the judgment result; and updating the display of the UI element based on the post-processed sensor data.

Description

[0001] [CROSS-REFERENCE TO RELATED APPLICATIONS]

[0002] This application claims priority to and the benefit of Korean Patent Application No. 10-2023-0154385, filed on November 9, 2023, the disclosure of which is incorporated herein by reference in its entirety. Technical Field

[0003] The present disclosure relates to an electronic device and a method for controlling the same, and more particularly to an electronic device for processing sensor data indicating an analyte concentration in a host and a method for controlling the same. Background Art

[0004] A continuous glucose monitoring system (CGMS) is a system that uses a sensor in contact with a user's body fluid (e.g., interstitial fluid) to obtain a user's blood glucose concentration and provide the blood glucose concentration to the user. The CGMS includes a continuous glucose monitor (CGM) attached to the user's body to detect signals from the user's body fluid and a user terminal device that provides the blood glucose concentration to the user.

[0005] The user terminal device provides the user's blood sugar concentration based on the signal detected by the CGM, but the signal detected by the CGM may have noise. For this reason, the blood sugar concentration provided to the user may be distorted and the user may be confused.

[0006] Therefore, it is necessary to adopt techniques for dealing with noise in the signals measured by the CGM to avoid confusing the user. Summary of the invention

[0007] The present disclosure is directed to providing an electronic device for minimizing distortion of a user's blood glucose concentration value.

[0008] The present disclosure also relates to providing an electronic device for displaying blood glucose concentration in a manner that is convenient for users to view.

[0009] The technical objectives of the present disclosure are not limited to the technical objectives set forth above, and ordinary technicians in the relevant field will clearly understand other technical objectives not set forth above from the following description.

[0010] According to one aspect of the present disclosure, a method for controlling an electronic device is provided, the method comprising: acquiring sensor data indicating the concentration of an analyte in a host; displaying a user interface (UI) element indicating the value of the sensor data; determining whether to post-process the sensor data based on the sensor data; post-processing the sensor data based on a result of the determination; and updating the display of the UI element based on the post-processed sensor data.

[0011] Determining whether to perform post-processing may include: when the standard deviation of a predetermined number of data points included in the sensor data is greater than a preset threshold, determining that post-processing is performed; and when the standard deviation of the data points is a preset threshold or less than a preset threshold, determining not to perform post-processing.

[0012] The sensor data may include a first data point corresponding to a first time point and a second data point corresponding to a second time point adjacent to the first time point, and determining whether to perform post-processing may include: when the time interval between the first time point and the second time point is shorter than a preset threshold time, determining that post-processing is performed; and when the time interval is the preset threshold time or longer than the preset threshold time, determining that post-processing is not performed.

[0013] Implementing post-processing may include: applying a predefined filter to a first data set consisting of multiple data points corresponding to a first time period to correct the values ​​of data points corresponding to a predetermined index among the multiple data points; using the data points whose values ​​have been corrected to generate a second data set corresponding to a second time period after the first time period; and applying the predefined filter to the second data set to correct the values ​​of data points corresponding to the predetermined index among the multiple data points included in the second data set.

[0014] The first time period may overlap with at least a portion of the second time period.

[0015] Applying the predefined filter to the first data set may include: normalizing the first data set; performing a convolution operation on the first data set using the predefined filter; removing distortion of the first data set; and denormalizing the first data set.

[0016] Normalizing the first data set may include subtracting a value of a data point corresponding to a last index among the plurality of data points included in the first data set from a value of each data point included in the first data set.

[0017] Updating the display of the UI element may include updating the display of the UI element such that the UI element indicates a value of the post-processed sensor data.

[0018] The method may also include calculating a post-processing count for each of a plurality of data points included in the sensor data, and updating the display of the UI element may include displaying a plurality of UI elements corresponding to the plurality of data points to be visually distinguishable based on the post-processing count.

[0019] Updating the display of the UI elements may include displaying the plurality of UI elements based on whether the post-processing count satisfies a predetermined count.

[0020] According to another aspect of the present disclosure, an electronic device is provided, the electronic device comprising: a communication interface including at least one communication circuit; a display; a memory configured to store at least one instruction; and a processor. The processor executes the at least one instruction to perform the following operations: obtaining sensor data indicating the concentration of an analyte in a host; displaying a UI element indicating the value of the sensor data on the display; judging whether to perform post-processing on the sensor data based on the sensor data; performing post-processing on the sensor data based on the result of the judgment; and updating the display of the UI element based on the post-processed sensor data.

[0021] The processor may determine to perform post-processing when a standard deviation of a predetermined number of data points included in the sensor data is greater than a preset threshold, and may determine not to perform post-processing when the standard deviation of the data points is the preset threshold or less than the preset threshold.

[0022] The sensor data may include a first data point corresponding to a first time point and a second data point corresponding to a second time point adjacent to the first time point, and the processor may determine to perform post-processing when the time interval between the first time point and the second time point is shorter than a preset threshold time, and may determine not to perform post-processing when the time interval is the preset threshold time or longer than the preset threshold time.

[0023] The processor may apply a predefined filter to a first data set consisting of a plurality of data points corresponding to a first time period to correct values ​​of data points corresponding to a predetermined index among the plurality of data points, generate a second data set corresponding to a second time period after the first time period using the data points whose values ​​have been corrected, and apply the predefined filter to the second data set to correct values ​​of data points corresponding to the predetermined index among the plurality of data points included in the second data set.

[0024] The processor may normalize the first data set, perform a convolution operation on the first data set using a predefined filter, remove distortion of the first data set, and denormalize the first data set.

[0025] The processor may normalize the first data set by subtracting a value of a data point corresponding to a last index among the plurality of data points included in the first data set from a value of each data point included in the first data set.

[0026] The processor may update the display of the UI element such that the UI element indicates the value of the post-processed sensor data.

[0027] The processor may calculate a post-processing count for each of a plurality of data points included in the sensor data, and display a plurality of UI elements corresponding to the plurality of data points to be visually distinguishable based on the post-processing count.

[0028] The processor may display the plurality of UI elements based on whether the post-processing count satisfies a predetermined count.

[0029] Solutions of the objectives of the present disclosure are not limited to the above solutions, and other solutions not described above will be clearly understood by those skilled in the art from the present specification and the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The above and other objects, features and advantages of the present disclosure will become more apparent to those skilled in the art by describing in detail exemplary embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0031] Figure 1 is a schematic diagram of a continuous glucose monitoring system (CGMS) according to an exemplary embodiment of the present disclosure.

[0032] Figure 2 is a flowchart illustrating a method of controlling an electronic device according to an exemplary embodiment of the present disclosure.

[0033] Figure 3 Information related to sensor data according to an exemplary embodiment of the present disclosure is shown.

[0034] Figure 4 Show Display Figure 3 An exemplary embodiment of a method of displaying sensor data (30) is shown.

[0035] Figure 5 A data set according to an exemplary embodiment of the present disclosure is shown.

[0036] Figure 6 is a diagram illustrating a post-processing method according to an exemplary embodiment of the present disclosure.

[0037] Figure 7 is a diagram illustrating a post-processing method according to another exemplary embodiment of the present disclosure.

[0038] Figure 8 is a flowchart illustrating a filter application method according to an exemplary embodiment.

[0039] Fig. 9 Show Display Figure 6 An exemplary embodiment of a method of a first data set (61) is shown.

[0040] Fig.10 is a block diagram of an analyte monitoring system 1000 according to an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0041] Terms used in the specification will first be briefly explained, and then the present disclosure will be explained in detail.

[0042] As the terms used herein, general terms that are currently used as widely as possible will be selected in consideration of the functions in the present disclosure, but the general terms may vary according to the intentions of ordinary technicians in the field, precedents, the emergence of new technologies, and similar conditions. Specifically, the applicant may select terms arbitrarily. In such cases, the meaning of the terms will be explained in detail through the relevant descriptions of the present disclosure. Therefore, the terms used herein should be defined based on their meanings and the overall content of the present disclosure rather than based on their names.

[0043] The present disclosure may be modified in various ways and have various embodiments, and specific embodiments will be shown in the drawings and described in detail. However, this is not intended to limit the present disclosure to specific embodiments, and it should be understood that the present disclosure includes all modifications, equivalent forms and alternative forms within the spirit and technical scope of the disclosure. When describing the embodiments, when it is determined that the detailed description of the relevant known technology obscures the subject matter of the present disclosure, it will not be repeated.

[0044] Terms such as "first", "second" and the like may be used to describe various components, but the components are not limited by the terms. The terms are only used to distinguish one component from other components.

[0045] Unless the context clearly indicates otherwise, singular expressions also include plural expressions. In this application, the terms "including", "having" and similar terms indicate the presence of features, integers, steps, operations, components, parts or combinations thereof described in this specification, and do not exclude the presence or addition of one or more other features, integers, steps, operations, components, parts or combinations thereof.

[0046] Hereinafter, the embodiments of the present disclosure will be described in detail with reference to the accompanying drawings so that a person skilled in the art of the present disclosure can easily implement the present disclosure. However, the present disclosure can be implemented in many different forms and is not limited to the embodiments described herein. In order to clearly illustrate the present disclosure, parts that are not related to the present description will be omitted in the accompanying drawings, and similar reference numbers refer to similar parts throughout the present specification.

[0047] Figure 1is a schematic diagram of a continuous glucose monitoring system (CGMS) according to an exemplary embodiment of the present disclosure.

[0048] refer to Figure 1 , the analyte monitoring system 1000 may include an analyte monitoring device 100 and an electronic device 200. For example, the analyte monitoring system 1000 may be a CGMS, and the analyte monitoring device 100 may be a continuous glucose monitor (CGM). The continuous glucose monitor (CGM) may include a continuous analyte sensor. The continuous analyte sensor may be a continuous glucose sensor. The electronic device 200 may be a user terminal device. As an example, the electronic device 200 may be a smart phone, a tablet personal computer (personal computer, PC), a smart watch, a personal digital assistant (personal digital assistant, PDA) or a dedicated receiver (e.g., a receiver).

[0049] Analyte monitoring device 100 may obtain information related to the concentration of analytes included in the body fluid of user 1. The analytes may include glucose and ketones. The information related to the analyte concentration may include the magnitude of the signal measured by analyte monitoring device 100. The information related to the analyte concentration may include a value indicating the analyte concentration.

[0050] Analyte monitoring device 100 may transmit information related to analyte concentration to electronic device 200 according to a predetermined schedule. For example, analyte monitoring device 100 may transmit information related to analyte concentration to electronic device 200 every five minutes.

[0051] Analyte monitoring device 100 may be attached to the body of user 1. At least a portion of analyte monitoring device 100 may be inserted into the skin of user 1 to be placed in the body of user 1.

[0052] The electronic device 200 may provide information related to the analyte concentration to the user. The electronic device 200 may display information related to the analyte concentration. The information related to the analyte concentration provided to the user may include data whose signals have been processed by the electronic device 200. For example, the electronic device 200 may generate a blood glucose concentration value by applying a predefined algorithm to a signal detected by the analyte monitoring device 100.

[0053] Figure 2 is a flowchart illustrating a method of controlling an electronic device according to an exemplary embodiment of the present disclosure.

[0054] refer to Figure 2, the electronic device 200 may acquire sensor data indicating the concentration of an analyte in the host (S1100). The sensor data may include a plurality of data points. The plurality of data points may be time-series data with a specific time interval. The value of each data point may be a concentration value of the analyte. Meanwhile, in the present disclosure, acquiring, displaying or processing sensor data or data points means acquiring, displaying or processing the value of sensor data or data points.

[0055] The electronic device 200 may acquire sensor data in various ways. According to an embodiment, the electronic device 200 may receive a sensor signal from the analyte monitoring device 100. Then, the electronic device 200 may derive the sensor data by processing the sensor signal. Processing the sensor signal may include noise filtering and calibration of the sensor signal. For example, calibration of the sensor signal may be an operation of deriving an analyte concentration value from the sensor signal using the sensitivity and / or offset value of the sensor.

[0056] According to another embodiment, the electronic device 200 may receive sensor data from the analyte monitoring device 100. In other words, the electronic device 200 may receive the concentration value of the analyte from the analyte monitoring device 100. Here, the analyte monitoring device 100 may derive the sensor data by processing the sensor signal. The analyte monitoring device 100 may transmit the sensor signal and the sensor data to the electronic device 200.

[0057] The electronic device 200 may display a user interface (UI) element indicating the value of the sensor data (S1200). For example, the electronic device 200 may display the value of each of the plurality of data points included in the sensor data in a two-dimensional (2D) chart. The x-axis of the chart indicates time, and the y-axis of the chart indicates the blood glucose value. The electronic device 200 may display a visual indicator at a position corresponding to the value and time point of each data point. The visual indicator may be a point. The visual indicator may have any of various shapes (e.g., a circle, a polygon, an arrow, and the like).

[0058] The electronic device 200 may display the latest data point different from the earlier data points. For example, the electronic device 200 may display only the earlier data points in the chart, but display not only the latest data point in the chart but also the value of the latest data point as a number. The number indicating the value of the latest data point may be displayed in a region different from the region where the chart is displayed.

[0059] The electronic device 200 may determine whether to perform post-processing on the sensor data (S1300). Post-processing of the sensor data may be performed on a data set consisting of a predetermined number (e.g., ten) of data points. The electronic device 200 may determine whether to perform post-processing on the data set based on whether there is an error in the data set. When there is an error in the data set, the electronic device 200 may determine not to perform post-processing on the data set. When there is no error in the data set, the electronic device 200 may determine to perform post-processing on the data set.

[0060] According to an embodiment, the electronic device 200 may determine whether there is an error in the data set based on the standard deviation of the data points included in the data set. Specifically, when the standard deviation is greater than a preset threshold, the electronic device 200 may determine that there is no error in the data set. Conversely, when the standard deviation is less than or equal to the preset threshold, the electronic device 200 may determine that there is an error in the data set.

[0061] In other words, if the variability of the sensor data is below a preset threshold, post-processing can be omitted. By omitting post-processing, unnecessary computational tasks can be avoided, thereby saving computational resources. In addition, since resources can be focused only on the data that needs to be processed, resource allocation can be performed more efficiently.

[0062] According to another embodiment, the electronic device 200 may determine whether there is an error in the data set based on whether there are missing data points. When the time interval between the data points constituting the data set is a threshold time or longer than the threshold time due to the missing data points, the value of the data set may be distorted during post-processing. If smoothing is applied to the data set when the time interval is irregular or greater than the threshold time, the calibrated value may be significantly different from the actual value. To prevent this, if the time interval exceeds the threshold, the electronic device 200 may determine that there is an error in the data set. Although data point loss is given as an example, the same process is applied when the time interval between data points exceeds the threshold due to communication failure or other problems.

[0063] Specifically, the electronic device 200 may determine whether there is an error (i.e., a missing data point) in the data set based on the time interval between the data points included in the data set. For example, the data set may include a first data point corresponding to a first time point and a second data point corresponding to a second time point adjacent to the first time point. The electronic device 200 may determine whether the time interval between the first time point and the second time point is shorter than a preset threshold time. When the time interval is shorter than the preset threshold time, the electronic device 200 may determine that there is no error in the data set. When the time interval is the preset threshold time or longer than the preset threshold time, the electronic device 200 may determine that there is an error in the data set. In other words, the electronic device 200 may determine that there is a missing data point between the first time point and the second time point. When it is determined that post-processing is performed on the sensor data (Yes in S1300), the electronic device 200 may perform post-processing on the sensor data (S1400). Post-processing according to the present disclosure may include an operation of adjusting the sensor data value so that the sensor data value is visually easier for the user to interpret. For example, post-processing of the sensor data may include an operation of applying a predefined filter to the sensor data to change the value of the sensor data. Post-processing of the sensor data may be performed in units of data sets consisting of a predetermined number of data points. In other words, a predefined filter may be applied in units of data sets. A data set may consist of the latest data point and a predetermined number (eg, nine) of earlier data points.

[0064] The predefined filter may include a smoothing filter. The smoothing filter may include a Savitzky-Golay filter.

[0065] The operation of applying the predefined filter to the data set may include an operation of normalizing the data set, an operation of performing a convolution operation on the data set using the predefined filter, an operation of removing distortion of the data set, and an operation of denormalizing the data set. Figure 7 The application of filters is elaborated in detail.

[0066] When acquiring a new data point, the electronic device 200 may generate a new data set and perform post-processing on the new data set. The time interval corresponding to the new data set may overlap with the time interval corresponding to the previously generated data set. Therefore, the new data set may include some data points included in the previously generated data set. Therefore, post-processing may be performed several times on one data point.

[0067] The values ​​of sensor data according to the present disclosure may be calibrated values ​​of raw data measured by the sensor. In this process, the units of the sensor data may be changed from current values ​​to concentration values. Post-processing of the sensor data may involve further calibration of the initially calibrated concentration values, and this may be a retrospective calibration.

[0068] When it is determined that the sensor data is not to be post-processed (No in S1300), the electronic device 200 may not post-process the sensor data. Therefore, the UI element displayed by the electronic device 200 may not be updated. For example, the representation of the UI element indicating the glucose concentration value may remain unchanged.

[0069] The electronic device 200 may update the display of the UI element based on the post-processed sensor data (S1500). The electronic device 200 may update the display of the UI element so that the UI element indicates the value of the post-processed sensor data. For example, the electronic device 200 may change the position of a visual indicator displayed at a first position to show a first blood glucose value before post-processing to a second position to show a second blood glucose value that has been updated according to the post-processing. Therefore, the user can identify the second blood glucose value as his or her own blood glucose value.

[0070] The electronic device 200 may calculate a post-processing count for each of the plurality of data points included in the sensor data. The electronic device 200 may display the plurality of UI elements in different ways according to the post-processing count of each data point. The electronic device 200 may display a UI element indicating the value of the data point in different ways according to whether the post-processing count of the data point satisfies a predetermined count. For example, the post-processing count of the first data point may satisfy the predetermined count, while the post-processing count of the second data point may not satisfy the predetermined count.

[0071] According to an embodiment, the electronic device 200 may display the first UI element corresponding to the first data point more clearly or darker than the second UI element corresponding to the second data point. According to another embodiment, the electronic device 200 may display the first UI element and the second UI element in different colors or patterns. According to yet another embodiment, the second UI element may include a message indicating that the blood glucose value shown by the second UI element may be inaccurate. According to yet another embodiment, the electronic device 200 may display the UI element corresponding to the data point based on the difference between the post-processing count of the data point and the predetermined count. For example, as the difference increases, the UI element may be displayed brighter.

[0072] Figure 3 Information related to sensor data according to an exemplary embodiment of the present disclosure is shown.

[0073] refer to Figure 3, the sensor data may include data points x1, x2, x3, x4, x5, and x6. The information 30 related to the sensor data may include information related to the data points x1, x2, x3, x4, x5, and x6. The information related to the data points x1, x2, x3, x4, x5, and x6 may include values ​​of the data points x1, x2, x3, x4, x5, and x6. The value of each data point may be the blood glucose value of the user.

[0074] The information related to the data points x1, x2, x3, x4, x5, and x6 may include a sequence consisting of the data points x1, x2, x3, x4, x5, and x6. The sequence consisting of the data points x1, x2, x3, x4, x5, and x6 may represent an index of each data point. The index of the data point may represent an index at which the data point has been measured or derived. The order of the data points may be given in ascending order over time. According to another embodiment, the order of the data points may be given in descending order over time.

[0075] Information related to data points x1, x2, x3, x4, x5, and x6 may include time points t1, t2, t3, t4, t5, and t6 corresponding to data points x1, x2, x3, x4, x5, and x6, respectively. Here, the time point corresponding to the data point may be a time point at which the value of the data point has been derived. The value of the data point may not be derived at a specific time interval. For example, due to a communication failure between the electronic device 200 and the analyte monitoring device 100, the communication of the sensor signal or the data point may be delayed. Alternatively, due to a malfunction of the electronic device 200 or the analyte monitoring device 100, the time point at which the value of the data point has actually been derived may not have a specific time interval.

[0076] Figure 4 Show Display Figure 3 An exemplary embodiment of a method of collecting sensor data 30 is shown.

[0077] refer to Figure 4 , the electronic device 200 may display UI elements 41, 42, 43, 44, 45, and 46 corresponding to the data points x1, x2, x3, x4, x5, and x6 included in the sensor data 30. The first UI element 41 may correspond to the first data point x1, the second UI element 42 may correspond to the second data point x2, the third UI element 43 may correspond to the third data point x3, the fourth UI element 44 may correspond to the fourth data point x4, the fifth UI element 45 may correspond to the fifth data point x5, and the sixth UI element 46 may correspond to the sixth data point x6.

[0078] The electronic device 200 may represent the sensor data 30 in various types of graphs. The x-axis of the graph may indicate time, and the y-axis of the graph may indicate blood glucose values. According to an embodiment, the electronic device 200 may display the sensor data 30 as follows: Figure 4 According to other embodiments, the electronic device 200 may also display the sensor data 30 as a line graph, a dashed line graph, or a bar graph. The line graph may be a straight line or a curve.

[0079] Figure 5 A data set according to an exemplary embodiment of the present disclosure is shown.

[0080] refer to Figure 5 , there is a data set 51 consisting of six data points x1, x2, x3, x4, x5, and x6. The electronic device 200 may generate a data set using a predetermined number of data points. Figure 5 An example in which the predetermined number is six is ​​shown, but the present disclosure is not limited thereto.

[0081] The electronic device 200 may generate a new data set each time a new data point is acquired. For example, the data set 51 may be generated when the latest data point x6 is acquired.

[0082] When the electronic device 200 does not have a predetermined number of data points, the electronic device 200 may not generate a data set until the predetermined number of data points exist. For example, in the early stages of the sensor utilization cycle, the number of data points that the electronic device 200 has may be less than the predetermined number. In this case, the electronic device 200 may neither generate a data set nor perform post-processing on the data set. Therefore, the time point when post-processing operations are performed on sensor data according to the present disclosure may be after acquiring at least the predetermined number of data points.

[0083] Meanwhile, data set 51 may include data point x6 having a value of 0. For example, when application of the predefined algorithm to the sensor signal is not completed, the value of the data point may be 0. Alternatively, the value of data point x6 may be 0 due to an internal error of electronic device 200 or any other error.

[0084] In the case where post-processing is performed on a data set 51 including a data point x6 having a value of 0, the value of the data set 51 may be distorted during the post-processing process. Therefore, it may be necessary to correct the value of 0 of the data point x6 to a value other than 0, or to exclude the data point x6 having a value of 0 from the data set 51, and use subsequent data points having values ​​other than 0 to generate the data set 51. However, in the latter case, it is necessary to wait until a new data point having a value other than 0 is acquired, which delays the generation and post-processing of the data set 51. Therefore, the user cannot view the value of the post-processed sensor data in real time, which may become a problem. To prevent this, the electronic device 200 can derive a new data set 52 by correcting the value of the data point x6.

[0085] The electronic device 200 may correct the value of the data point x6 based on the strength of the sensor signal corresponding to the data point x6. Specifically, the electronic device 200 may derive a new value of the data point x6 by applying a predefined algorithm to the sensor signal corresponding to the data point x6. For example, the operation of applying the predefined algorithm may include the operation of deriving a blood glucose value from the sensor signal using the sensor sensitivity and / or offset value.

[0086] With this operation in which the electronic device 200 corrects the data set 51, it is possible to prevent the values ​​of the data set 51 from being distorted during post-processing. In addition, there is no need to wait for new subsequent data and the user can view the values ​​of the post-processed data in real time.

[0087] When the data set 52 is generated, the electronic device 200 may determine whether to perform post-processing on the data set 52. As described above, the electronic device 200 may determine whether to perform post-processing based on whether there is an error in the data set 52.

[0088] According to an embodiment, the electronic device 200 may determine whether there is an error in the data set 52 based on the standard deviation of the data points x1, x2, x3, x4, x5, and x6. Specifically, when the standard deviation of the data points x1, x2, x3, x4, x5, and x6 is greater than a preset threshold, the electronic device 200 may determine that there is no error in the data set 52. On the contrary, when the standard deviation of the data points x1, x2, x3, x4, x5, and x6 is less than or equal to the preset threshold, the electronic device 200 may determine that there is an error in the data set 52.

[0089] According to another embodiment, the electronic device 200 may determine whether there is an error in the data set 52 based on the time intervals between adjacent data points among the data points x1, x2, x3, x4, x5, and x6. Figure 3, the electronic device 200 may calculate the interval between the first time point t1 and the second time point t2, the interval between the second time point t2 and the third time point t3, the interval between the third time point t3 and the fourth time point t4, the interval between the fourth time point t4 and the fifth time point t5, and the interval between the fifth time point t5 and the sixth time point t6. When all the calculated intervals are shorter than the preset threshold time, the electronic device 200 may determine that there is no error in the data set 52. On the contrary, when any of the calculated intervals is longer than or equal to the preset threshold time, the electronic device 200 may determine that there is an error in the data set 52.

[0090] Figure 6 is a diagram illustrating a post-processing method according to an exemplary embodiment of the present disclosure.

[0091] refer to Figure 6 , the electronic device 200 may perform post-processing on the first data set 61. For example, the electronic device 200 may apply a predefined filter to the first data set 61. Therefore, the values ​​of some data points corresponding to the predetermined indexes among the data points x1, x2, x3, x4, x5, and x6 may be updated.

[0092] Even when the filter is applied to all data points x1, x2, x3, x4, x5, and x6, only the values ​​of the data points x3, x4, and x5 corresponding to the predetermined indexes may be updated, and the values ​​of the other data points x1, x2, and x6 may not be updated. The values ​​of the data points x1 and x2 corresponding to the first few indexes may be distorted due to the filter application. Therefore, the values ​​of the data points x1 and x2 may not be updated with the values ​​obtained after the filter is applied. Since the value of the data point x6 corresponding to the last index is the most recent blood glucose value that the user is most interested in, when the value changes in the post-processing, it may confuse the user. Therefore, the value of the data point x6 may not be updated with the value obtained after the filter is applied.

[0093] After post-processing the data set 61, the electronic device 200 may obtain a post-processing count for each of the data points x1, x2, x3, x4, x5, and x6. Specifically, the post-processing counts of the data points x3, x4, and x5 corresponding to the predetermined index are increased, while the post-processing counts of the other data points x1, x2, and x6 are not increased. Therefore, the post-processing count N of the data set 61 may be different from the post-processing count of each individual data point. In addition, some data points may have different post-processing counts.

[0094] When acquiring the new data point x7, the electronic device 200 may acquire the second data set 62. The second data set 62 may be composed of a portion (x2, x3, x4, x5, and x6) of the post-processed first data set 61 and the new data point x7. In other words, the time periods of adjacent data sets may overlap, and the adjacent data sets may include common data points.

[0095] The electronic device 200 may perform post-processing on the second data set 62. Specifically, the electronic device 200 may apply a predefined filter to the second data set 62. Therefore, the values ​​of the data points x4, x5, and x6 corresponding to the predetermined index may be updated among the data points x2, x3, x4, x5, x6, and x7. The post-processing counts of the data points x4, x5, and x6 corresponding to the predetermined index may be increased by 1.

[0096] When acquiring the new data point x8, the electronic device 200 may acquire the third data set 63. The third data set 63 may be composed of a portion (x3, x4, x5, x6, and x7) of the post-processed second data set 62 and the new data point x8.

[0097] The electronic device 200 may perform post-processing on the third data set 63. Specifically, the electronic device 200 may apply a predefined filter to the third data set 63. Therefore, the values ​​of the data points x5, x6, and x7 corresponding to the predetermined index may be updated among the data points x3, x4, x5, x6, x7, and x8. The post-processing counts of the data points x5, x6, and x7 corresponding to the predetermined index may be increased by 1.

[0098] As described above, each time a new data point is acquired, the electronic device 200 can generate a data set, determine whether to perform post-processing on the data set, and then perform post-processing when it is determined to perform post-processing. When post-processing is repeated on several data sets, the electronic device 200 can store the post-processing counts of the respective data points.

[0099] Figure 7 is a diagram illustrating a post-processing method according to another exemplary embodiment of the present disclosure.

[0100] refer to Figure 7 , the electronic device 200 may generate a first data set 71. The electronic device may determine whether there is an error in the first data set 71 to determine whether to perform post-processing on the first data set 71. For example, the time interval between the first time point corresponding to the first data point x1 and the second time point corresponding to the second data point x2 may be longer than a preset threshold time (e.g., 10 minutes). In this case, the electronic device 200 may determine that there is an error in the first data set 71 and may not perform post-processing on the first data set 71.

[0101] There may be no error in the second data set 72 and the third data set 73. Therefore, the electronic device 200 may be connected with Figure 6 The second data set 72 and the third data set 73 are post-processed in the same manner as described in the above and the post-processing count of each data point is obtained. Figure 6 and Figure 7 By comparison, each of the data points x3, x4, and x5 differ in the post-processing count. This is because the post-processing was not applied to the first data set 71 due to the error. In this way, the post-processing count of each data point can vary depending on whether an error exists.

[0102] Figure 8 is a flowchart illustrating a filter application method according to an exemplary embodiment.

[0103] refer to Figure 8 , the electronic device 200 may normalize the data set (S1410). The electronic device 200 may normalize the value of each of the plurality of data points included in the data set.

[0104] According to an embodiment, the electronic device 200 may subtract the value of the data point corresponding to the last index among the plurality of data points from the value of each data point, as shown in Equation 1. Then, the electronic device 200 may divide the value obtained by subtracting the value of the data point corresponding to the last index from the value of each data point by the standard deviation of the plurality of data points.

[0105] [Equation 1]

[0106]

[0107] In Equation 1, x is the value of the data point before normalization, x(end) is the value of the data point corresponding to the last index of the data set X, std(X) is the standard deviation of the values ​​of the data points included in the data set X, and x norm is the normalized value of the data point. Equation 1 may be referred to as “zero-end normalization” or “zero-end norm”.

[0108] When the last data point of each data set is used for normalization, the difference between the post-processed data points and the latest data points that have not been post-processed can be minimized. Therefore, the data points around the latest data point can be smoothed.

[0109] According to another embodiment, the electronic device 200 may subtract the average value of the plurality of data points from the value of each data point. Then, the electronic device 200 may divide the value obtained by subtracting the average value from the value of each data point by the standard deviation of the plurality of data points.

[0110] The electronic device 200 may perform a convolution operation on the data set (S1420). The electronic device 200 may perform the convolution operation using a predefined smoothing filter.

[0111] The electronic device 200 may remove distortion of the data set (S1430). The electronic device 200 may remove a portion of the result of the convolution operation corresponding to the first few indexes. The reason is that the portion corresponding to the first few indexes may include distortion caused by filter characteristics. This is similar to Figure 6 Related, in Figure 6 , the values ​​of the data points x2 and x3 included in the second data set 62 are not updated. The reason is that the values ​​of the data points x2 and x3 corresponding to the first few indexes may be distorted during the process of applying the filter to the second data set 62. The electronic device 200 does not update the values ​​of the data points x2 and x3 to prevent the data points x2 and x3 from being distorted due to post-processing.

[0112] At the same time, the range of the removed indexes can be determined based on the length of the filter coefficients. For example, the number of removed indexes can be half the length of the filter coefficients. When the length of the filter coefficients is an odd number, the number of removed indexes can be half the length of the filter coefficients, and the decimal places are discarded.

[0113] The electronic device 200 may denormalize the data set (S1440). The electronic device 200 may denormalize the values ​​of the plurality of data points included in the data set. The electronic device 200 may update the value of the data point having a predetermined index based on the denormalized value.

[0114] Fig. 9 Show Display Figure 6 An exemplary embodiment of a method of a first data set 61 is shown.

[0115] refer to Fig. 9 , the electronic device 200 may display UI elements 91, 92, 93, 94, 95, and 96 corresponding to the data points x1, x2, x3, x4, x5, and x6 included in the first data set 61. The UI element 96 corresponding to the latest data point x6 may be displayed to be visually distinguishable from the other UI elements 91, 92, 93, 94, and 95.

[0116] As described above, the electronic device 200 performs post-processing on the first data set 61 and can update the values ​​of the data points x3, x4 and x5 accordingly. The electronic device 200 can update the display of the UI elements 93, 94 and 95 based on the updated values. As shown in the figure, the electronic device 200 can change the positions of the UI elements 93, 94 and 95 to show the updated values.

[0117] Each time a new data point is acquired, the electronic device 200 may generate a new data set and perform post-processing on the new data set. The generated new data set may include some of the data points included in the data set corresponding to the previous time period. Therefore, post-processing may be performed several times on each data point, and the display of the UI element corresponding to each data point may be updated several times. When there is no error in the data set, the post-processing count of one data point may be the same as the number of data points whose values ​​have been updated by a single process.

[0118] Although not shown in the drawings, the electronic device 200 may update the display of the UI element corresponding to each data point based on the post-processing count of the data point. For example, the electronic device 200 may display the data points that meet the predetermined post-processing count and the data points that do not meet the predetermined post-processing count in different ways. Specifically, the UI elements corresponding to the data points may be different in shape or color.

[0119] Fig.10 is a block diagram of an analyte monitoring system 1000 according to an exemplary embodiment of the present disclosure.

[0120] refer to Fig.10 , the analyte monitoring system 1000 may include an analyte monitoring device 100 and an electronic device 200 .

[0121] Analyte monitoring device 100 may include analyte sensor 110 and sensor electronics unit 120 .

[0122] The analyte sensor 110 may be an element for sensing an analyte signal (or sensor signal). The analyte sensor 110 may include a sensor probe at least a portion of which is inserted into the body. In the sensor probe, a sensing area that reacts to glucose in the body may be formed to measure the glucose concentration in the host. The analyte sensor 110 may be a continuous glucose sensor.

[0123] The sensor electronic unit 120 may include at least one communication interface. The sensor electronic unit 120 may communicate with the electronic device 200 via the communication interface. For example, the communication interface may include a Bluetooth module, a Bluetooth Low Energy (BLE) module, a radio frequency (RF) module, and a near field communication (NFC) module.

[0124] The sensor electronics unit 120 may transmit the sensor signal acquired by the analyte sensor 110 to the electronic device 200. In addition, the sensor electronics unit 120 may transmit the value of the data point derived from the sensor signal to the electronic device 200. The sensor electronics unit 120 may transmit the sensor signal or the value of the data point to the electronic device 200 at a predetermined interval (e.g., five minutes).

[0125] The sensor electronics unit 120 may include a memory storing an operating system (OS) for controlling the overall operation of the components of the analyte monitoring device 100 and instructions or data related to the components of the analyte monitoring device 100. In addition, the sensor electronics unit 120 may include a processor electrically connected to the memory to control the overall functions and operations of the analyte monitoring device 100.

[0126] The electronic device 200 may include a display 210, a communication interface 220, a memory 230, and a processor 240. The electronic device 200 may be a user terminal device such as a smart phone.

[0127] The display 210 may output sensor data. For example, the display 210 may output the user's blood sugar value.

[0128] The communication interface 220 may include at least one communication circuit. The communication interface 220 may receive a sensor signal from the analyte monitoring device 100. The communication interface 220 may receive a value of a data point from the analyte monitoring device 100.

[0129] The memory 230 may store an OS for controlling overall operations of components of the electronic device 200 and instructions or data related to components of the electronic device 200. The memory 230 may be implemented as a nonvolatile memory (e.g., a hard disk, a solid state drive (SSD) or a flash memory), a volatile memory, or the like.

[0130] The processor 240 may be electrically connected to the memory 230 to control the overall functions and operations of the electronic device 200. The processor 240 may control the electronic device 200 by executing instructions stored in the memory 230.

[0131] Processor 240 may acquire sensor data indicative of an analyte concentration in the host. According to an embodiment, processor 240 may derive sensor data from a sensor signal received from analyte monitoring device 100 via communication interface 220. According to another embodiment, processor 240 may receive sensor data indicative of an analyte concentration from analyte monitoring device 100 via communication interface 220.

[0132] The processor 240 may display a UI element indicating the value of the sensor data on the display 210. The processor 240 may control the display so that the UI element is displayed.

[0133] The processor 240 may determine whether to perform post-processing on the sensor data based on the sensor data. According to an embodiment, when the standard deviation of a predetermined number of data points included in the sensor data is greater than a preset threshold, the processor 240 may determine to perform post-processing. Conversely, when the standard deviation is less than or equal to the preset threshold, the processor 240 may determine not to perform post-processing.

[0134] According to another embodiment, the processor 240 may determine whether to perform post-processing based on the time interval between adjacent data points. For example, the processor 240 may obtain a first data point corresponding to a first time point and a second data point corresponding to a second time point adjacent to the first data point. When the time interval between the first time point and the second time point is shorter than a preset threshold time, the processor 240 may determine to perform post-processing. On the contrary, when the time interval is the preset threshold time or longer than the preset threshold time, the processor 240 may determine not to perform post-processing.

[0135] The processor 240 may perform post-processing on the sensor data. The processor 240 may correct the value of the data point corresponding to the predetermined index among the plurality of data points by applying a predefined filter to a first data set consisting of the plurality of data points corresponding to the first time period. The processor 240 may generate a second data set corresponding to a second time period after the first time period using the data points whose values ​​have been corrected. The processor 240 may correct the value of the data point corresponding to the predetermined index among the plurality of data points included in the second data set by applying the predefined filter to the second data set. Here, the first time period may overlap at least a portion of the second time period.

[0136] The processor 240 may apply a predefined filter to the data set. The processor 240 may normalize the first data set. For example, the processor 240 may normalize the first data set by subtracting the value of the data point corresponding to the last index among the plurality of data points included in the first data set from the value of each data point included in the first data set.

[0137] The processor 240 may perform a convolution operation on the first data set using a predefined filter. The processor 240 may remove distortion of the first data set. The processor 240 may denormalize the first data set.

[0138] Processor 240 may update the display of the UI element based on the post-processed sensor data. For example, processor 240 may change the display of the UI element so that the UI element shows the value of the post-processed sensor data.

[0139] The processor 240 may obtain a post-processing count for each of the plurality of data points included in the sensor data. The processor 240 may display a plurality of UI elements corresponding to the plurality of data points so as to be visually distinguishable based on the post-processing count. For example, the processor 240 may display the plurality of UI elements based on whether the post-processing count satisfies a predetermined count.

[0140] According to the embodiments of the present disclosure, distortion of the blood glucose concentration value provided to the user can be minimized.

[0141] According to the embodiments of the present disclosure, the user can conveniently check the blood sugar concentration, thereby improving the convenience and satisfaction of the user.

[0142] Other effects that can be achieved or expected from the embodiments of the present disclosure have been disclosed explicitly or implicitly in the detailed description of the embodiments of the present disclosure. For example, various effects expected according to the embodiments of the present disclosure have been disclosed in the above description.

[0143] The above description discloses various embodiments of the present invention with reference to the accompanying drawings. Other aspects, advantages and features of the present disclosure will become apparent to those skilled in the art by reading the above description.

[0144] The various embodiments described above may be implemented in a recording medium that can be read by a computer or a similar device using software, hardware, or a combination thereof. In some cases, the embodiments described herein may be implemented as a processor. When the embodiments are implemented as software, the embodiments of the procedures, functions, and similar conditions described herein may be implemented as separate software modules. Each of the software modules may implement one or more functions and operations described herein.

[0145] Computer instructions for implementing the processing operations according to the above-mentioned various embodiments of the present disclosure may be stored in a non-transitory computer-readable recording medium. When executed by a processor, these computer instructions stored in the non-transitory computer-readable recording medium may cause a specific device to implement the processing operations according to the above-mentioned various embodiments.

[0146] A non-transitory computer-readable medium is not a medium that stores data for a short period of time (e.g., a register, a cache, a memory, or a similar device), but a machine-readable medium that stores data semi-permanently. Examples of non-transitory computer-readable media may include compact discs (CDs), digital versatile discs (DVDs), hard disks, Blu-ray discs, Universal Serial Bus (USB) memories, memory cards, read-only memories (ROMs), and similar devices.

[0147] The machine-readable medium may be provided in the form of a non-transitory storage medium. Here, a "non-transitory storage medium" is a tangible device that does not include any signal (e.g., electromagnetic waves), and the term is used regardless of whether data is stored in the storage medium semi-permanently or temporarily. For example, a "non-transitory storage medium" may include a buffer that temporarily stores data.

[0148] The methods according to various embodiments disclosed in this document may be included in and provided in a computer program product. The computer program product may be traded as a product between a seller and a buyer. The computer program product may be distributed in the form of a machine-readable storage medium (e.g., a compact disc read only memory (CD-ROM)) or through an application store (e.g., a Play Store). TM (PlayStore TM )) online distribution (e.g., downloading or uploading), or directly between two user devices (e.g., smartphones). In the case of online distribution, at least a part of the computer program product (e.g., a downloadable application (app)) may be temporarily generated or at least temporarily stored in a machine-readable storage medium (e.g., a manufacturer's server, an application store's server, or a memory of a relay server).

[0149] Although the exemplary embodiments of the present disclosure have been shown and described above, the present disclosure is not limited thereto. A person skilled in the art may make various modified embodiments without departing from the gist of the present disclosure as described in the claims, and it should be understood that these modified embodiments fall within the technical spirit or scope of the present disclosure.

Claims

1. A method for controlling an electronic device, the method comprising: acquiring sensor data indicative of a concentration of an analyte in a host; displaying a user interface element indicative of a value of the sensor data; determining whether to perform post-processing on the sensor data based on the sensor data; Post-processing the sensor data based on the result of the determination; as well as A display of the user interface element is updated based on the post-processed sensor data.

2. The method according to claim 1, wherein determining whether to perform the post-processing comprises: When the standard deviation of a predetermined number of data points included in the sensor data is greater than a preset threshold, determining to perform the post-processing; as well as When the standard deviation of the data point is the preset threshold or less than the preset threshold, it is determined not to perform the post-processing.

3. The method according to claim 1, wherein the sensor data comprises a first data point corresponding to a first time point and a second data point corresponding to a second time point adjacent to the first time point, and Determining whether to perform the post-processing includes: When the time interval between the first time point and the second time point is shorter than a preset threshold time, determining to perform the post-processing; as well as When the time interval is the preset threshold time or longer than the preset threshold time, it is determined not to perform the post-processing.

4. The method of claim 1, wherein performing the post-processing comprises: Applying a predefined filter to a first data set consisting of a plurality of data points corresponding to a first time period to correct a value of a data point corresponding to a predetermined index among the plurality of data points; generating a second data set corresponding to a second time period after the first time period using the data points wherein the values ​​have been corrected; and The predefined filter is applied to the second data set to correct a value of a data point corresponding to the predetermined index among a plurality of data points included in the second data set. The method of claim 4 , wherein the first time period overlaps at least a portion of the second time period.

6. The method of claim 4, wherein applying the predefined filter to the first data set comprises: Normalizing the first data set; performing a convolution operation on the first data set using the predefined filter; removing distortion from the first data set; as well as The first data set is denormalized.

7. The method of claim 6, wherein normalizing the first data set comprises: A value of a data point corresponding to a last index among the plurality of data points included in the first data set is subtracted from a value of each data point included in the first data set.

8. The method of claim 1 , wherein updating the display of the user interface element comprises: The display of the user interface element is updated so that the user interface element indicates a value of the post-processed sensor data.

9. The method of claim 1 , further comprising calculating a post-processing count for each of a plurality of data points included in the sensor data, Wherein updating the display of the user interface element comprises: Based on the post-processing count, a plurality of user interface elements corresponding to the plurality of data points are displayed to be visually distinguishable.

10. The method of claim 9, wherein updating the display of the user interface element comprises: The plurality of user interface elements are displayed based on whether the post-processing count satisfies a predetermined count.

11. An electronic device, comprising: a communication interface comprising at least one communication circuit; monitor; a memory configured to store at least one instruction; as well as processor, The processor executes the at least one instruction to perform the following operations: acquiring sensor data indicative of a concentration of an analyte in a host; displaying, on the display, a user interface element indicating a value of the sensor data; determining whether to perform post-processing on the sensor data based on the sensor data; Post-processing the sensor data based on the result of the determination; as well as A display of the user interface element is updated based on the post-processed sensor data.

12. An electronic device according to claim 11, wherein the processor determines to perform the post-processing when the standard deviation of a predetermined number of data points included in the sensor data is greater than a preset threshold, and determines not to perform the post-processing when the standard deviation of the data points is the preset threshold or less than the preset threshold.

13. The electronic device according to claim 11, wherein the sensor data comprises a first data point corresponding to a first time point and a second data point corresponding to a second time point adjacent to the first time point, and The processor determines to perform the post-processing when the time interval between the first time point and the second time point is shorter than a preset threshold time, and determines not to perform the post-processing when the time interval is the preset threshold time or longer than the preset threshold time.

14. The electronic device of claim 11, wherein the processor applies a predefined filter to a first data set consisting of a plurality of data points corresponding to a first time period to correct a value of a data point corresponding to a predetermined index among the plurality of data points, generating a second data set corresponding to a second time period following the first time period using the data points wherein the values ​​have been corrected, and The predefined filter is applied to the second data set to correct a value of a data point corresponding to the predetermined index among a plurality of data points included in the second data set.

15. The electronic device of claim 14, wherein the first time period overlaps at least a portion of the second time period.

16. The electronic device of claim 14, wherein the processor normalizes the first data set, performs a convolution operation on the first data set using the predefined filter, removes distortion of the first data set, and denormalizes the first data set.

17. An electronic device according to claim 16, wherein the processor normalizes the first data set by subtracting the value of the data point corresponding to the last index among the multiple data points included in the first data set from the value of each data point included in the first data set. 18 . The electronic device of claim 11 , wherein the processor updates the display of the user interface element such that the user interface element indicates a value of the post-processed sensor data.

19. An electronic device according to claim 11, wherein the processor calculates a post-processing count for each of a plurality of data points included in the sensor data, and displays a plurality of user interface elements corresponding to the plurality of data points in a visually distinguishable manner based on the post-processing count.

20. The electronic device of claim 19, wherein the processor displays the plurality of user interface elements based on whether the post-processing count satisfies a predetermined count.

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