Electronic apparatus and method of controlling the same

Abstract

To provide an electronic apparatus and a method of controlling the same.SOLUTION: The method includes acquiring sensor data indicating 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 on the basis of the sensor data, performing post-processing on the sensor data on the basis of a result of the determination, and updating the display of the UI element on the basis of the postprocessed sensor data.SELECTED DRAWING: Figure 1

Description

[Background technology]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to and the benefit of Korean Patent Application No. 10-2023-0154385, filed November 9, 2023, the disclosure of which is incorporated herein by reference in its entirety. 1. Field of the invention The present disclosure relates to electronic devices and methods of controlling the same, and more particularly to electronic devices and methods of controlling the same for processing sensor data indicative of an analyte concentration in a host. 2. Description of Related Technology A continuous glucose monitoring system (CGMS) is a system that uses a sensor in contact with the user's bodily fluids (e.g., interstitial fluid) to obtain and provide a user's blood glucose concentration. A CGMS includes a continuous glucose monitor (CGM) that is worn on the user's body and detects signals from the user's bodily fluids, and a user terminal device that provides the blood glucose concentration to the user.

[0002] The user terminal device provides the user's blood glucose concentration based on the signal detected by the CGM, but the signal detected by the CGM may have noise, which may distort the blood glucose concentration provided to the user and cause confusion to the user.

[0003] Therefore, techniques are needed to process noise in signals measured by CGMs to avoid causing confusion to the user. Summary of the Invention

[0004] The present disclosure is directed to providing an electronic device for minimizing distortion of a user's blood glucose concentration value.

[0005] The present disclosure is also directed to providing an electronic device for displaying blood glucose concentrations in a manner convenient for a user to view.

[0006] The technical objectives of the present disclosure are not limited to those described above, and other technical objectives not described above will be clearly understood by those skilled in the art from the following description.

[0007] According to an aspect of the present disclosure, there is provided a method for controlling an electronic device, comprising: acquiring sensor data indicative of a concentration of an analyte in a host; displaying a user interface (UI) element indicative of a value of the sensor data; determining, based on the sensor data, whether to perform post-processing on the sensor data; performing post-processing on the sensor data based on a result of the determination; and updating a display of the UI element based on the post-processed sensor data.

[0008] The step of determining whether to perform post-processing may include a step of determining to perform the post-processing if a standard deviation of a predetermined number of data points included in the sensor data is greater than a preset threshold, and a step of determining not to perform the post-processing if the standard deviation of the data points is at or less than the preset threshold.

[0009] 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 determining whether to perform the post-processing step may include determining to perform the post-processing if a time interval between the first time point and the second time point is shorter than a predetermined threshold time, and determining not to perform the post-processing if the time interval is the predetermined threshold time or longer.

[0010] The step of performing the post-processing may include 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 following the first time period using the data points whose values ​​have been corrected, and applying the predefined filter to the second data set to correct a value of a data point corresponding to the predetermined index among the plurality of data points included in the second data set.

[0011] The first time period may overlap with at least a portion of the second time period.

[0012] The step of applying the pre-defined filter to the first data set may include normalizing the first data set, performing a convolution operation on the first data set with the pre-defined filter, removing distortions of the first data set, and de-normalizing the first data set.

[0013] The normalization of the first data set may include subtracting a value of a data point corresponding to a last index of the plurality of data points included in the first data set from a value of each data point included in the first data set.

[0014] Updating the display of the UI element may include updating the display of the UI element such that the UI element shows a value of the post-processed sensor data.

[0015] The method may further include calculating a post-processing count for each of a plurality of data points included in the sensor data, and the updating of the display of the UI elements may include displaying a plurality of UI elements corresponding to the plurality of data points in a visually distinct manner based on the post-processing counts.

[0016] The updating of the display of the UI elements may include displaying the UI elements based on whether the post-processing count meets a predetermined count.

[0017] According to another aspect of the present disclosure, an electronic device is provided that includes a communication interface having at least one communication circuit, a display, a memory configured to store at least one instruction, and a processor that 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 a user interface (UI) element on the display indicative of a value of the sensor data, determining based on the sensor data whether to perform post-processing on the sensor data, performing post-processing on the sensor data based on a result of the determination, and updating a display of the UI element based on the post-processed sensor data.

[0018] The processor may determine to perform the post-processing if the 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 the post-processing if the standard deviation of the data points is at or less than the preset threshold.

[0019] 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 the post-processing if a time interval between the first time point and the second time point is shorter than a predetermined threshold time, and may determine not to perform the post-processing if the time interval is the predetermined threshold time or longer.

[0020] 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 a value of a data point corresponding to a predetermined index among the plurality of data points, generate a second data set corresponding to a second time period following 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 a value of a data point corresponding to the predetermined index among the plurality of data points included in the second data set.

[0021] The processor may normalize the first data set, perform a convolution operation on the first data set with the predefined filter to remove distortion of the first data set, and de-normalize the first data set.

[0022] The processor may normalize the first dataset by subtracting a value of a data point corresponding to a last index of the plurality of data points included in the first dataset from a value of each data point included in the first dataset.

[0023] The processor may update the display of the UI element such that the UI element indicates a value of the post-processed sensor data.

[0024] The processor may calculate a post-processing count for each of a plurality of data points included in the sensor data, and may visually distinguishably display a plurality of UI elements corresponding to the plurality of data points based on the post-processing counts.

[0025] The processor may display the plurality of UI elements based on whether the post-processing count meets a predetermined count.

[0026] Solutions to the objectives of the present disclosure are not limited to those described above, and other solutions not described above will be clearly understood by those skilled in the art from this specification and the accompanying drawings. [Brief description of the drawings]

[0027] The above and other objects, features, and advantages of the present disclosure will become more apparent to those skilled in the art from the detailed description of illustrative embodiments thereof taken in conjunction with the accompanying drawings.

[0028] [Figure 1] FIG. 1 is a schematic diagram of a continuous glucose monitoring system (CGMS) according to an exemplary embodiment of the present disclosure. [Diagram 2] 4 is a flowchart illustrating a method for controlling an electronic device according to an exemplary embodiment of the present disclosure. [Diagram 3] 1 illustrates information regarding sensor data, according to an exemplary embodiment of the present disclosure. [Figure 4] 4 illustrates an exemplary embodiment of a method for displaying the sensor data (30) of FIG. [Diagram 5] 1 illustrates a data set according to an exemplary embodiment of the present disclosure. [Figure 6] FIG. 1 illustrates a post-processing method according to an exemplary embodiment of the present disclosure. [Figure 7] FIG. 13 illustrates a post-processing method according to another exemplary embodiment of the present disclosure. [Figure 8] 4 is a flowchart illustrating a method for applying a filter according to an example embodiment. [Figure 9] 7 illustrates an exemplary embodiment of a method for displaying the first data set (61) of FIG. 6. [Figure 10] FIG. 1 is a block diagram of an analyte monitoring system 1000 according to an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0029] The terminology used herein will first be briefly explained, and then the present disclosure will be described in detail.

[0030] As terms used in this specification, currently used general terms are selected as widely as possible in consideration of the functions in this disclosure, but they may change according to the intentions of those skilled in the art, precedents, the emergence of new technology, and the like. In particular, terms may be arbitrarily selected by the applicant. In this case, the meaning of the term is explained in detail throughout the relevant explanation of this disclosure. Therefore, the terms used in this specification shall be defined based on their meaning and the overall content of this disclosure, not their names.

[0031] The present disclosure may be modified in various ways and have various embodiments, and a specific embodiment is shown in the drawings and described in detail. However, this is not intended to limit the present disclosure to a specific embodiment, and the present disclosure should be understood to include all modifications, equivalents, and alternatives within the spirit and technical scope disclosed. In describing the embodiments, detailed descriptions of related known technologies are omitted if they are deemed to obscure the subject matter of the present disclosure.

[0032] Terms such as "first," "second," and similar terms may be used to describe various components, but the components are not limited by the terms. The terms are used only to distinguish one component from another.

[0033] The singular terms include the plural terms unless the context clearly dictates otherwise. In this application, the terms "include," "have," and similar terms indicate the presence of features, integers, steps, operations, components, portions, or combinations thereof described herein, and do not exclude the presence or addition of one or more other features, integers, steps, operations, components, portions, or combinations thereof.

[0034] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings so that those skilled in the art 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 in this specification. In order to clearly explain the present disclosure, parts that are not relevant to the description are omitted in the drawings, and similar reference numerals refer to similar parts throughout the specification.

[0035] FIG. 1 is a schematic diagram of a continuous glucose monitoring system (CGMS) according to an exemplary embodiment of the present disclosure.

[0036] 1, analyte monitoring system 1000 may include analyte monitoring device 100 and electronic device 200. For example, analyte monitoring system 1000 may be a CGMS and 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. Electronic device 200 may be a user terminal device. As examples, electronic device 200 may be a smartphone, a tablet personal computer (PC), a smart watch, a personal digital assistant (PDA), or a dedicated receiver (e.g., a receiver).

[0037] Analyte monitoring device 100 may obtain information regarding a concentration of an analyte in a bodily fluid of user 1. The analytes may include glucose and ketones. The information regarding the concentration of the analyte may include an amplitude of a signal measured by analyte monitoring device 100. The information regarding the concentration of the analyte may include a value indicative of the concentration of the analyte.

[0038] The analyte monitoring device 100 may transmit information regarding the analyte concentration to the electronic device 200 according to a predetermined schedule. For example, the analyte monitoring device 100 may transmit information regarding the analyte concentration to the electronic device 200 every five minutes.

[0039] The analyte monitoring device 100 may be worn on the body of the user 1. At least a portion of the analyte monitoring device 100 may be inserted into the skin of the user 1 so as to be positioned inside the user's 1 body.

[0040] The electronic device 200 may provide information regarding the concentration of the analyte to a user. The electronic device 200 may display information regarding the concentration of the analyte. The information regarding the concentration of the analyte 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 concentration value by applying a predefined algorithm to the signal detected by the analyte monitoring device 100.

[0041] FIG. 2 is a flow chart illustrating a method for controlling an electronic device according to an exemplary embodiment of the present disclosure.

[0042] 2, the electronic device 200 may acquire sensor data indicative of a concentration of an analyte in a host (S1100). The sensor data may include a plurality of data points. The plurality of data points may be time series data having a certain 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 the sensor data or data points refers to acquiring, displaying, or processing the values ​​of the sensor data or data points.

[0043] Electronic device 200 may obtain sensor data in a variety of ways. According to an embodiment, electronic device 200 may receive a sensor signal from analyte monitoring device 100. Electronic device 200 may then derive 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 act of deriving an analyte concentration value from the sensor signal using a sensor sensitivity and / or offset value.

[0044] According to another embodiment, electronic device 200 may receive sensor data from analyte monitoring device 100. In other words, electronic device 200 may receive analyte concentration values ​​from analyte monitoring device 100. Here, analyte monitoring device 100 may derive sensor data by processing the sensor signal. Analyte monitoring device 100 may transmit the sensor signal and the sensor data to electronic device 200.

[0045] The electronic device 200 may display a user interface (UI) element showing the value of the sensor data (S1200). For example, the electronic device 200 may display the value of each of a number of data points included in the sensor data in a two-dimensional (2D) graph. The x-axis of the graph shows time, and the y-axis of the graph shows blood glucose level. The electronic device 200 may display a visual indicator at a position corresponding to the value and time of each data point. The visual indicator may be a point. The visual indicator may have any of a variety of shapes, such as a circle, a polygon, an arrow, and the like.

[0046] The electronic device 200 may display the most recent data point separately from previous data points. For example, the electronic device 200 may display only previous data points in a graph, but may also display not only the most recent data point in the graph, but also the value of the most recent data point as a number. The number representing the value of the most recent data point may be displayed in an area separate from the area in which the graph is displayed.

[0047] The electronic device 200 may determine whether to perform post-processing on the sensor data (S1300). The post-processing of the sensor data may be performed on a dataset consisting of a predetermined number (e.g., 10) of data points. The electronic device 200 may determine whether to perform post-processing on the dataset according to whether there is an error in the dataset. If there is an error in the dataset, the electronic device 200 may determine not to perform post-processing on the dataset. If there is no error in the dataset, the electronic device 200 may determine to perform post-processing on the dataset.

[0048] According to an embodiment, the electronic device 200 may determine whether there is an error in a data set based on the standard deviation of the data points included in the data set. Specifically, if the standard deviation is greater than a preset threshold, the electronic device 200 may determine that there is no error in the data set. On the other hand, if 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.

[0049] In other words, if the variability of the sensor data is below a preset threshold, post-processing may be omitted. By skipping post-processing, unnecessary computational tasks may be avoided, thereby saving computational resources. In addition, resource allocation may be more efficient, since resources may be focused only on data that requires processing.

[0050] According to another embodiment, the electronic device 200 may determine whether there is an error in the dataset based on whether there is a missing data point. If the time interval between data points constituting a dataset is equal to or longer than a threshold time due to the missing data point, the value of the dataset may be distorted in a post-processing process. If smoothing is applied to the dataset when the time interval is irregular or larger than the threshold time, the calibrated value may be significantly different from the actual value. To prevent this, the electronic device 200 may determine that there is an error in the dataset if the time interval exceeds a threshold. Although the missing data point is presented as an example, the same applies if the time interval between data points exceeds a threshold due to communication failure or other problems.

[0051] Specifically, the electronic device 200 may determine whether there is an error (i.e., a missing data point) in the dataset based on a time interval between data points included in the dataset. For example, the dataset 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. If the time interval is shorter than the preset threshold time, the electronic device 200 may determine that there is no error in the dataset. If the time interval is equal to or longer than the preset threshold time, the electronic device 200 may determine that there is an error in the dataset. 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. If it is determined to perform post-processing 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 adjusting sensor data values ​​to make them more visually interpretable to a user. For example, post-processing of sensor data may include applying a predefined filter to the sensor data to modify the sensor data values. Post-processing of sensor data may be performed on a per-dataset basis consisting of a predefined number of data points. In other words, the predefined filter may be applied on a per-dataset basis. The data set may consist of the most recent data point and a predefined number (e.g., 9) of previous data points.

[0052] The predefined filter may include a smoothing filter, which may include a Savitzky-Golay filter.

[0053] Applying a predefined filter to a dataset may include normalizing the dataset, performing a convolution operation on the dataset with the predefined filter, undistorting the dataset, and denormalizing the dataset. Applying a filter is described in more detail below with reference to FIG. 7.

[0054] When new data points are acquired, 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. Accordingly, the new data set may include some data points included in the previously generated data set. Thus, post-processing may be performed multiple times on one data point.

[0055] The values ​​of the sensor data according to the present disclosure may be calibrated values ​​of the raw data measured by the sensor. In this processing, the units of the sensor data may be changed from the current value to a concentration value. Post-processing of the sensor data may involve further calibrating the initially calibrated concentration value, which may be a retroactive calibration.

[0056] If it is determined not to perform post-processing on the sensor data ("NO" in S1300), the electronic device 200 may not perform post-processing on the sensor data. Thus, the UI elements displayed by the electronic device 200 may not be updated. For example, the representation of the UI element showing the glucose concentration value may remain unchanged.

[0057] 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 such that the UI element shows 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 level before post-processing to a second position to show a second blood glucose level updated following post-processing. In response, the user may recognize the second blood glucose level as his or her own blood glucose level.

[0058] The electronic device 200 may calculate a post-processing count for each of a plurality of data points included in the sensor data. The electronic device 200 may display a plurality of UI elements differently depending on the post-processing count for each data point. The electronic device 200 may display a UI element indicating a value of a data point differently depending on whether the post-processing count for a data point meets a predetermined count. For example, the post-processing count for a first data point may meet the predetermined count and the post-processing count for a second data point may not meet the predetermined count.

[0059] According to an embodiment, the electronic device 200 may display a first UI element corresponding to a first data point brighter or darker than a second UI element corresponding to a 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 indicated by the second UI element may be inaccurate. According to yet another embodiment, the electronic device 200 may display a UI element corresponding to a data point based on a difference value between the post-processed count and the predetermined count for the data point. For example, the UI element may be displayed brighter as the difference value increases.

[0060] FIG. 3 illustrates information regarding sensor data, according to an exemplary embodiment of the present disclosure.

[0061] 3, the sensor data may include data points x1, x2, x3, x4, x5, and x6. The information 30 about the sensor data may include information about the data points x1, x2, x3, x4, x5, and x6. The information about 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 level of the user.

[0062] The information about data points x1, x2, x3, x4, x5, and x6 may include the sequence of data points x1, x2, x3, x4, x5, and x6. The sequence of data points x1, x2, x3, x4, x5, and x6 may represent an index of each data point. The index of a data point may represent an index at which the data point was measured or derived. The order of the data points may be presented in ascending order over time. According to another embodiment, the order of the data points may be presented in descending order over time.

[0063] The information regarding 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 points corresponding to the data points may be the time points at which the values ​​of the data points are derived. The values ​​of the data points may not be derived at regular time intervals. 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 points may be delayed. Alternatively, due to a failure of the electronic device 200 or the analyte monitoring device 100, the time points at which the values ​​of the data points are actually derived may not have regular time intervals.

[0064] FIG. 4 illustrates an exemplary embodiment of a method for displaying the sensor data 30 of FIG.

[0065] 4, the electronic device 200 may display UI elements 41, 42, 43, 44, 45, and 46 corresponding to 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.

[0066] The electronic device 200 may represent the sensor data 30 in various types of graphs. The x-axis of the graph may represent time and the y-axis of the graph may represent blood glucose levels. According to an embodiment, the electronic device 200 may display the sensor data 30 as a dot graph, as shown in Fig. 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 curved line.

[0067] FIG. 5 illustrates a data set according to an exemplary embodiment of the present disclosure.

[0068] 5, there is a data set 51 comprised of six data points x1, x2, x3, x4, x5, and x6. The electronic device 200 may generate the data set using a predetermined number of data points. Although FIG. 5 shows an example where the predetermined number is six, the disclosure is not limited thereto.

[0069] The electronic device 200 may generate a new data set each time a new data point is acquired. For example, data set 51 may be generated upon acquisition of the most recent data point x6.

[0070] If the electronic device 200 does not have the predetermined number of data points, the electronic device 200 may not generate a data set until the predetermined number of data points are present. For example, early in a sensor usage cycle, the electronic device 200 may have fewer than the predetermined number of data points. In this case, the electronic device 200 may not generate a data set and may not perform post-processing on the data set. Thus, the point at which post-processing operations are performed on the sensor data in accordance with the present disclosure may be after at least the predetermined number of data points have been acquired.

[0071] On the other hand, the data set 51 may include a data point x6 having a value of 0. For example, the value of the data point may be 0 if application of the predefined algorithm to the sensor signal has not been completed. Alternatively, the value of the data point x6 may be 0 due to an internal error of the electronic device 200 or any other error.

[0072] When performing post-processing on the dataset 51 including the data point x6 having a value of 0, the values ​​of the dataset 51 may be distorted in the post-processing process. Therefore, it may be necessary to correct the value of the data point x6 that is 0 to a value other than 0, or to exclude the data point x6 having a value of 0 from the dataset 51 and generate the dataset 51 using subsequent data points having values ​​other than 0. 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 dataset 51. Accordingly, the user cannot view the values ​​of the post-processed sensor data in real time, which is problematic. To prevent this, the electronic device 200 may derive a new dataset 52 by correcting the value of the data point x6.

[0073] The electronic device 200 may correct the value of data point x6 based on the strength of the sensor signal corresponding to data point x6. Specifically, the electronic device 200 may derive a new value for data point x6 by applying a predefined algorithm to the sensor signal corresponding to data point x6. For example, applying the predefined algorithm may include deriving a blood glucose value from the sensor signal using a sensor sensitivity and / or offset value.

[0074] Such action of the electronic device 200 to correct the data set 51 can prevent the values ​​of the data set 51 from being distorted in the post-processing process, and allows the user to view the values ​​of the post-processed data in real time, without having to wait for new subsequent data.

[0075] 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 are errors in the data set 52.

[0076] 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, if 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 other hand, if 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.

[0077] 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. Referring again to FIG. 3, the electronic device 200 may calculate the interval between the first time t1 and the second time t2, the interval between the second time t2 and the third time t3, the interval between the third time t3 and the fourth time t4, the interval between the fourth time t4 and the fifth time t5, and the interval between the fifth time t5 and the sixth time t6. If 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 other hand, if any one 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.

[0078] FIG. 6 is a diagram illustrating a post-processing method according to an exemplary embodiment of the present disclosure.

[0079] 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. Values ​​of some of the data points x1, x2, x3, x4, x5, and x6 corresponding to a given index may be updated accordingly.

[0080] Even if a filter is applied to all data points x1, x2, x3, x4, x5, and x6, only the values ​​of data points x3, x4, and x5 corresponding to a certain index may be updated, and the values ​​of other data points x1, x2, and x6 may not be updated. The values ​​of data points x1 and x2 corresponding to the first few indexes may be distorted due to the application of a filter. Therefore, the values ​​of data points x1 and x2 may not be updated with values ​​obtained after the filter is applied. The value of data point x6 corresponding to the last index is the most recent blood glucose value that the user is most interested in, so if the value is changed in post-processing, it may cause confusion to the user. Therefore, the value of data point x6 may not be updated with values ​​obtained after the filter is applied.

[0081] After post-processing is performed on 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 count for the data points x3, x4, and x5 corresponding to a given index is incremented, while the post-processing count for the other data points x1, x2, and x6 is not incremented. Thus, the post-processing count N of the data set 61 may be different from the post-processing count for each individual data point. Also, some data points may have different post-processing counts.

[0082] When a new data point x7 is acquired, the electronic device 200 may acquire a second data set 62. The second data set 62 may be composed of the post-processed portions of the first data set 61 (x2, x3, x4, x5, and x6) and the new data point x7. In other words, the time periods of adjacent data sets may overlap and adjacent data sets may include common data points.

[0083] 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. Accordingly, values ​​of data points x4, x5, and x6 corresponding to a given index among data points x2, x3, x4, x5, x6, and x7 may be updated. Post-processing counts for data points x4, x5, and x6 corresponding to a given index may be increased by 1.

[0084] When a new data point x8 is acquired, the electronic device 200 may acquire a third data set 63. The third data set 63 may be composed of a portion of the post-processed second data set 62 (x3, x4, x5, x6, and x7) and the new data point x8.

[0085] 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. Accordingly, values ​​of data points x5, x6, and x7 corresponding to a given index among data points x3, x4, x5, x6, x7, and x8 may be updated. Post-processing counts for data points x5, x6, and x7 corresponding to a given index may be increased by 1.

[0086] As described above, the electronic device 200 may generate a data set each time a new data point is acquired, determine whether to perform post-processing on the data set, and then perform post-processing if it is determined to perform post-processing. While repeating post-processing on multiple data sets, the electronic device 200 may store a post-processing count for each data point.

[0087] FIG. 7 is a diagram illustrating a post-processing method according to another exemplary embodiment of the present disclosure.

[0088] 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 and determine whether to perform post-processing on the first data set 71. For example, a time interval between a first time point corresponding to a first data point x1 and a second time point corresponding to a 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.

[0089] There may be no errors in either the second data set 72 or the third data set 73. Thus, the electronic device 200 may perform post-processing on the second data set 72 and the third data set 73 in the same manner as described in FIG. 6 to obtain a post-processing count for each data point. Comparing FIG. 6 and FIG. 7, the post-processing counts for each of the data points x3, x4, and x5 are different. This is because no post-processing was applied to the first data set 71 due to an error. Thus, the post-processing counts for each data point may be different depending on whether there is an error.

[0090] FIG. 8 is a flow chart illustrating a method for applying a filter according to an example embodiment.

[0091] 8, the electronic device 200 may normalize the data set (S1410). The electronic device 200 may normalize the values ​​of each of the multiple data points included in the data set.

[0092] According to an embodiment, the electronic device 200 may subtract the value of the data point corresponding to the last index of the multiple 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 multiple data points. formula 1

number

[0093] According to another embodiment, the electronic device 200 may subtract the average of the multiple data points from the value of each data point. The electronic device 200 may then divide the value obtained by subtracting the average from the value of each data point by the standard deviation of the multiple data points.

[0094] 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.

[0095] The electronic device 200 may remove distortion of the data set (S1430). The electronic device 200 may remove parts of the result of the convolution operation that correspond to the first few indexes. This is because the parts that correspond to the first few indexes may contain distortion due to the filter characteristics. This is related to FIG. 6 in which the values ​​of the data points x2 and x3 included in the second data set 62 are not updated. This is because the values ​​of the data points x2 and x3 that correspond to the first few indexes may be distorted in 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 by post-processing.

[0096] On the other hand, the range of the indexes to be removed may be determined based on the length of the filter coefficients. For example, the number of indexes to be removed may be half the length of the filter coefficients. If the length of the filter coefficients is an odd number, the number of indexes to be removed may be half the length of the filter coefficients, with the decimal point being truncated.

[0097] The electronic device 200 may denormalize the dataset (S1440). The electronic device 200 may denormalize values ​​of a plurality of data points included in the dataset. The electronic device 200 may update a value of a data point having a predetermined index based on the denormalized values.

[0098] FIG. 9 illustrates an exemplary embodiment of a method for displaying the first data set 61 of FIG.

[0099] 9, the electronic device 200 may display UI elements 91, 92, 93, 94, 95, and 96 corresponding to data points x1, x2, x3, x4, x5, and x6 included in the first data set 61. The UI element 96 corresponding to the most recent data point x6 may be displayed so as to be visually distinct from the other UI elements 91, 92, 93, 94, and 95.

[0100] As described above, electronic device 200 may perform post-processing on first data set 61, and the values ​​of data points x3, x4, and x5 may be updated accordingly. Electronic device 200 may update the display of UI elements 93, 94, and 95 based on the updated values. As shown in the figure, electronic device 200 may change the position of UI elements 93, 94, and 95 to show the updated values.

[0101] 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. Accordingly, the post-processing may be performed multiple times on each data point, and the display of the UI element corresponding to each data point may be updated multiple times. If there are no errors in the data set, the post-processing count for a data point may be the same as the number of data points whose value was updated by a single process.

[0102] Although not shown in the figure, the electronic device 200 may update the display of UI elements corresponding to each data point based on the post-processing count for the data point. For example, the electronic device 200 may display data points that meet a predefined post-processing count differently from data points that do not meet a predefined post-processing count. In particular, the UI elements corresponding to the data points may differ in shape or color.

[0103] FIG. 10 is a block diagram of an analyte monitoring system 1000 according to an exemplary embodiment of the present disclosure.

[0104] With reference to FIG. 10, an analyte monitoring system 1000 can include an analyte monitoring device 100 and an electronic device 200 .

[0105] The analyte monitoring device 100 may include an analyte sensor 110 and a sensor electronics unit 120 .

[0106] The analyte sensor 110 may be an element for sensing an analyte signal (or a sensor signal). The analyte sensor 110 may include a sensor probe, at least a portion of which is inserted into the body. Within the sensor probe, a sensing region may be formed that is responsive to glucose in the body to measure the glucose concentration in the host. The analyte sensor 110 may be a continuous glucose sensor.

[0107] The sensor electronics unit 120 may include at least one communication interface. The sensor electronics 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.

[0108] The sensor electronics unit 120 may transmit a sensor signal acquired through the analyte sensor 110 to the electronic device 200. The sensor electronics unit 120 may also transmit data point values ​​derived from the sensor signal to the electronic device 200. The sensor electronics unit 120 may transmit the sensor signal or data point values ​​to the electronic device 200 at predetermined intervals (e.g., 5 minutes).

[0109] Sensor electronics unit 120 may include an operating system (OS) for controlling the overall operation of the components of analyte monitoring device 100, and a memory for storing instructions or data related to the components of analyte monitoring device 100. Sensor electronics unit 120 may also include a processor electrically connected to the memory for controlling the overall functionality and operation of analyte monitoring device 100.

[0110] 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 smartphone.

[0111] The display 210 may output sensor data. For example, the display 210 may output the user's blood glucose level.

[0112] The communications interface 220 may include at least one communications circuit. The communications interface 220 may receive sensor signals from the analyte monitoring device 100. The communications interface 220 may receive data point values ​​from the analyte monitoring device 100.

[0113] The memory 230 may store an OS for controlling the overall operation of the components of the electronic device 200, and instructions or data related to the components of the electronic device 200. The memory 230 may be implemented as a non-volatile memory (e.g., a hard disk, a solid state drive (SSD), or a flash memory), a volatile memory, or the like.

[0114] The processor 240 is electrically connected to the memory 230 and may control the overall functionality and operation of the electronic device 200. The processor 240 may control the electronic device 200 by executing instructions stored in the memory 230.

[0115] Processor 240 may obtain sensor data indicative of a concentration of an analyte in the host. According to an embodiment, processor 240 may derive the sensor data from a sensor signal received from analyte monitoring device 100 through communications interface 220. According to another embodiment, processor 240 may receive sensor data indicative of a concentration of an analyte from analyte monitoring device 100 through communications interface 220.

[0116] The processor 240 may display UI elements indicating values ​​of the sensor data on the display 210. The processor 240 may control the display such that the UI elements are displayed.

[0117] The processor 240 may determine whether to perform post-processing on the sensor data based on the sensor data. According to an embodiment, if 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. On the other hand, if the standard deviation is less than or equal to the preset threshold, the processor 240 may determine not to perform post-processing.

[0118] According to another embodiment, the processor 240 may determine whether to perform post-processing based on a 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. If 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 other hand, if the time interval is equal to or longer than the preset threshold time, the processor 240 may determine not to perform post-processing.

[0119] The processor 240 may perform post-processing on the sensor data. The processor 240 may correct values ​​of data points corresponding to a predetermined index among the plurality of data points corresponding to a first time period by applying a predefined filter to a first data set composed of a plurality of data points. The processor 240 may generate a second data set corresponding to a second time period following the first time period using the data points whose values ​​have been corrected. The processor 240 may correct values ​​of data points corresponding to a predetermined index among the plurality of data points included in the second data set by applying a predefined filter to the second data set. Here, the first time period may overlap with at least a portion of the second time period.

[0120] The processor 240 may apply a predefined filter to the dataset. The processor 240 may normalize the first dataset. For example, the processor 240 may normalize the first dataset by subtracting a value of a data point corresponding to a last index of a plurality of data points included in the first dataset from a value of each data point included in the first dataset.

[0121] The processor 240 may perform a convolution operation on the first data set using a predefined filter. The processor 240 may remove distortions of the first data set. The processor 240 may denormalize the first data set.

[0122] The processor 240 may update the display of the UI element based on the post-processed sensor data. For example, the processor 240 may change the display of the UI element such that the UI element shows the value of the post-processed sensor data.

[0123] The processor 240 may obtain a post-processing count for each of a plurality of data points included in the sensor data. The processor 240 may visually distinguishably display a plurality of UI elements corresponding to the plurality of data points based on the post-processing count. For example, the processor 240 may display a plurality of UI elements based on whether the post-processing count meets a predetermined count.

[0124] According to embodiments of the present disclosure, it is possible to minimize distortion of blood glucose concentration values ​​provided to a user.

[0125] According to the embodiments of the present disclosure, a user can easily view his / her blood glucose concentration, which may increase the convenience and satisfaction of the user.

[0126] Other advantages that can be achieved or expected from the embodiments of the present disclosure are explicitly or implicitly disclosed in the detailed description of the embodiments of the present disclosure. For example, various advantages that are expected from the embodiments of the present disclosure are disclosed in the above description.

[0127] Other aspects, advantages and features of the present disclosure will become apparent to those skilled in the art from the above description, which, taken in conjunction with the accompanying drawings, discloses various embodiments of the invention.

[0128] The various embodiments described above may be implemented in a recording medium that can be read by a computer or similar device using software, hardware, or a combination thereof. In some cases, the embodiments described herein may be implemented as a processor. When an embodiment is implemented as software, the embodiments of the procedures, functions, and the like described herein may be implemented as separate software modules. Each of the software modules may perform one or more functions and operations described herein.

[0129] Computer instructions for performing the processing operations according to the various embodiments of the present disclosure described above may be stored in a non-transitory computer-readable recording medium. These computer instructions stored in the non-transitory computer-readable recording medium, when executed by a processor, can cause a particular device to perform the processing operations according to the various embodiments described above.

[0130] A non-transitory computer-readable medium is a machine-readable medium that stores data semi-permanently, as opposed to a medium that stores data for a short moment, such as a register, a cache, a memory, or the like. Examples of non-transitory computer-readable media may include a compact disc (CD), a digital versatile disc (DVD), a hard disk, a Blu-ray disc, a Universal Serial Bus (USB) memory, a memory card, a read-only memory (ROM), and the like.

[0131] The machine-readable medium may be provided in the form of a non-transitory storage medium, where a "non-transitory storage medium" is a tangible device that does not contain any signals (e.g., electromagnetic waves), and the term is used regardless of whether data is stored semi-permanently or temporarily in the storage medium. For example, a "non-transitory storage medium" may include a buffer in which data is temporarily stored.

[0132] The methods according to various embodiments disclosed herein 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., CD-ROM), or may be distributed online (e.g., downloaded or uploaded) via an application store (e.g., PlayStore®), or may be distributed 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 app) may be temporarily generated or at least temporarily stored in a machine-readable storage medium, such as a memory of a manufacturer's server, an application store's server, or a relay server.

[0133] Although the exemplary embodiments of the present disclosure have been shown and described above, the present disclosure is not limited thereto. Those skilled in the art can make various modified embodiments without departing from the spirit of the present disclosure described in the claims, and it is understood that these modified embodiments belong to the technical spirit or scope of the present disclosure.

Claims

1. acquiring sensor data indicative of a concentration of an analyte in the host; displaying a user interface (UI) element indicative of a value of the sensor data; determining, based on the sensor data, whether to perform post-processing on the sensor data; performing post-processing on the sensor data based on the result of the determination; and updating the display of the UI elements based on the post-processed sensor data.

13. A method for controlling an electronic device comprising:

2. The step of determining whether to perform post-processing comprises: determining to perform the post-processing if a standard deviation of a predetermined number of data points included in the sensor data is greater than a preset threshold; and determining not to perform the post-processing if the standard deviation of the data points is at or below the preset threshold.

2. The method of claim 1, comprising:

3. the sensor data includes 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 step of determining whether to perform post-processing comprises: determining to perform the post-processing if a time interval between the first time point and the second time point is less than a preset threshold time; and determining not to perform the post-processing if the time interval is equal to or greater than the preset threshold time; 2. The method of claim 1, comprising:

4. The step of performing the post-processing comprises: applying a predefined filter to a first data set comprising a plurality of data points corresponding to a first time period to correct a value of a data point corresponding to a predetermined index of the plurality of data points; generating a second data set using the data points with the corrected values, the second data set corresponding to a second time period subsequent to the first time period; and applying the predefined filter to the second data set to correct a value of a data point of a plurality of data points included in the second data set that corresponds to the predetermined index; 4. The method according to claim 1 , further comprising:

5. The method of claim 4 , wherein the first time period overlaps with at least a portion of the second time period.

6. The step of applying the predefined filter to the first data set includes: 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; and Denormalizing the first data set.

5. The method of claim 4, comprising:

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

8. The method of claim 1 , wherein the updating the display of the UI element comprises updating the display of the UI element such that the UI element shows a value of the post-processed sensor data.

9. calculating a post-processing count for each of a plurality of data points included in the sensor data; wherein the step of updating the display of the UI elements includes visually distinguishably displaying a plurality of UI elements corresponding to the plurality of data points based on the post-processing counts.

4. The method according to any one of claims 1 to 3.

10. The method of claim 9 , wherein the updating the display of the UI elements comprises displaying the UI elements based on whether the post-processing count meets a predetermined count.

11. a communication interface having at least one communication circuit; display; a memory configured to store at least one instruction; and Processor wherein the processor comprises: obtaining sensor data indicative of a concentration of an analyte in the host; displaying on the display a user interface (UI) element indicative of a value of the sensor data; determining, based on the sensor data, whether to perform post-processing on the sensor data; performing post-processing on the sensor data based on the result of the determination; and updating the display of the UI elements based on the post-processed sensor data. Executing the at least one instruction to perform the operation of electronic equipment.

12. 12. The electronic device of claim 11, wherein the processor determines to perform the post-processing if a 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 if the standard deviation of the data points is at or less than the preset threshold.

13. 12. The electronic device of claim 11, wherein the sensor data includes 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 if a 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 if the time interval is equal to or longer than the preset threshold time.

14. The processor applies a predefined filter to a first data set comprising a plurality of data points corresponding to a first time period to correct a value of a data point corresponding to a predetermined index of the plurality of data points; generating a second data set using the data points with the corrected values, the second data set corresponding to a second time period subsequent to the first time period; and 14. The electronic device of claim 11, further comprising: applying the predefined filter 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 with at least a portion of the second time period.

16. 15. The electronic device of claim 14, wherein the processor normalizes the first data set, performs a convolution operation on the first data set with the predefined filter to remove distortion of the first data set, and de-normalizes the first data set.

17. 17. The electronic device of claim 16, wherein the processor normalizes the first data set by subtracting a value of a data point corresponding to a last index of the plurality of data points included in the first data set from a value of each data point included in the first data set.

18. The electronic device of claim 11 , wherein the processor is further configured to update the display of the UI element such that the UI element indicates a value of the post-processed sensor data.

19. 14. The electronic device of 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 UI elements corresponding to the plurality of data points in a visually distinct manner based on the post-processing count.

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

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