Method for monitoring thyroid function according to drug intake, and monitoring server and user terminal for performing same

Abstract

To provide a monitoring method for guiding a patient to visit a hospital by preventing the patient from taking too much medicine.SOLUTION: The present invention relates to a method of determining whether to output a warning message regarding a thyroid dysfunction of a user, and provides a thyroid dysfunction monitoring method including a S4100 of receiving medication ingestion information of a user from an external device, a S4300 of selecting a monitoring algorithm used to determine whether to output a warning message based on the medication ingestion information, and a S4500 of determining whether to output a warning message based on the selected monitoring algorithm.SELECTED DRAWING: Figure 25

Description

[Technical Field]

[0001] The present invention relates to a method for monitoring thyroid function associated with drug administration.

[0002] The present invention also relates to a monitoring server that performs thyroid function monitoring in association with medication intake.

[0003] The present invention also relates to a user terminal for performing thyroid function monitoring in association with medication intake.

[0004] The present invention also relates to a method for monitoring thyroid function based on skin conductance data. [Background technology]

[0005] According to statistics, 12% of the total population in the United States will experience thyroid dysfunction in their lifetime, and approximately 20 million Americans are known to suffer from illnesses caused by thyroid dysfunction. Thyroid dysfunction is a disease that requires attention and continuous monitoring, as it causes inconvenience and complications in the lives of many people not only in the United States but all over the world.

[0006] However, currently, monitoring for thyroid dysfunction requires visiting a hospital for a blood test, and the timing of the test is often delayed due to hospital appointments. This makes systematic monitoring impossible, and the visit to the hospital itself is essentially a waste of time for patients, so many patients wait until symptoms of thyroid dysfunction appear before undergoing testing.

[0007] This lack of monitoring for thyroid dysfunction leads to various negative effects, such as worsening of patient symptoms and increased medical costs, and therefore there is a need for a method for continuous and easy-to-use thyroid function monitoring for patients.

[0008] In addition, most patients with thyroid dysfunction are taking medication for treatment, but they are concerned about the appropriate dosage of the medication they are taking and whether it will cause side effects to their body. Therefore, there is a need to develop a thyroid function monitoring method that can be provided to patients who are taking medication. Summary of the Invention [Problem to be solved by the invention]

[0009] An embodiment of the present invention provides a monitoring method for detecting the occurrence of thyroid dysfunction as a side effect of medication taken by a patient.

[0010] Embodiments of the present invention provide a monitoring method for preventing patient drug overdose and guiding hospital visits.

[0011] An embodiment of the present invention provides a method for predicting thyroid dysfunction in a user based on skin conductance data acquired through a wearable device. [Means for solving the problem]

[0012] A method for determining whether to output a warning message regarding abnormal thyroid function of a user according to an embodiment of the present invention, comprising: receiving medication information of the user from an external device, wherein the medication information includes at least one of prescription date and time of a medication regarding thyroid function, medication name, medication type, medication dose, and medication cycle; selecting a monitoring algorithm to be used for determining whether to output the warning message based on the medication information, wherein the monitoring algorithm is a first monitoring algorithm or a second monitoring algorithm different from the first monitoring algorithm; and determining whether to output the warning message based on the selected monitoring algorithm; the step of determining whether to output the warning message includes the steps of: determining to output the warning message when the monitored heart rate is greater than a reference heart rate by a first critical value or more, if the selected monitoring algorithm is the first monitoring algorithm; and determining to output the warning message when the monitored heart rate is smaller than the reference heart rate by a second critical value or more, if the selected monitoring algorithm is the second monitoring algorithm; the reference heart rate is calculated based on the user's thyroid hormone level and the user's heart rate, or based on the user's heart rate for multiple consecutive days, and the monitoring heart rate is calculated based on the user's heart rate during a rest period; The rest period is selected based on information about the user's exercise state. [Effects of the Invention]

[0013] According to the present invention, there is provided a monitoring method for monitoring thyroid dysfunction that may occur as a side effect of medication in a patient, so that a patient who is concerned about side effects associated with medication can continue treatment in a stable state.

[0014] According to the present invention, there is provided a monitoring method for preventing a patient from taking too much medicine by monitoring whether the patient continues to take prescribed medicine even though treatment has been completed due to a prolonged period of taking medicine, thereby encouraging the patient to visit a hospital.

[0015] According to the present invention, there is provided a method for defining a rest period in skin conductance data acquired through a wearable device and predicting a user's thyroid dysfunction based on the skin conductance data in the rest period.

[0016] The effects of the present invention are not limited to those described above, and effects not mentioned above will be clearly understood by those skilled in the art from this specification and the accompanying drawings. [Brief explanation of the drawings]

[0017] [Figure 1] FIG. 1 is a schematic diagram of a thyroid function monitoring system according to an embodiment of the present invention. [Figure 2] FIG. 1 is a block diagram of a wearable device according to an embodiment of the present invention. [Figure 3] 2 is a block diagram of a user terminal according to an embodiment of the present invention; [Figure 4] FIG. 2 is a block diagram of a monitoring server according to an embodiment of the present invention. [Figure 5] 1 is a flowchart illustrating a thyroid function monitoring method according to an embodiment of the present invention. [Figure 6] 1 is a flowchart illustrating a method for calculating monitoring data according to an embodiment of the present invention. [Figure 7] 10 is a flowchart illustrating a method for calculating reference data according to an embodiment of the present invention. [Figure 8] 1 is a flowchart illustrating a reference data calculation method according to an embodiment of the present invention. [Figure 9] 10 is a flowchart illustrating a method for calculating reference data when a monitoring server receives thyroid status information outside the normal range according to an embodiment of the present invention. [Figure 10] 1 is a flowchart illustrating a method for determining thyroid dysfunction according to an embodiment of the present invention. [Figure 11] 1 is a flowchart illustrating an algorithm for comparing reference data and monitoring data according to an embodiment of the present invention. [Figure 12] FIG. 1 shows the characteristics of clinicians participating in the clinical research process by clinical group. [Figure 13] FIG. 1 shows the change in thyroid function parameters between visits 1 and 2 in clinicians who participated in the clinical study course. [Figure 14]FIG. 14 shows the results of an analysis of the relationship between free T4 thyroid hormone concentration and heart rate parameters based on the changes in the thyroid function parameters shown in FIG. 13. [Figure 15] FIG. 14 shows the results of an analysis of the relationship between hypothyroidism and heart rate parameters based on the changes in the thyroid function parameters shown in FIG. 13. [Figure 16] This figure shows the change in average free T4 at the time of visit, the change in hypothyroidism symptom score at the time of visit, the change in On-site HR at the time of visit, the change in WD-rHR at the time of visit, the change in WD-sleepHR at the time of visit, and the change in WD-2to6HR at the time of visit, based on the change in thyroid function parameters by time of visit shown in Figure 13. [Figure 17] 10A and 10B are diagrams for explaining the timing of operation execution of a method for monitoring abnormal thyroid function according to an embodiment of the present invention. [Figure 18-19] FIG. 10 is a diagram illustrating a user interface in the abnormal thyroid function monitoring system according to an embodiment of the present invention. [Figure 20] 1 is a flowchart illustrating thyroid dysfunction monitoring according to an embodiment of the present invention. [Figure 21] A diagram for explaining PPG data acquired through a wearable device and analysis of the PPG data performed through a monitoring server. [Figure 22] 1 is a flowchart illustrating thyroid function monitoring based on a second factor according to an embodiment of the present invention. [Figure 23] 1 is a conceptual diagram illustrating a method for acquiring ECG data using a wearable device according to an embodiment of the present invention. [Figure 24] 24(a) and 24(b) are diagrams for explaining a method for analyzing the waveform of ECG data according to an embodiment of the present invention. [Figure 25] 1A and 1B are diagrams illustrating a method for monitoring thyroid function in consideration of medication according to an embodiment of the present invention. [Figure 26]1 is a flowchart illustrating a method for selecting a monitoring algorithm according to an embodiment of the present invention. [Figure 27] 1 is a flowchart illustrating a thyroid function monitoring method involving selection of a 1-1 monitoring algorithm according to an embodiment of the present invention. [Figure 28] 1 is a flowchart illustrating a thyroid function monitoring method involving selection of a first-second monitoring algorithm according to an embodiment of the present invention. [Figure 29] FIG. 10 is a diagram for explaining the timing of execution of operations in the method for monitoring abnormal thyroid function (S400) according to an embodiment of the present invention. [Figure 30] 10A and 10B are diagrams illustrating a user interface of a user terminal that provides medication advice according to an embodiment of the present invention. [Figure 31] FIG. 1 is a block diagram of a skin conductance measuring sensor according to an embodiment of the present invention. [Figure 32] 1 shows an apparatus for measuring skin conductance according to an embodiment of the present invention; [Figure 33] FIG. 10 shows a graph of skin conductance according to an embodiment of the present invention. [Figure 34] FIG. 10 shows a graph of skin conductance according to another embodiment of the present invention. [Figure 35] FIG. 10 shows a graph of skin conductance data according to an embodiment of the present invention. [Figure 36] FIG. 10 is a diagram illustrating pause periods in a graph of skin conductance data according to an embodiment of the present invention. [Figure 37] 10 is a diagram illustrating pause periods in a graph of skin conductance data according to another embodiment of the present invention. FIG. [Figure 38] FIG. 10 is a diagram illustrating pause periods in a graph of skin conductance data according to another embodiment of the present invention. [Figure 39] FIG. 10 is a diagram illustrating pause periods in a graph of skin conductance data according to another embodiment of the present invention. [Figure 40] FIG. 10 is a diagram illustrating pause periods in a graph of skin conductance data according to another embodiment of the present invention. [Figure 41] FIG. 10 is a diagram illustrating pause periods in a graph of skin conductance data according to another embodiment of the present invention. [Figure 42] 1 is a flowchart illustrating a method for extracting pauses according to an embodiment of the present invention. [Figure 43] FIG. 10 is a graph showing skin conductance data according to the wearing state of a wearable device according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0018] The above-mentioned objects, features, and advantages of the present invention will become more apparent from the following detailed description taken in conjunction with the accompanying drawings. However, the present invention can be modified in various ways and can have various embodiments. In the following, specific embodiments will be described in detail with reference to the drawings.

[0019] In the drawings, the thicknesses of layers and regions have been exaggerated for clarity of illustration, and an element or layer described as being "on" another element or layer does not necessarily mean directly on top of the other element or layer, but also includes cases where other layers or other elements are interposed. The same reference numerals used throughout the specification essentially refer to the same elements. Furthermore, the same reference numerals are used to describe elements with the same function within the same concept shown in the drawings of each embodiment.

[0020] If a detailed description of well-known functions or configurations of the present invention is deemed unnecessary for the gist of the present invention, the detailed description will be omitted. Furthermore, numbers (e.g., 1, 2, etc.) used in the description of this specification are merely identification symbols for distinguishing one component from another.

[0021] In the following description, the suffixes "module" and "section" are used to refer to components in order to facilitate the preparation of the specification, and do not have any meanings or roles that distinguish them from each other.

[0022] According to an embodiment of the present invention, there is provided a method for determining whether to output a warning message regarding a user's thyroid function abnormality, the method including the steps of receiving medication information of the user from an external device, the medication information including at least one of prescription date and time, medication name, medication type, medication dosage, and medication cycle of a medication related to thyroid function; selecting a monitoring algorithm to be used for determining whether to output the warning message based on the medication information, the monitoring algorithm being a first monitoring algorithm or a second monitoring algorithm different from the first monitoring algorithm; and determining whether to output the warning message based on the selected monitoring algorithm. The step of determining whether to output the warning message includes, if the selected monitoring algorithm is the first monitoring algorithm, determining to output the warning message when a monitored heart rate is greater than a reference heart rate by a first critical value or more, and, if the selected monitoring algorithm is the second monitoring algorithm, determining to output the warning message when the monitored heart rate is smaller than the reference heart rate by a second critical value or more. In addition, the reference heart rate is calculated based on the user's thyroid hormone level and the user's heart rate, or based on the user's heart rate for multiple consecutive days, and the monitoring heart rate is calculated based on the user's heart rate during a pause period, and the pause period is selected based on information regarding the user's exercise state.

[0023] In the method for determining whether to output a warning message regarding abnormal thyroid function of the user, the step of selecting the monitoring algorithm includes a step of classifying the user into a hyperthyroidism treatment group or a hypothyroidism treatment group based on the medication taking information.

[0024] The method for determining whether to output a warning message regarding the user's thyroid dysfunction includes selecting the first monitoring algorithm when the user is classified into the hypothyroidism treatment group.

[0025] In the method for determining whether to output a warning message regarding abnormal thyroid function of the user, the step of selecting the monitoring algorithm includes a step of selecting the second monitoring algorithm if the user is classified into a hyperthyroidism treatment group.

[0026] In the method for determining whether to output a warning message regarding abnormal thyroid function of the user, the step of determining whether to output the warning message includes, when the selected monitoring algorithm is the first monitoring algorithm, a step of checking whether a predetermined period has elapsed based on the prescription date and time when the monitored heart rate is smaller than the reference heart rate by a second critical value or more, and a step of determining whether to output the warning message if the predetermined period has elapsed.

[0027] In the method for determining whether to output a warning message regarding abnormal thyroid function of the user, the step of determining whether to output the warning message includes, when the selected monitoring algorithm is the second monitoring algorithm, a step of checking whether a predetermined period has elapsed based on the prescription date and time when the monitored heart rate is greater than the reference heart rate by a first critical value or more, and a step of determining whether to output the warning message if the predetermined period has elapsed.

[0028] The method for determining whether to output a warning message regarding abnormal thyroid function of the user includes, before receiving the medication information from the external device, a step of determining whether to output the warning message if the monitored heart rate is greater than the reference heart rate by a first critical value or more, and a step of determining whether to output the warning message if the monitored heart rate is smaller than the reference heart rate by a second critical value or more.

[0029] In the method for determining whether to output a warning message regarding abnormal thyroid function of the user, the pause section is determined based on a section in which the user's step count is 0 and continues for a predetermined time or more, or the pause section is determined based on a section in which the user's acceleration is 0 and continues for a predetermined time or more.

[0030] The method for determining whether to output a warning message regarding abnormal thyroid function of the user further includes a step of calculating the reference heart rate, wherein the step of calculating the reference heart rate includes a step of confirming whether the received thyroid hormone value is within a normal range when the thyroid hormone value is received, and a step of calculating the reference heart rate based on resting heart rates for a plurality of consecutive days including the day of the test of the thyroid hormone value if the thyroid hormone value is within the normal range.

[0031] In the method for determining whether to output a warning message regarding abnormal thyroid function of the user, the step of calculating the reference heart rate includes a step of calculating a current heart rate based on resting heart rates for multiple consecutive days including the day of the thyroid hormone test when the thyroid hormone value is outside a normal range, and a step of estimating the reference heart rate when the user has normal thyroid function based on the thyroid hormone value and the calculated current heart rate.

[0032] In the method for determining whether to output a warning message regarding the user's thyroid dysfunction, the step of selecting the monitoring algorithm is performed each time the medication taking information is received, the step of determining whether to output the warning message is performed every day after the step of selecting is performed, and the step of determining whether to output the warning message is performed a greater number of times than the step of selecting the monitoring algorithm.

[0033] The method for determining whether to output a warning message regarding abnormal thyroid function of the user further includes a step of determining whether to output a notification of taking a medication based on the medication cycle, and the step of determining whether to output a notification of taking a medication is performed more times than the step of determining whether to output the warning message.

[0034] The method for determining whether to output a warning message regarding abnormal thyroid function of the user further includes receiving heart rate information of the user every second period from the external device that measures the heart rate of the user every first period, the second period being longer than the first period.

[0035] The method for determining whether to output a warning message regarding abnormal thyroid function of the user further includes transmitting a signal to the external device to output the warning message through a display unit of the external device if it is determined to output the warning message.

[0036] According to an embodiment of the present invention, there is provided a recording medium having computer readable code stored thereon, the recording medium storing a program for performing the method recited in any one of the above claims.

[0037] According to an embodiment of the present invention, a monitoring server is provided, comprising: a communication unit that receives biological information acquired from a user of a wearable device from an external device; and a control unit that selects a monitoring algorithm based on medication information of the user received through the communication unit, determines whether to output a warning message based on the selected monitoring algorithm, and controls to transmit a signal through the communication unit when it is determined to output the warning message, wherein the medication information includes at least one of prescription date and time of a medication related to thyroid function, medication name, medication type, medication dose, and medication cycle, and the monitoring algorithm is a first monitoring algorithm or a second monitoring algorithm. the first monitoring algorithm is an algorithm that determines whether to output the warning message when the monitored heart rate is greater than a reference heart rate by a first critical value or more, and the second monitoring algorithm is an algorithm that determines whether to output the warning message when the monitored heart rate is smaller than the reference heart rate by a second critical value or more, the reference heart rate is calculated based on the user's thyroid hormone level and the user's heart rate, or is calculated based on the user's heart rate for multiple consecutive days, the monitoring heart rate is calculated based on the user's heart rate in a pause section, and the pause section is selected based on information regarding the user's exercise state.

[0038] According to an embodiment of the present invention, a user terminal is provided, comprising: a communication unit that receives biological information acquired from a user of a wearable device from the wearable device; an input unit that receives medication information of the user; wherein the medication information includes at least one of prescription date and time of a medication related to thyroid function, medication name, medication type, medication dose, and medication cycle; and a control unit that selects a monitoring algorithm based on the medication information, determines whether to output a warning message based on the selected monitoring algorithm, and controls an output unit to output a warning message related to abnormal thyroid function of the user, wherein the monitoring algorithm is a first monitoring algorithm. the first monitoring algorithm is an algorithm for determining whether to output the warning message when the monitored heart rate is greater than a reference heart rate by a first critical value or more, and the second monitoring algorithm is an algorithm for determining whether to output the warning message when the monitored heart rate is smaller than the reference heart rate by a second critical value or more, the reference heart rate is calculated based on the user's thyroid hormone level and the user's heart rate, or is calculated based on the user's heart rate for multiple consecutive days, the monitoring heart rate is calculated based on the user's heart rate in a pause section, and the pause section is selected based on information regarding the user's exercise state.

[0039] According to an embodiment of the present invention, there is provided a method for predicting thyroid dysfunction of a user using a wearable device worn on a part of the user's body, the method including the steps of acquiring skin conductance data of the user through the wearable device that measures the user's skin conductance; extracting a pause interval in which the skin conductance data changes within a critical range for a predetermined period based on the skin conductance data; and predicting thyroid dysfunction of the user by comparing a reference skin conductance with a monitoring skin conductance, wherein the pause interval is an interval that starts after a decrease interval in which the skin conductance data decreases by more than the magnitude of the critical range, the reference skin conductance is calculated based on the skin conductance data in the pause interval during the reference period, and the monitoring skin conductance is calculated based on the skin conductance data in the pause interval during the monitoring period, wherein the monitoring period is a period for determining whether the user's thyroid function is abnormal, and the reference period and the monitoring period do not overlap each other.

[0040] Here, the monitoring period may include at least one day.

[0041] Here, the measurement cycle of the skin conductance of the wearable device may be shorter than the predetermined period.

[0042] Here, the magnitude of the critical range is 2 μS or less.

[0043] Here, the method includes a step of transmitting the message to the wearable device based on the result of the predicting step, wherein the message includes a first warning message when the monitored skin conductance is greater than the reference skin conductance by a first value, and a second warning message when the monitored skin conductance is less than the reference skin conductance by a second value, wherein the first warning message includes information regarding hyperthyroidism, and the second warning message includes information regarding hypothyroidism.

[0044] Here, the first value and the second value may be different from each other.

[0045] Here, if the number of times the first warning message or the second warning message is output exceeds a predetermined number of times within a certain period of time, the message may include a comment suggesting that an expert's opinion be sought.

[0046] Here, the monitoring period may be based on the user's sleep period.

[0047] Here, the monitoring period may include a plurality of REM sleep periods of the user.

[0048] Here, the monitoring skin conductance may be obtained based on at least one or more of the pause periods.

[0049] Here, the reference skin conductance may be obtained based on at least one or more of the pause periods when the user has a normal thyroid function.

[0050] Here, the message may include a questionnaire for self-diagnosis.

[0051] Here, the skin conductance data during the pause period may be equal to or less than a predetermined value.

[0052] Here, the average of the skin conductance data before the decrease section may be greater than the average of the skin conductance data after the decrease section.

[0053] Here, a change frequency of the skin conductance data before the decrease section may be greater than a change frequency of the skin conductance data after the decrease section, and the change frequency may be based on a differential value of the skin conductance data.

[0054] Here, a change frequency of the skin conductance before the decrease section may be greater than a change frequency of the skin conductance during the decrease section, and the change frequency may be based on a differential value of the skin conductance data.

[0055] Here, the frequency of change of the skin conductance data during the wearing period is greater than the frequency of change of the skin conductance data during the rest period, the wearing period is a certain period after the user wears the wearable device, and the frequency of change can be based on a differential value of the skin conductance data.

[0056] Here, the difference in skin conductance before and after the wearing period is greater than the difference in skin conductance before and after the pause period, and the wearing period is a certain period after the user wears the wearable device.

[0057] Here, the first time point included in the reference period may be a time point that precedes the second time point included in the monitoring period.

[0058] It is possible to provide a recording medium on which a program for executing any one of the methods described above is recorded and on which computer-readable code is stored.

[0059] Hereinafter, a thyroid function monitoring system (100) according to an embodiment of the present specification will be described.

[0060] The thyroid function monitoring system (100) is a system that senses a user's biological signals through a wearable device (1000) and predicts the thyroid function of the user of the wearable device (1000) based on the sensed biological signals.

[0061] <Thyroid Function Monitoring System (100)> FIG. 1 is a schematic diagram of a thyroid function monitoring system (100) according to one embodiment of the present invention.

[0062] Referring to FIG. 1, the thyroid function monitoring system (100) may include a wearable device (1000), a user terminal (2000), and a monitoring server (3000).

[0063] However, the components illustrated in FIG. 1 are not required, and the thyroid function monitoring system (100) may include more or fewer components.

[0064] The wearable device (1000) can be worn on the user's body and can sense the user's biological signals.

[0065] The wearable device 1000 can sense the user's biological information. As one example, the wearable device 1000 can sense the user's heart rate information. As another example, the wearable device 1000 can sense the user's heart rate information and the user's movement information. As yet another example, the wearable device 1000 can sense the user's heart rate information and the user's temperature information. As yet another example, the wearable device 1000 can sense the user's skin conductance information. Of course, the examples given in this specification are not limiting, and the wearable device 1000 can sense one or more biological information corresponding to the user's biological signal.

[0066] The wearable device 1000 can transmit the sensed biological information to the user terminal 2000 and / or the monitoring server 3000. For example, the wearable device 1000 can transmit the sensed biological information to the user terminal 2000, and the user terminal 2000 can transmit the received biological information to the monitoring server 3000.

[0067] As one example, the wearable device (1000) may transmit the user's heart rate information to the user terminal (2000). As another example, the wearable device (1000) may transmit the user's movement information to the user terminal (2000). As yet another example, the wearable device (1000) may transmit the user's temperature to the user terminal (2000). As yet another example, the wearable device (1000) may transmit the user's skin conductance information to the user terminal (2000).

[0068] The wearable device (1000) can transmit the sensed first biological information to the user terminal (2000) in conjunction with information about the external environment.

[0069] In one example, the wearable device 1000 may transmit information about the sensed first biological signal in conjunction with time information to the user terminal 2000. As a specific example, the wearable device 1000 may map the user's heart rate information with time information and transmit the information to the user terminal 2000.

[0070] In another example, the wearable device 1000 may transmit information about the sensed first biological signal in conjunction with external temperature information to the user terminal 2000. As a specific example, the wearable device 1000 may map the user's skin conductance information with external temperature information and transmit the information to the user terminal 2000.

[0071] The wearable device (1000) can transmit the sensed first biometric information to the user terminal (2000) in conjunction with the first biometric information and other second biometric information.

[0072] For example, the wearable device (1000) can transmit multiple types of biosignals (e.g., heart rate information, temperature information) to the user terminal (2000) in the form of a data set in which the data are related over time.

[0073] The user terminal (2000) can perform a predetermined operation based on the biometric information received from the wearable device (1000).

[0074] For example, if the user's thyroid condition information is input through the terminal input unit 2100, the user terminal 2000 can transmit the user's thyroid condition information to the monitoring server 3000.

[0075] As another example, the user terminal (2000) may transmit the received biometric information to the monitoring server (3000) according to a predetermined condition. As a specific example, the user terminal (2000) may transmit the received biometric information to the monitoring server (3000) if the biometric information is received. As another specific example, the user terminal (2000) may store the received biometric information and transmit the stored biometric information to the monitoring server (3000) at a predetermined period.

[0076] The monitoring server 3000 can perform thyroid function monitoring based on the received biological information. The thyroid function monitoring method according to the present invention will be described in more detail below.

[0077] The monitoring server (3000) can transmit result information associated with thyroid function monitoring to the user terminal (2000). The user terminal (2000) can output information corresponding to the result information through the terminal output unit (2200) based on the result information received from the monitoring server (3000).

[0078] The monitoring server 3000 can transmit a warning based on the result of the thyroid function monitoring to the user terminal 2000. The user terminal 2000 can output a warning about the user's thyroid function through the terminal output unit 2200 based on the signal received from the monitoring server 3000.

[0079] Thus, a thyroid function monitoring system 100 according to one embodiment of the present invention has been generally described.

[0080] FIG. 1 shows a schematic diagram of a system in which a wearable device (1000) and a user terminal (2000) are communicatively connected, and a user terminal (2000) and a monitoring server (3000) are communicatively connected, but the connection relationship between each component may be modified.

[0081] In one example, the thyroid function monitoring system 100 can be implemented in a form in which the connection relationship between the user terminal 2000 and the monitoring server 3000 is switched so that the monitoring server 3000 communicates directly with the wearable device 1000, and the user terminal 2000 receives information via the monitoring server 3000. In another example, the monitoring server 3000 can be implemented in the form of a program installed in the user terminal 2000, so that the monitoring system 100 can be implemented in which only communication between the user terminal 2000 and the wearable device 1000 is performed. As another example, the wearable device (1000) may be embodied in a form in which the wearable device (1000) directly communicates with the monitoring server (3000) and the information received from the monitoring server (3000) is output to the user by the wearable device (1000), thereby realizing a monitoring system (100) in which only communication between the wearable device (1000) and the monitoring server (3000) is performed.

[0082] 1 shows a case where there is only one user terminal 2000, but the monitoring server 3000 may be implemented in a form connected to each of multiple user terminals 2000. One user may use the thyroid function monitoring system 100 using one user terminal 2000, one user may use multiple user terminals 2000, and multiple users may use the thyroid function monitoring system 100 using one user terminal 2000.

[0083] Hereinafter, the components of the thyroid function monitoring system 100 according to an embodiment of the present invention will be described in detail.

[0084] <Components of the Thyroid Function Monitoring System (100)> 1. Wearable Devices (1000) FIG. 2 is a block diagram of a wearable device (1000) according to an embodiment of the present invention.

[0085] As shown in Fig. 2, the wearable device 1000 may include a device input unit 1100, a device output unit 1200, a device communication unit 1300, a device sensor unit 1400, a device memory unit 1500, and a device control unit 1600. Of course, the components illustrated in Fig. 2 are not essential, and the wearable device 1000 may have more or fewer components.

[0086] The device input unit 1100 may perform a function of acquiring information from a user. The device input unit 1100 may receive user input from a user. The user input may be various forms of input, including, but not limited to, key input, touch input, and / or voice input.

[0087] The device input unit 1100 may be implemented by a commonly used user input device. For example, the device input unit 1100 may be implemented by various types of input means for detecting or receiving user inputs, such as a touch sensor for detecting a user's touch, a microphone for receiving an audio signal, a camera for recognizing gestures through image recognition, a proximity sensor including an illuminance sensor or an infrared sensor for detecting the user's approach, a motion sensor for recognizing a user's movement through an acceleration sensor or a gyro sensor, and / or other various types of input means.

[0088] Here, the term "touch sensor" refers to a piezoelectric or electrostatic touch sensor that senses a touch through a touch panel or touch film attached to a display panel, and / or an optical touch sensor that senses a touch by an optical method.

[0089] Alternatively, the device input unit (1100) may be implemented in the form of an input interface (USB port, PS / 2 port, etc.) that connects an external input device that receives user input to the wearable device (1000) instead of independently sensing user input.

[0090] Alternatively, the device input unit (1100) may include not only a means for sensing the user's intended input, but also an imaging device such as a camera that inputs data on the captured imaging area into the wearable device (1000).

[0091] The device output unit 1200 may perform a function of outputting information so that the user can check it. The device output unit 1200 may output information acquired from the user, information acquired from an external device, and / or processed information. The information output may be configured in various forms, including but not limited to visual, auditory, and / or tactile output.

[0092] The device output unit 1200 may be implemented as a display that outputs images, a speaker that outputs sounds, a haptic device that generates vibrations, and / or other various types of output means.

[0093] Here, the term "display" refers to a broad range of image display devices, including liquid crystal displays (LCDs), light emitting diode (LED) displays, organic light emitting diode (OLED) displays, flat panel displays (FPDs), transparent displays, curved displays, flexible displays, 3D displays, holographic displays, projectors, and / or any other various forms capable of performing an image output function.

[0094] Alternatively, the device output unit (1200) may be implemented in the form of an output interface (USB port, PS / 2 port, etc.) that connects an external output device that outputs information to the wearable device (1000) instead of an external device that independently outputs information.

[0095] The device output unit 1200 may be integrated with the device input unit 1100. For example, when the device output unit 1200 is a display, the device output unit 1200 is in the form of a touch display integrated with the device input unit 1100, which is an in-touch sensor.

[0096] The device communication unit 1300 can perform a function to allow the wearable device 1000 to transmit / receive data to / from an external device. According to an example of this embodiment, the device communication unit 1300 can communicate with the user terminal 2000 and / or the monitoring server 3000.

[0097] The device communication unit 1300 may include one or more modules capable of communication. The device communication unit 1300 may include a module that enables communication with an external device via a wired system. Alternatively, the device communication unit 1300 may include a module that enables communication with an external device via a wireless system. Alternatively, the device communication unit 1300 may include a module that enables communication with an external device via a wired system and a module that enables communication with an external device via a wireless system.

[0098] As a specific example, the device communication unit 1300 may be configured as a wired communication module connected to the Internet through a LAN (Local Area Network), a mobile communication module such as LTE (Long Term Evolution) that connects to a mobile communication network via a mobile communication base station and transmits / receives data, a short-range communication module using a WLAN (Wireless Local Area Network) series communication method such as Wi-Fi or a WPAN (Wireless Personal Area Network) series communication method such as Bluetooth or Zigbee, a satellite communication module using a GNSS (Global Navigation Satellite System) such as GPS (Global Positioning System), or a combination thereof.

[0099] The device sensor unit 1400 can perform a function of acquiring biometric information of the user of the wearable device 1000. According to an example of this embodiment, the device sensor unit 1400 can acquire heart rate information of the user.

[0100] The device sensor unit 1400 may include one or more modules capable of acquiring biometric information of a user. Specifically, the device sensor unit 1400 may include a PPG sensor module that acquires information about heartbeats (e.g., heart rate) using an optical method, an ECG sensor module that acquires information about heartbeats (e.g., electrocardiogram) using an electrical method, a temperature sensor module that acquires information about temperature using a contact / non-contact method, a motion sensor module that acquires information about user movement using an acceleration sensor, a gyro sensor, and / or a step detection sensor, an EDA sensor module that acquires information about sympathetic nervous system activity using skin conductance, or a combination thereof. Of course, the device sensor unit 1400 may be implemented with various sensors for acquiring biometric information of a user, without being limited thereto.

[0101] According to an embodiment of this embodiment, the device sensor unit 1400 may perform a function of acquiring information about the external environment of the wearable device 1000. As an example, the device sensor unit 1400 may include a temperature sensor module that measures the external temperature of the wearable device 1000.

[0102] The device memory unit 1500 can store various data and programs necessary for the wearable device 1000 to operate. The device memory unit 1500 can store information acquired by the wearable device 1000.

[0103] As one example, the device memory unit 1500 stores biometric information acquired by the device sensor unit 1400. As another example, the device memory unit 1500 may store an operating system (OS) for driving the wearable device 1000, various programs driven or used by the wearable device 1000 to perform thyroid function monitoring, and various data related to media referenced by these programs.

[0104] The device memory unit 1500 can store data temporarily or semi-permanently. Examples of the device memory unit 1500 include a hard disk drive (HDD), a solid state drive (Solid State Drive), a flash memory (1400), a read-only memory (ROM), a random access memory (RAM), or cloud storage. Of course, the device memory unit 1500 is not limited thereto and can be implemented with various modules for storing data.

[0105] The device memory unit (1500) can be provided in a form that is built into the wearable device (1000) or in a form that is detachable.

[0106] The device control unit 1600 can perform the function of controlling the overall operation of the wearable device 1000. The device control unit 1600 can perform calculations and processes of various information to control the operation of the components of the terminal.

[0107] The device controller 1600 can be realized by a computer or similar device using hardware, software, or a combination thereof. In terms of hardware, the device controller 1600 can be provided in the form of an electronic circuit such as a CPU chip that processes electrical signals and performs control functions, and in terms of software, it can be provided in the form of a program that drives the hardware device controller 1600.

[0108] According to an example, the device control unit 1600 can control the device sensor unit 1400 to sense the biosignal of the user.

[0109] According to one embodiment, the device control unit (1600) can control the device memory unit (1500) to temporarily store the sensed biosignal and delete the stored biosignal after bioinformation based on the biosignal is transferred through the device communication unit (1300).

[0110] In the following, unless otherwise specified, the operation of the wearable device (1000) can be interpreted as being performed under the control of the device control unit (1600).

[0111] The wearable device 1000 according to this embodiment may be a wearable wristband worn on a user's wrist to acquire biometric information, a wearable sock worn on a user's foot to acquire biometric information, a wearable ring worn on a user's finger to acquire biometric information, a wearable patch attached to a user's skin to acquire biometric information, a wearable hair band worn on a user's head to acquire biometric information, a wearable device worn on a user's ear as an earring or hung on an earphone, or a wearable lens inserted into a user's eye. Of course, the wearable device 1000 is not limited to the examples enumerated herein and may be embodied in various forms.

[0112] 2. User terminal (2000) FIG. 3 is a block diagram of a user terminal (2000) according to an embodiment of the present invention.

[0113] As shown in Figure 3, the user terminal (2000) includes a terminal input unit (2100), a terminal output unit (2200), a terminal communication unit (2300), a terminal memory unit (2400), and a terminal control unit (2500). However, the components shown in Figure 3 are not essential, and the user terminal (2000) may have more or fewer components.

[0114] The terminal input unit (2100) is similar to the device input unit (1100) of the wearable device (1000) described above and can perform a function of acquiring information from the user.

[0115] The terminal output unit 2200 can perform a function of outputting information for the user to check, similar to the device output unit 1200 of the wearable device 1000 described above.

[0116] The terminal communication unit 2300 can perform a function of transmitting / receiving data to / from an external device, similar to the device communication unit 1300 of the wearable device 1000 described above.

[0117] The terminal memory unit (2400) is similar to the device memory unit (1500) of the wearable device (1000) described above and can store various data and programs necessary for the user terminal (2000) to operate.

[0118] The terminal control unit (2500) can perform the function of controlling the overall operation of the user terminal (2000) in a manner similar to the device control unit (1600) of the wearable device (1000) described above.

[0119] According to the embodiment, the terminal control unit 2500 processes information based on the user's input input through the terminal input unit 2100, and controls the processed information to be transmitted to the monitoring server 3000 through the terminal communication unit 2300. Specifically, the terminal control unit 2500 acquires thyroid status information and / or medication information through the terminal input unit 2100, processes the corresponding information to fit the communication format with the monitoring server 3000, and transmits the information through the terminal communication unit 2300.

[0120] According to another embodiment, the terminal control unit 2500 processes information received from the monitoring server 3000 through the terminal communication unit 2300 and provides the information to the user through the terminal output unit 2200. Specifically, the terminal control unit 2500 receives information related to the result of thyroid function monitoring through the terminal communication unit 2300, and can output the result of the determination of thyroid function abnormality through the terminal output unit 2200 to warn the user of the thyroid function.

[0121] In the following, unless otherwise specified, the operation of the user terminal (2000) can be interpreted as being executed under the control of the terminal control unit (2500).

[0122] Therefore, explanations of overlapping modules in the terminal input unit (2100), terminal output unit (2200), terminal communication unit (2300), terminal memory unit (2400) and terminal control unit (2500) will be omitted.

[0123] The user terminal (2000) according to the embodiment of this invention may include not only mobile terminals such as mobile phones, smartphones, tablet PCs, notebooks, PDAs (Personal Digital Assistants), PMPs (Portable Multimedia Players), navigation systems, etc., but also fixed terminals such as digital TVs, desktop computers, kiosks, etc. More generally, anything that can connect to other electronic devices and / or servers via a network and exchange information can be the user terminal (2000) in this specification.

[0124] 3. Monitoring Server (3000) FIG. 4 is a block diagram of the monitoring server (3000) according to this embodiment.

[0125] As shown in Fig. 4, the monitoring server (3000) may include a server communication unit (3100), a server database (3200), and a server control unit (3300). However, the components shown in Fig. 4 are not essential, and the monitoring server (3000) may include more or fewer components. Furthermore, each component of the monitoring server (3000) may be physically included in a single server, or may be a distributed server distributed according to its respective function.

[0126] The server communication unit 3100 is similar to the device communication unit 1300 of the wearable device 1000 described above and can perform the function of transmitting / receiving data to / from an external device. Therefore, a description of the overlapping modules of the server communication unit 3100 will be omitted.

[0127] According to an embodiment of this embodiment, the server communication unit 3100 can receive information regarding biometric information of the user of the wearable device 1000 from the user terminal 2000. According to another embodiment of this embodiment, the server communication unit 3100 can receive thyroid status information of the user acquired through the user terminal 2000.

[0128] The server database (3200) is similar to the device memory unit (1500) of the wearable device (1000) described above, and can store various data and programs necessary for the operation of the monitoring server (3000). Therefore, a description of the duplicated modules of the server database (3200) will be omitted.

[0129] According to an example embodiment of this embodiment, the server database (3200) may store the monitoring algorithm, user information, and / or user biometric information utilized in predicting thyroid function.

[0130] The server control unit 3300 is similar to the device control unit 1600 of the wearable device 1000 described above, and can perform the function of controlling the overall operation of the monitoring server 3000. Therefore, a description of the overlapping modules of the server control unit 3300 will be omitted.

[0131] According to this embodiment, the server control unit (3300) can predict the user's thyroid function based on the user's biological information received through the server communication unit (3100) using the monitoring algorithm stored in the server database (3200). Specifically, the server control unit (3300) can calculate reference data based on the biological information and thyroid status information received through the server communication unit (3100). In addition, the server control unit (3300) can calculate monitoring data based on the biological information received through the server communication unit (3100).

[0132] According to another embodiment, the server control unit 3300 may select a specific monitoring algorithm from among the monitoring algorithms stored in the server database 3200 based on the medication information received through the server communication unit 3100. The server control unit 3300 may perform thyroid function monitoring based on the selected monitoring algorithm.

[0133] In the following, unless otherwise specified, it can be understood that the operation of the monitoring server (3000) is performed under the control of the server control unit (3300).

[0134] The monitoring server (3000) according to an example of this embodiment may include computer hardware on which a thyroid function monitoring program runs or other programs, and / or a computer program that provides services to electronic devices.

[0135] The monitoring server (3000) according to the embodiment of the present invention manages or controls a network to which external terminals and servers are connected, and can share software resources such as data used for thyroid function monitoring. The monitoring server (3000) may be a single physical server, or may be a distributed server in which multiple servers distribute their processing loads and roles.

[0136] Hereinafter, the operation of the thyroid function monitoring system 100 according to the embodiment of the present invention will be described in detail.

[0137] In the detailed description of the operation of the thyroid function monitoring system (100), unless otherwise specified, the thyroid function monitoring system (100) includes a wearable device (1000), a user terminal (2000) and a monitoring server (3000), and the wearable device (1000) is a wearable watch, the user terminal (2000) is a smartphone with a display, and the monitoring server (3000) is a single server.

[0138] However, this is merely a specific description based on one embodiment for the convenience of explanation, and the scope of the present invention is not limited to the embodiment described in this specification, but is determined by the principles of interpretation of the claims.

[0139] <Operation of the Thyroid Function Monitoring System (100)> 1. Operation of Thyroid Dysfunction Monitoring (S100) 1-1 Thyroid Dysfunction Monitoring (S100) The thyroid function monitoring system 100 of this embodiment can predict thyroid dysfunction of a user based on the user's biological signals. According to an example of this embodiment, the thyroid function monitoring system 100 can determine thyroid dysfunction based on the user's heart rate information.

[0140] Here, "thyroid dysfunction" refers to the onset of any of the following conditions: hyperthyroidism, hypothyroidism, and thyrotoxicosis.

[0141] Here, "predicting thyroid dysfunction" means obtaining the results of thyroid dysfunction monitoring based on the user's biometric information, and in this specification, the term is sometimes used interchangeably with "judging thyroid dysfunction" or "diagnosing thyroid dysfunction."

[0142] FIG. 5 is a flowchart for explaining the thyroid function monitoring method (S100) according to an example of this embodiment.

[0143] 5, once biological information is acquired (S1100), monitoring data is calculated (S1300) and the user's thyroid dysfunction can be determined (S1500). According to an embodiment, the above steps S1100, S1300 and S1500 can be executed by the monitoring server (3000).

[0144] 1-1.1 Acquisition of Biometric Information (S1100) The wearable device 1000 can acquire biometric information of a user. The wearable device 1000 can acquire biometric information of a user wearing the wearable device 1000.

[0145] The wearable device (1000) can acquire the user's biological information at regular intervals.

[0146] For example, the device sensor unit 1400 of the wearable device 1000 may include a PPG sensor, and the wearable device 1000 may acquire the user's heart rate information in a first cycle using the PPG sensor. The device sensor unit 1400 of the wearable device 1000 may include a motion sensor, and the wearable device 1000 may acquire the user's motion information in a second cycle using the motion sensor. The first cycle and the second cycle may be the same. Of course, the first cycle and the second cycle may be different.

[0147] The wearable device 1000 can transmit the user's biometric information to the user terminal 2000. As an example, the wearable device 1000 can acquire the user's biometric information and simultaneously transmit it to the user terminal 2000. As another example, the wearable device 1000 can transmit a set of acquired user's biometric information to the user terminal 2000 at a predetermined interval. In this case, the interval at which the wearable device 1000 acquires the user's biometric information may be shorter than the interval at which the wearable device 1000 transmits the user's biometric information.

[0148] The wearable device 1000 can transmit one or more types of user biometric information to the user terminal 2000. As an example, the biometric information transmitted to the user terminal 2000 can be heart rate information. As another example, the biometric information transmitted to the user terminal 2000 can be heart rate information and exercise information.

[0149] The biological information transmitted by the wearable device 1000 may be associated with other information. As one example, the biological information transmitted by the wearable device 1000 may be time-related heart rate information. As another example, the biological information transmitted by the wearable device 1000 may be time-related heart rate information and time-related movement information. As yet another example, the biological information transmitted by the wearable device 1000 may be in a form related to time, heart rate information, and movement information.

[0150] The user terminal (2000) can transmit the received biometric information to the monitoring server (3000). As an example, the user terminal (2000) can receive the user's biometric information and simultaneously transmit it to the monitoring server (3000). As another example, the user terminal (2000) can transmit a set of received biometric information of a plurality of users to the monitoring server (3000) at a predetermined interval. In this case, the interval at which the user terminal (2000) acquires the set of biometric information may be shorter than the interval at which the set of biometric information is transmitted.

[0151] The monitoring server 3000 can acquire (S1100) biometric information from the user terminal 2000. The server communication unit 3100 can acquire (S1100) biometric information from the user terminal 2000. The monitoring server 3000 can receive biometric information sensed by the wearable device 1000 transformed into an appropriate shape through the user terminal 2000.

[0152] 1-1-2 Calculation of monitoring data (S1300) The monitoring server (3000) can calculate monitoring data based on the acquired biological information (S1300). The server control unit (3300) can calculate monitoring data (S1300).

[0153] Here, the "monitoring data" is the user's condition data for the monitoring period of the subject of thyroid function assessment in one thyroid function abnormality assessment operation (S1500).

[0154] As one example, the "monitoring data" may be calculated based on the user's condition data for a monitoring period (e.g., a number of consecutive days prior to the time of determining thyroid dysfunction (S1500)). As another example, the "monitoring data" may be calculated based on the user's condition data for one or more intervals (e.g., rest intervals) that satisfy a predetermined condition during the monitoring period.

[0155] According to an example embodiment of this embodiment, the monitoring data may be calculated based on the user's state data when the user is in a stable state during the monitoring period. According to another example embodiment of this embodiment, the monitoring data may be calculated based on the user's state data when the user is in a resting state during the monitoring period.

[0156] FIG. 6 is a flowchart for explaining the method for calculating monitoring data (S1300) according to an example of this embodiment.

[0157] The monitoring data calculation method (S1300) may include checking the pause period (S1310), extracting biological information corresponding to the pause period (S1330), and calculating the monitoring data (S1350). According to one example, the above steps S1310, S1330, and S1350 may be performed by the monitoring server (3000).

[0158] The monitoring server 3000 may check for a pause period (S1310). The server control unit 3300 may check for a pause period (S1310). The monitoring server 3000 may check for at least one pause period during a predetermined period (e.g., a monitoring period) (S1310). As an example, the monitoring period may be one day (24 hours). As another example, the predetermined period may be multiple days (e.g., 5 days). As another example, the monitoring period may be shorter than one day.

[0159] The monitoring server 3000 may check for a pause period corresponding to a predetermined condition during a predetermined period (S1310). For example, the monitoring server 3000 may check for a pause period based on the user's exercise information during a monitoring period (S1310).

[0160] The predetermined condition for determining whether the sleep period is present may be related to the user's motion information. Specifically, for example, the sleep period may be determined as a period in which it is determined that the user is motionless for a predetermined period of time (e.g., 5 minutes) based on the user's motion information. As another example, the predetermined condition may be related to the user's skin conductance information. Specifically, for example, the sleep period may be determined as a period in which it is determined that the user is asleep based on the user's skin conductance information.

[0161] There may be a plurality of pause intervals confirmed in step S1310. The monitoring period may include a plurality of pause intervals. The plurality of pause intervals may be discontinuous with one another. For example, a period in which user movement is detected may exist between one pause interval and another pause interval. In other words, the monitoring period may include one pause interval, another pause interval, and a period in which user movement is detected.

[0162] The monitoring server (3000) may extract (S1330) biometric information corresponding to the pause interval. The server control unit (3300) may extract (S1330) biometric information corresponding to the pause interval. The monitoring server (3000) may extract (S1330) biometric information corresponding to one or more confirmed pause intervals. The monitoring server (3000) may extract (S1330) biometric information corresponding to one or more confirmed pause intervals included in the monitoring period.

[0163] The monitoring server (3000) can extract heart rate information corresponding to the confirmed pause intervals included in the monitoring period, or temperature information corresponding to the confirmed pause intervals included in the monitoring period, or skin conductance information corresponding to the confirmed pause intervals included in the monitoring period.

[0164] According to an example of this embodiment, the monitoring server (3000) can extract multiple pieces of heart rate information corresponding to each of multiple confirmed pause periods included in the monitoring period.

[0165] The monitoring server (3000) can calculate the monitoring data (S1350). The server control unit (3300) can calculate the monitoring data (S1350). The monitoring server (3000) can calculate the monitoring data based on the extracted biological information (S1350).

[0166] In one example, the monitoring server (3000) can calculate, into the monitoring data, an average value of a plurality of pieces of heart rate information corresponding to each of a plurality of confirmed pause sections included in the monitoring period. In another example, the monitoring server (3000) can calculate, into the monitoring data, a median value of median values ​​of a plurality of pieces of heart rate information corresponding to each of a plurality of pause sections included in the monitoring period. In yet another example, the monitoring server (3000) can calculate, into the monitoring data, a calculated value of the remaining heart rate information, excluding the maximum and minimum values, from the plurality of pieces of heart rate information corresponding to each of a plurality of pause sections included in the monitoring period.

[0167] 1-1.3 Calculation of reference data (S200) 1-1.3.1 Reference data The monitoring server (3000) according to the embodiment of this invention can use reference data. Here, "reference data" refers to reference data that is compared with monitoring data when performing a thyroid dysfunction determination operation.

[0168] According to one embodiment of the present invention, the reference data may be determined based on the user's biometric information when the user's thyroid function status information corresponds to a normal state. According to another embodiment of the present invention, the reference data may be determined based on the user's biometric information in a corresponding period when the user's biometric information corresponds to a predetermined condition.

[0169] 1-1.3.2 Calculation method of reference data FIG. 7 is a flowchart for explaining the method of calculating the reference data (S200) according to the embodiment of the present invention.

[0170] Referring to FIG. 7, when the monitoring server 3000 receives thyroid status information (S2100), it can calculate reference data (S2300).

[0171] The monitoring server 3000 can receive thyroid status information from the user terminal 2000 (S2100). The server communication unit 3100 can receive thyroid status information from the terminal communication unit 2300 (S2100).

[0172] According to an example of this embodiment, the user terminal 2000 can receive thyroid status information of the user through the terminal input unit 2100. At this time, the thyroid status information is information about thyroid hormone levels obtained by a blood test or the like of the user. Alternatively, the thyroid status information is information about the thyroid status obtained by questioning the user about his or her symptoms.

[0173] The user terminal (2000) can transmit the input thyroid status information to the monitoring server (3000).

[0174] When the thyroid status information is received (S2100), the monitoring server (3000) can calculate reference data (S2300). When the thyroid status information is received, the server control unit (3300) can calculate reference data (S2300).

[0175] FIG. 8 is a flowchart for explaining the method of calculating the reference data (S2300) according to the embodiment of the present invention.

[0176] The reference data calculation method (S2300) includes the steps of determining a calculation period for the reference data (S2310), checking a pause period during the calculation period for the reference data (S2320), extracting biometric information corresponding to the pause period (S2330), and calculating the reference data (S2360). According to one example, steps S2310, S2320, S2330, and S2360 can be performed by the monitoring server (3000).

[0177] The monitoring server (3000) can determine a calculation period for the reference data (S2310). The server control unit (3300) can determine a calculation period for the reference data (S2310). The server control unit (3300) can determine a calculation period for the reference data (S2310) based on information stored in the server database (3200).

[0178] The monitoring server (3000) may determine a calculation period for the reference data based on the received thyroid status information (S2310). According to this specification, the calculation period for the reference data may be expressed interchangeably as a reference period or a reference period.

[0179] The calculation period for the reference data is the period during which the thyroid function of the user corresponds to "normal" according to the thyroid status information.

[0180] As an example, if the input thyroid status information corresponds to the normal range, a predetermined period before and after the time the thyroid status information is input can be determined as the calculation period for the reference data.

[0181] As another example, when multiple pieces of thyroid status information are input, a predetermined period before and after the time when thyroid status information corresponding to normal is input can be determined as the calculation period for the reference data. Specifically, for example, if thyroid status information relating to normal thyroid hormone levels is input on March 1, thyroid status information relating to abnormal thyroid hormone levels is input on June 1, and thyroid status information relating to normal thyroid hormone levels is input on September 1, a predetermined period based on March 1 (e.g., a five-day period from February 27 to March 3) and a predetermined period based on September 1 (e.g., a five-day period from August 30 to September 3) can be determined as the calculation period for the reference data.

[0182] The monitoring server 3000 may check the pause interval corresponding to the determined calculation period of the reference data (S2320). The server control unit 3300 may check the pause interval corresponding to the determined calculation period of the reference data (S2320). The server control unit 3300 may check the pause interval corresponding to the determined calculation period of the reference data based on the conditions stored in the server database 3200 (S2320).

[0183] The "pause period in step S2320" may be determined based on a condition corresponding to a condition for checking the pause period in the calculation of the monitoring data. For example, if the pause period in step S1310 is determined to be a period in which it is determined that a predetermined time (e.g., 5 minutes) has elapsed without the user's movement, the pause period in step S2320 may also be determined to be a period in which it is determined that a predetermined time (e.g., 5 minutes) has elapsed without the user's movement.

[0184] There may be a plurality of pause intervals confirmed in step S2320. The calculation period for the reference data may include a plurality of pause intervals. The plurality of pause intervals may be discontinuous with one another. For example, a period in which user movement is detected may exist between one pause interval and another pause interval. In other words, the calculation period for the reference data may include one pause interval, another pause interval, and a period in which user movement is detected.

[0185] The monitoring server (3000) may extract (S2330) biometric information corresponding to the pause interval. The server control unit (3300) may extract (S2330) biometric information corresponding to the pause interval. The monitoring server (3000) may extract (S2330) biometric information corresponding to one or more confirmed pause intervals. The monitoring server (3000) may extract (S2330) biometric information corresponding to one or more confirmed pause intervals included in the calculation period of the reference data.

[0186] The monitoring server (3000) may extract heart rate information corresponding to the confirmed pause interval included in the calculation period of the reference data, or temperature information corresponding to the confirmed pause interval included in the calculation period of the reference data, or skin conductance information corresponding to the confirmed pause interval included in the calculation period of the reference data.

[0187] According to an example of this embodiment, the monitoring server (3000) can extract multiple pieces of heart rate information corresponding to each of multiple confirmed pause periods included in the calculation period of the reference data.

[0188] The "biological information in S2330" may correspond to biological information extracted by calculating the monitoring data. For example, if heart rate is extracted as biological information in S1330, the heart rate may be extracted as biological information in S2330. For another example, if skin conductance is extracted as biological information in S1330, the skin conductance may be extracted as biological information in S2330.

[0189] The monitoring server (3000) can calculate the reference data (S2360). The server control unit (3300) can calculate the reference data (S2360). The monitoring server (3000) can calculate the reference data based on the extracted biometric information (S2360).

[0190] As one example, the monitoring server (3000) may calculate, as the reference data, an average value of a plurality of pieces of heart rate information corresponding to each of a plurality of confirmed pause periods included in the calculation period of the reference data. As another example, the monitoring server (3000) may calculate, as the reference data, a median value of median values ​​of a plurality of pieces of heart rate information corresponding to each of a plurality of pause periods included in the calculation period of the reference data. As yet another example, the monitoring server (3000) may calculate, as the reference data, a calculated value of the remaining heart rate information, excluding the maximum and minimum values, from the plurality of pieces of heart rate information corresponding to each of a plurality of pause periods included in the calculation period of the reference data.

[0191] FIG. 9 is a diagram for explaining a method for calculating reference data when the monitoring server (3000) receives thyroid status information that is outside the normal range according to an example of this embodiment.

[0192] The monitoring server (3000) can determine a calculation period for reference data (S2310), check for a pause during the calculation period for reference data (S2320), and extract biometric information for the confirmed pause period (S2330).

[0193] In step S2310, the calculation period of the reference data is the period during which the user's thyroid function corresponds to "normal" according to the received thyroid status information, as described above.

[0194] In step S2310, the calculation period for the reference data is the period when the monitoring server (3000) is unable to receive the user's thyroid status information within the normal range (i.e., when the thyroid status information received by the monitoring server (3000) is outside the normal range), and the received thyroid status information indicates that the user's thyroid function is "abnormal."

[0195] For example, if the input thyroid status information does not correspond to the normal range, a predetermined period before and after the time point at which the thyroid status information is input may be determined as the calculation period for the reference data.

[0196] The monitoring server 3000 can check the pause period corresponding to the calculation period of the predetermined reference data (S2320) and extract the biometric information corresponding to the pause period (S2330). The steps S2320 and S2330 have already been described in detail above, so a repeated description will be omitted.

[0197] The monitoring server 3000 can calculate reference period data (S2340). The server control unit 3300 can calculate reference period data (S2340). The monitoring server 3000 can calculate the reference period data based on biological information corresponding to a pause period included in the calculation period of the reference data.

[0198] As one example, the monitoring server (3000) may calculate, as the reference time data, an average value of a plurality of pieces of heart rate information corresponding to each of a plurality of confirmed pause periods included in the calculation period of the reference data. As another example, the monitoring server (3000) may calculate, as the reference period data, a median value of median values ​​of a plurality of pieces of heart rate information corresponding to each of a plurality of pause periods included in the calculation period of the reference data. As yet another example, the monitoring server (3000) may calculate, as the reference period data, a calculated value of the remaining heart rate information, excluding the maximum and minimum values, from the plurality of pieces of heart rate information corresponding to each of a plurality of pause periods included in the calculation period of the reference data.

[0199] At this time, the reference period data calculated in step S2340 is data calculated using biological information when the user's thyroid function is estimated to be "abnormal."

[0200] The monitoring server (3000) can calculate the reference data (S2350). The server control unit (3300) can calculate the reference data (S2350). The monitoring server (3000) can calculate the reference data based on the received thyroid status information (S2350). The monitoring server (3000) can calculate the reference data by correcting the reference period data based on the received thyroid status information (S2350).

[0201] For example, the monitoring server (3000) can calculate how much ng / dL the user's hormone levels should increase / decrease to fall within the normal range according to the received thyroid status information, and estimate the amount of change in biological information due to the increase / decrease. The monitoring server (3000) can calculate reference data by adding or subtracting the estimated amount of change in biological information from reference period data (S2350).

[0202] The monitoring server (3000) may store data necessary for correcting the reference period data. For example, the monitoring server (3000) may store data relating to the relationship between hormone levels and resting heart rates of multiple users. As a specific example, the monitoring server (3000) may store statistical data on the approximate increase in heart rate when hormone levels increase by 0.1 ng / dL.

[0203] As described above, the method by which the monitoring server (3000) calculates the reference data when receiving the thyroid status information from the user terminal (2000) has been described in detail.

[0204] According to an embodiment of the present invention, if there is no stored thyroid status information of a user, the monitoring server (3000) can transmit a signal to the user terminal (2000) to receive input of thyroid status information through the user terminal (2000). As an example, the monitoring server (3000) can transmit a necessary signal to the user terminal (2000) so that an input interface for receiving input of thyroid status information is output through the terminal output unit (2200) of the user terminal (2000).

[0205] According to an example of this embodiment, if there is no stored thyroid status information of the user, the monitoring server (3000) may calculate reference data based on the monitoring data calculated in step S1300. Specifically, for example, the monitoring server (3000) may calculate reference data based on the user's heart rate acquired over a predetermined number of days. The monitoring server (3000) may calculate reference data based on each user's heart rate during a plurality of pause periods acquired over a predetermined number of days. In this case, the predetermined number of days is a period longer than the monitoring period.

[0206] 1-1.3.3 Calculation period of reference data According to an embodiment of this embodiment, the reference data may be calculated by being triggered by receiving thyroid status information. According to another embodiment of this embodiment, the reference data may be calculated at a predetermined interval in the monitoring server 3000. According to yet another embodiment of this embodiment, the reference data may be calculated when the monitoring server 3000 receives a signal for calculating the reference data from an external device (e.g., the user terminal 2000).

[0207] According to another embodiment of the present invention, the reference data may be calculated for each calculation period of the monitoring data. In other words, the monitoring server (3000) may calculate the monitoring data and then newly calculate the reference data. Alternatively, the monitoring server (3000) may calculate the reference data before calculating the monitoring data and then compare the two data.

[0208] 1-1.4 Determining Thyroid Dysfunction (S1500) Continuing with FIG. 5, thyroid function monitoring (S100) according to an example of this embodiment can be performed through obtaining biological information (S1100), calculating monitoring data (S1300), and determining thyroid dysfunction (S1500).

[0209] FIG. 10 is a flowchart for explaining a method for determining thyroid dysfunction (S1500) according to an example of this embodiment.

[0210] 10, the monitoring server 3000 may calculate monitoring data (S1300) and then compare the monitoring data with reference data (S1510). The server control unit 3300 may compare the monitoring data with the reference data (S1510).

[0211] The algorithm for comparing the reference data with the monitoring data can be designed in various ways. The algorithm for comparing the reference data with the monitoring data can be stored in the server database (3200) of the monitoring server (3000).

[0212] As one example, the algorithm for comparing the reference data with the monitoring data is designed to determine whether or not there is a thyroid dysfunction (S1530) if the monitoring data is greater than the reference data by a predetermined numerical range or more. As another example, the algorithm for comparing the reference data with the monitoring data is designed to determine whether or not there is a thyroid dysfunction (S1530) if the monitoring data is less than the reference data by a predetermined numerical range or more. As yet another example, the algorithm for comparing the reference data with the monitoring data is designed to determine whether or not there is a thyroid dysfunction (S1530) if the monitoring data is greater than or less than the reference data by a predetermined numerical range or more.

[0213] If the reference data and the monitoring data are compared in step S1510 and it is determined that the data matches the preset conditions, the monitoring server (3000) can determine that the user has an abnormality in thyroid function (S1530). The server control unit (3300) can compare the reference data and the monitoring data (S1510) and determine that the data matches the preset conditions and that the user has an abnormality in thyroid function (S1530).

[0214] If it is determined in step S1530 that the user has an abnormal thyroid function, the monitoring server 3000 may transmit a signal to the user terminal 2000 so that a warning regarding the user's thyroid abnormality is output through the terminal output unit 2200 of the user terminal 2000. As an example, the user terminal 2000 may control the terminal output unit 2200 to output a warning regarding the user's hyperthyroidism in response to the signal received from the monitoring server 3000. As another example, the user terminal 2000 may control the terminal output unit 2200 to output a warning regarding the user's hypothyroidism in response to the signal received from the monitoring server 3000. As yet another example, the user terminal 2000 may control the terminal output unit 2200 to output a warning regarding the user's thyrotoxicosis in response to the signal received from the monitoring server 3000. As another example, if the user terminal (2000) receives a signal corresponding to S1530 from the monitoring server (3000) more than a predetermined number of times within a certain period of time, the user terminal (2000) can control the terminal output unit (2200) to output a warning regarding the user's thyrotoxicosis including a comment suggesting that the user seek an expert's opinion.

[0215] The warning regarding the abnormal thyroid function may be a warning as to whether or not a disease has occurred, a warning as to the possibility (or risk) of developing a disease, or a guidance to visit a hospital.

[0216] The warning regarding the abnormal thyroid function may be a visual output such as a display panel, or an audible output such as a speaker, but is not limited to these.

[0217] FIG. 11 is a flowchart illustrating an algorithm for comparing reference data and monitoring data according to an example of this embodiment.

[0218] The monitoring server 3000 can compare the monitoring data with the reference data and determine whether the monitoring data is greater than the reference data by a first threshold value or more (S1511).

[0219] The monitoring server (3000) may determine that the user has an abnormal thyroid function if the monitoring data is greater than the reference data by a first threshold value or more. For example, if the monitored heart rate is greater than the reference heart rate by a first threshold value (e.g., 10 beats) or more, the monitoring server (3000) may determine that the user has an abnormal thyroid function. The monitoring server (3000) may predict (S1531) the user's hyperthyroidism. The monitoring server (3000) may diagnose (S1531) the user's hyperthyroidism. The monitoring server (3000) may determine (S1531) the user's hyperthyroidism.

[0220] If it is determined in step S1531 that the user has an abnormality in thyroid function, the monitoring server (3000) can transmit a signal to the user terminal (2000) so that a warning regarding the user's thyroid abnormality is output through the terminal output unit (2200) of the user terminal (2000).

[0221] If the monitoring data is not greater than the reference data by the first threshold or more, the monitoring server 3000 may compare the monitoring data with the reference data to determine whether the monitoring data is smaller than the reference data by the second threshold or more (S1513).

[0222] The monitoring server (3000) may determine that the user has an abnormal thyroid function if the monitoring data is smaller than the reference data by a second critical value or more. As an example, the monitoring server (3000) may determine that the user has an abnormal thyroid function if the monitored heart rate is smaller than the reference heart rate by a second critical value (e.g., 8 beats) or more. The monitoring server (3000) may predict (S1531) the user's hypothyroidism. The monitoring server (3000) may diagnose (S1531) the user's hypothyroidism. The monitoring server (3000) may determine (S1531) the user's hypothyroidism.

[0223] If it is determined in step S1533 that the user has an abnormality in thyroid function, the monitoring server (3000) can transmit a signal to the user terminal (2000) so that a warning regarding the user's thyroid abnormality is output through the terminal output unit (2200) of the user terminal (2000).

[0224] 12 to 16 are diagrams showing the results of clinical research into the correlation between hypothyroidism and heart rate, based on the fact that the monitoring system according to the embodiment of this invention predicts thyroid dysfunction.

[0225] In this clinical study, to confirm the relationship between hypothyroidism and heart rate, 44 post-thyroidectomy hypothyroid patients (hereafter referred to as clinicians) were recruited and had their heart rate continuously monitored by wearing a wearable device. In this clinical study, the wearable device was a Fitbit Charge 2. TM The clinicians wearing the wearable device visited the clinic three times and compared changes in thyroid hormone levels during thyroid hormone treatment with changes in heart rate measured by the wearable device as thyroid hormone therapy was discontinued and maintained.

[0226] Specifically, patients in this clinical study were divided into two groups. Group 1 consisted of 30 clinicians. Patients in Group 1 visited the clinic three times. They were taking thyroid hormone medication before the first visit, after the second visit, and until the third visit. They were instructed not to take thyroid hormone medication between the first and second visits and one month before the second visit. Patients in Group 1 were euthyroid at the first and third visits and had hypothyroidism at the second visit. This group was classified as the hypothyroidism group. Group 2 consisted of 14 clinicians. Patients in Group 2 also visited the clinic three times. They were instructed to take thyroid hormone medication continuously from before the first visit until the third visit. This group was instructed to take thyroid hormone medication continuously from before the first visit until the third visit. This group was instructed to have euthyroid function at the first, second, and third visits. This group was classified as the control group.

[0227] Figure 12 shows the characteristics of the clinicians who participated in the clinical research process by clinical group. In Figure 12, Age is age, Gender is sex, Body mass index is body mass index, Systolic blood pressure is systolic blood pressure, Diastolic blood pressure is diastolic blood pressure, On-site resting heart rate is the heart rate measured with an automatic blood pressure monitor at the patient's visit, Thyroid function test is the thyroid hormone concentration measured at the patient's visit, Free T4 is thyroid hormone, TSH is thyroid-stimulating hormone, Glucose is glucose, BUN is blood element, Creatinine is creatinine, Total cholesterol is total cholesterol, Total protein is total protein, Albumin is albumin, AST is aspartate transaminase, ALT is alcohol transaminase, WBC is white blood cell, Hemoglobin is hemoglobin, and Platelet is platelet. In FIG. 12, the values ​​shown for each characteristic represent the mean and standard deviation of the values ​​corresponding to each characteristic of a large number of clinicians belonging to the clinical group, and are expressed as (mean)±(standard deviation).

[0228] Figure 13 shows the changes in thyroid function parameters between the first and second visits for clinicians who participated in the clinical research process. In Figure 13, free T4 is the thyroid hormone concentration, TSH is the thyroid-stimulating hormone concentration, Zulewski's clinical score is Zulewski's clinical score for hypothyroidism, On-site rHR is the resting heart rate measured palliatively after 15 minutes of rest at the time of the visit, WD-rHR is the average resting heart rate measured by a wearable device over the five days prior to the visit, WD-sleepHR is the average sleep heart rate measured by a wearable device over the five days prior to the visit, and WD-2to6HR is the average heart rate measured by a wearable device between 2:00 AM and 6:00 AM over the five days prior to the visit.

[0229] In Figure 13, Visit 1 or 3 represents the value of each characteristic based on the data for the first visit of clinicians belonging to each clinical group. If data for the first visit of each clinician was missing, the data for the first visit was replaced with the data for the third visit of that clinician for analysis. In Figure 13, Visit 2 represents the value of each characteristic based on the data for the second visit of clinicians belonging to each clinical group.

[0230] In Figure 13, in the hypothyroidism group, the thyroid hormone (free T4) values ​​measured at the second visit compared to the first visit fell below the normal range (0.8-1.8 ng / dL), indicating that clinicians belonging to this clinical group were in a state of hypothyroidism at the second visit. In Figure 13, in the control group, the thyroid hormone (free T4) values ​​measured at the first and second visits were all within the normal range (0.8-1.8 ng / dL), indicating that clinicians belonging to this clinical group were in a state of euthyroidism at the first and second visits. In Figure 13, the thyroid hormone (free T4) levels in the control group were all within the normal range at Visit 1 or 3 and Visit 2, but there was a statistically significant decrease in the free T4 thyroid hormone levels at Visit 2 compared to Visit 1. On-site rHR was unable to reflect the significant decrease in the thyroid hormone (free T4) levels in the control group, but the parameters measured by the wearable device (i.e., WD-rHR, WD-sleepHR, WD-2to6HR) were found to be relatively more sensitive indicators that reflected the significant decrease in the thyroid hormone (free T4) levels in the control group.

[0231] Figure 14 shows the results of an analysis of the relationship between free T4 thyroid hormone concentrations and heart rate parameters based on the changes in thyroid function parameters shown in Figure 13. In Figure 14, the unstandardized beta for On-site rHR refers to the relationship between the resting heart rate measured after 15 minutes of rest at the time of the visit and the free T4 thyroid hormone concentration. The unstandardized beta for WD-rHR refers to the relationship between the average resting heart rate measured with a wearable device over the five days prior to the visit and the free T4 thyroid hormone concentration. The unstandardized beta for WD-sleepHR refers to the relationship between the average sleep heart rate measured with a wearable device over the five days prior to the visit and the free T4 thyroid hormone concentration. The unstandardized beta for WD-2to6HR refers to the relationship between the average heart rate measured with a wearable device over the five days prior to the visit and the free T4 thyroid hormone concentration.

[0232] Figure 14 confirms that there is a greater correlation between some parameters calculated using heart rate obtained by a wearable device and free T4 thyroid hormone concentration than there is between resting heart rate at the time of admission to the hospital and free T4 thyroid hormone concentration.

[0233] Figure 15 shows the analysis results of the correlation between hypothyroidism and heart rate parameters based on the changes in thyroid function parameters shown in Figure 13. Here, hypothyroidism refers to the result of a doctor's diagnosis of thyroid dysfunction based on hormone levels measured at the time of the patient's visit. In Figure 15, the unstandardized beta of On-site rHR refers to the correlation between the resting heart rate measured after 15 minutes of rest at the time of the patient's visit and the diagnosis of hypothyroidism. The unstandardized beta of WD-rHR refers to the correlation between the average resting heart rate measured by the wearable device over the five days prior to the patient's visit and the diagnosis of hypothyroidism. The unstandardized beta of WD-sleepHR refers to the correlation between the average sleep heart rate measured by the wearable device over the five days prior to the patient's visit and the diagnosis of hypothyroidism. The unstandardized beta of WD-2to6HR refers to the correlation between the average heart rate measured by the wearable device over the five days prior to the patient's visit and the diagnosis of hypothyroidism.

[0234] Figure 15 confirms that there is a greater correlation between some parameters calculated using heart rate obtained by a wearable device and the diagnosis of hypothyroidism than there is between the resting beat rate at the time of hospital visit and the diagnosis of hypothyroidism.

[0235] 14 and 15, it was confirmed that the correlation between parameters calculated using heart rate obtained by a wearable device and hypothyroidism is greater than the correlation between resting heart rate at the time of hospital visit and hypothyroidism. This is because, due to the characteristics of wearable devices, when predicting thyroid dysfunction based on heart rate information obtained through a wearable device, it is possible to predict thyroid dysfunction based on relatively "long-term" heart rate information obtained during daily life, rather than predicting thyroid dysfunction based on "short-term" heart rate information at the time of hospital visit, and it is therefore believed that relatively accurate predictions are possible.

[0236] Figure 16 shows the change in mean free T4, hypothyroidism symptom score, on-site HR, WD-rHR, WD-sleepHR, and WD-2to6HR based on the changes in thyroid function parameters by visit shown in Figure 13 (from top left to right, error bars 95% CI). In Figure 16, on-site rHR refers to the resting heart rate measured palliatively after 15 minutes of rest at the time of the visit. WD-rHR refers to the average resting heart rate measured by a wearable device over the five days prior to the visit. WD-sleepHR refers to the average sleep heart rate measured by a wearable device over the five days prior to the visit. WD-2to6HR refers to the average heart rate measured by a wearable device between 2:00 AM and 6:00 AM over the five days prior to the visit. In Figure 16, among the acquired heart rate-related parameters, WD-rHR, WD-sleepHR, and WD-2to6HR were calculated based on biometric information and / or time, exercise, and sleep information acquired using a wearable device.

[0237] In Figure 16, the change in symptom score according to visit time shows that the error bars for the hypothyroidism group overlap with the mean value for the control group, making it difficult to use the symptom score as a single indicator to distinguish between hypothyroidism and normal. On the other hand, in Figure 16, the error bars for heart rate-related parameters (On-site rHR, WD-rHR, WD-sleepHR, and WD-2to6HR) according to visit time not only lie below the mean value for the control group, but also show that the mean value for the hypothyroidism group is significantly different from the mean value for the control group, indicating that heart rate-related parameters (On-site rHR, WD-rHR, WD-sleepHR, and WD-2to6HR) can be used as a single indicator to distinguish between hypothyroidism and normal.

[0238] As a result, it was proven that parameters based on heart rate measured by a wearable device can not only be used as an indicator for predicting hypothyroidism, but also show stronger predictive power than conventionally used symptom scores. The "association between heart rate measured by a wearable device and the prevalence or recurrence rate of hypothyroidism" confirmed based on this clinical study shows that by using the monitoring system according to the example of this embodiment, it is possible to easily predict recurrence by evaluating the degree of hyperthyroidism regulation from changes in resting heart rate, without the patient having to visit the hospital in person, simply by wearing a wearable device.

[0239] This specification does not separately describe the content of clinical research on the correlation between hyperthyroidism and heart rate as the basis for the prediction of thyroid dysfunction by the monitoring system according to the embodiment of the present invention. This is disclosed in Korean Patent Registration No. 10-2033696, and even if the specification does not include a duplicate description of the content, a person skilled in the art would be able to fully understand that it is possible to predict hyperthyroidism based on heart rate information using a wearable device.

[0240] 1-1.5 Timing of Thyroid Dysfunction Monitoring (S100) According to an example of this embodiment, the method for monitoring thyroid dysfunction (S100) may include obtaining biological information (S1100), calculating monitoring data (S1300), and determining thyroid dysfunction (S1500).

[0241] The biometric information acquisition (S1100) can be performed according to a first cycle. The biometric information acquisition (S1100) cycle can be determined according to a data transmission cycle of the user terminal (2000). The biometric information acquisition (S1100) cycle can be determined according to a biometric information transmission cycle of the user terminal (2000). The biometric information acquisition (S1100) can be performed according to a determined cycle (i.e., the immediately preceding first cycle).

[0242] The period (i.e., the immediately preceding first period) at which the monitoring server 3000 acquires biometric information from the user terminal 2000 (S1100) may be different from the period at which the wearable device 1000 acquires biometric signals. The period at which the monitoring server 3000 acquires biometric information from the user terminal 2000 (S1100) may be longer than or equal to the period at which the wearable device 1000 acquires biometric signals. This has already been explained above, so a repeated explanation will be omitted.

[0243] The calculation of monitoring data (S1300) may be performed according to a second cycle. The calculation of monitoring data (S1300) may be performed by being triggered by the acquisition of biological information (S1100). The calculation of monitoring data (S1300) may be performed according to a cycle (i.e., the immediately preceding second cycle) preset in the monitoring server (3000). Alternatively, the calculation of monitoring data (S1300) may be performed when a signal for performing step S1300 is received from an external device (e.g., user terminal (2000)).

[0244] The period in which the monitoring server (3000) calculates the monitoring data (S1300) (i.e., the immediately preceding second period) may be different from the period in which the monitoring server (3000) acquires the biometric information (S1100) (i.e., the immediately preceding first period). The period in which the monitoring server (3000) calculates the monitoring data (S1300) (i.e., the immediately preceding second period) is longer than or the same as the period in which the monitoring server (3000) acquires the biometric information (S1100) (i.e., the immediately preceding first period).

[0245] The determination of thyroid dysfunction (S1500) may be performed according to the third cycle. The determination of thyroid dysfunction (S1500) may be performed by triggering the calculation of monitoring data (S1300). The determination of thyroid dysfunction (S1500) may be performed according to a cycle preset in the monitoring server (3000) (i.e., the immediately preceding third cycle).

[0246] The cycle (i.e., the immediately preceding third cycle) at which the monitoring server (3000) determines thyroid dysfunction (S1500) may be different from the cycle (i.e., the immediately preceding second cycle) at which the monitoring server (3000) calculates monitoring data (S1300). The cycle (i.e., the immediately preceding third cycle) at which the monitoring server (3000) determines thyroid dysfunction (S1500) may be longer than or equal to the cycle (i.e., the immediately preceding second cycle) at which the monitoring server (3000) calculates monitoring data (S1300).

[0247] FIG. 17 is a diagram for explaining the timing of executing the operation of the method for monitoring abnormal thyroid function (S100) according to the example of this embodiment.

[0248] According to an embodiment of this embodiment, the reference data can be calculated by triggering receipt of thyroid status information. The biological information can be acquired according to a first period. The monitoring data can be calculated according to a second period. The determination of thyroid dysfunction can be performed in response to the calculation of the monitoring data.

[0249] Referring to FIG. 12, when the monitoring server 3000 receives thyroid status information (S2100), it can calculate reference data for a reference data calculation period (RP) (S2300).

[0250] The reference data may be calculated based on biological information of at least one pause period included in the reference data calculation period (RP). The reference data may be calculated based on a heart rate of at least one pause period included in the reference data calculation period (RP). When the biological information used in the calculation includes a heart rate, the reference data may be calculated using the reference data calculation method described herein and start with a reference heart rate.

[0251] In one example, the reference heart rate may be determined based on the heart rate during a rest period extracted from a period including days when the user's thyroid hormone levels are within the normal range.

[0252] In another example, the reference heart rate may be determined by extracting a resting period during a period including days when the user's thyroid hormone levels are outside the normal range, and estimating the reference period data based on the heart rate during the resting period.

[0253] In another example, the baseline heart rate can be calculated based on the user's heart rate over multiple consecutive days in the absence of the user's thyroid hormone levels.

[0254] The reference data calculation period (RP) may also include a period before the thyroid status information is received (S2100). In one example, the reference data calculation period (RP) may be determined to be a total of five days, including two days before and two days after the day on which the thyroid status information is received (S2100).

[0255] The monitoring server (3000) may acquire (S1100) biological information according to a first cycle. The period for acquiring biological information (S1100) may include a section that overlaps with the calculation period (RP) of reference data. The period for acquiring biological information (S1100) may overlap with the period for calculating monitoring data (S1300).

[0256] The biological information acquired according to the first period may be stored in the monitoring server 3000. The biological information acquired according to the first period may be stored in the monitoring server 3000 for a predetermined period.

[0257] The monitoring server 3000 may calculate monitoring data according to the second period (S1300). The monitoring server 3000 may calculate monitoring data for the monitoring period (MP) for each second period (S1300).

[0258] The monitoring data may be calculated based on biological information of at least one pause section included in the monitoring period (MP). The monitoring data may be calculated based on a heart rate of at least one pause section included in the monitoring period (MP). When the heart rate is included in the biological information used in the calculation calculated by the monitoring data calculation method described in this specification, the monitoring data may be disclosed as a monitored heart rate.

[0259] In one example, the monitoring heart rate may be determined by extracting a pause section of the monitoring period and determining the heart rate during the pause section. The pause section may be selected based on information regarding the user's exercise state. The pause section may be selected based on a section in which the user's step count is zero and continues for a predetermined period of time or more. The pause section may be selected based on a section in which the user's acceleration is zero and continues for a predetermined period of time or more.

[0260] In another example, the monitored heart rate may be determined based on the heart rate during a sleep period extracted from the monitoring period. The sleep period may be selected based on information regarding the user's exercise state. Alternatively, the sleep period may be selected based on information regarding breathing, noise, or biological information other than the heart rate, etc., obtained through the device sensor unit 1400.

[0261] The monitoring server (3000) can calculate first monitoring data for a first monitoring period (MP) at a first time point, and can calculate second monitoring data for a second monitoring period (MP) at a second time point after a period corresponding to a second period has elapsed from the first time point.

[0262] The first monitoring period (MP) and the second monitoring period (MP) may include an overlapping section. The first monitoring period (MP) and the second monitoring period (MP) may be of the same length. In other words, if the first monitoring period (MP) is 5 days, the second monitoring period (MP) is also 5 days.

[0263] Although not required, the reference data calculation period (RP) and the monitoring period (MP) may be the same length of time, i.e., if the reference data calculation period (RP) is 5 days, the monitoring period (MP) is also 5 days.

[0264] According to an example of this embodiment, the period in which the monitoring server (3000) calculates the monitoring data (S1300) may be longer than the period in which the monitoring server (3000) acquires the biological information (S1100). Specifically, for example, the monitoring server (3000) can acquire the biological information at three-hour intervals and calculate the monitoring data at one-day intervals.

[0265] The time point at which the monitoring data is calculated (S1300) may be adjacent to the end time point of the monitoring period (MP) of the monitoring data. The time point at which the monitoring data is calculated (S1300) may be the same as the end time point of the monitoring period (MP) of the monitoring data. The time point at which the monitoring data is calculated (S1300) may be substantially the same as the end time point of the monitoring period (MP) of the monitoring data.

[0266] The monitoring server (3000) can determine (S1500) whether or not there is a thyroid dysfunction according to the third cycle. After calculating (S1300) the monitoring data, the monitoring server (3000) can determine (S1500) whether or not there is a thyroid dysfunction.

[0267] After calculating the monitoring data (S1300), the monitoring server (3000) can compare the monitoring data with the reference data (S1510). When the comparison of the reference data with the monitoring data (S1510) satisfies predetermined conditions, the monitoring server (3000) determines that there is an abnormality in thyroid function (S1530) and can execute appropriate actions to output a warning.

[0268] The number of times step S1510 is performed may be greater than or equal to the number of times step S1530 is performed.

[0269] The period in which the monitoring server (3000) calculates the reference data (S200) may be longer than the period in which the monitoring data is calculated (S1300).

[0270] According to an embodiment of the present invention, the reference data may be stored in the server database 3200 of the monitoring server 3000 before calculating the monitoring data (S1300). The monitoring server 3000 can check the stored reference data at the time of calculating the monitoring data (S1300) and determining whether or not there is thyroid dysfunction (S1500). Therefore, the reference data values ​​stored in the server database 3200 can be maintained until a new event occurs and the reference data is updated. When thyroid status information is received, the monitoring server 3000 can calculate the reference data (S200) and store the new reference data in the server database 3200.

[0271] Once the reference data is calculated (S200), the determination of thyroid dysfunction using the reference data (S1500) can be performed multiple times. After the reference data is calculated (S200), multiple monitoring of thyroid dysfunction (S100) can be performed until another event occurs.

[0272] According to an example of this embodiment, the time when the reference data is calculated (S2300) and the time when the thyroid function abnormality is determined (S1500) may not overlap. The calculation period of the reference data (RP) and the monitoring period (MP) may not overlap.

[0273] According to an example of this embodiment, in response to the reception of thyroid status information (S2100), first reference data for the calculated calculation period (RP) of the first reference data can be calculated (S2300). In response to the reception of new thyroid status information (S2100), second reference data for the calculated calculation period (RP) of the second reference data can be calculated (S2300).

[0274] Here, the calculation period (RP) of the first reference data and the calculation period (RP) of the second reference data may not overlap, or the calculation period (RP) of the first reference data may be included in the calculation period (RP) of the second reference data.

[0275] 2. User Interface of Thyroid Dysfunction Abnormality Monitoring System (100) 18 and 19 are diagrams for explaining a user interface 400 in the abnormal thyroid function monitoring system 100 according to an example of this embodiment.

[0276] According to an example of this embodiment, the user terminal (2000) can output the results associated with the thyroid dysfunction monitoring (S100). According to an example of this embodiment, the user terminal (2000) can output the results associated with the thyroid dysfunction determination (S1500). According to an example of this embodiment, the user terminal (2000) can output the results associated with the thyroid dysfunction determination (S1530).

[0277] The terminal output unit 2200 of the user terminal 2000 can output appropriate information to the user based on the information received from the monitoring server 3000.

[0278] Referring to FIG. 18, the user terminal (2000) can output a user interface (400) including a thyroid information interface (4100), a heart rate information interface (4200), and a hormone information interface (4300).

[0279] The thyroid information interface 4100 may output information based on the thyroid dysfunction determination (S1500). As an example, the thyroid information interface 4100 may output information regarding the risk of thyroid dysfunction. As a specific example, the thyroid information interface 4100 may output information indicating that the risk of hyperthyroidism is 74%, which is high. The thyroid information interface 4100 may further output information to encourage a hospital visit. As a specific example, the thyroid information interface 4100 may output a message indicating that the risk of hyperthyroidism is high and that the patient should consult with their doctor. As a specific example, the thyroid information interface 4100 may output a questionnaire for self-diagnosis.

[0280] The "alert" described herein can include providing information to the user based on the determination of thyroid dysfunction (S1500). The "alert message" described herein can include providing information to the user based on the determination of thyroid dysfunction (S1500) in a visual, auditory, tactile, olfactory, and / or gustatory manner.

[0281] The heart rate information interface 4200 may output information based on the calculation of the monitoring data (S1300). In one example, the heart rate information interface 4200 may output the monitoring heart rate information. In a specific example, the heart rate information interface 4200 may output the monitoring heart rate information so that the change in the monitoring heart rate information over time is displayed.

[0282] Information based on the calculation of the reference data (S200) may also be output to the heart rate information interface 4200. As an example, reference heart rate information may be output to the heart rate information interface 4200. As a specific example, reference heart rate information, first critical heart rate information that is higher than the reference heart rate by about a first critical value and serves as a criterion for determining thyroid abnormality, and second critical heart rate information that is lower than the reference heart rate by about a second critical value and serves as a criterion for determining thyroid abnormality may also be output to the heart rate information interface 4200.

[0283] The heart rate information interface 4200 may output other information along with the monitored heart rate information. In a specific example, the heart rate information interface 4200 may output the monitored heart rate information and the time-dependent changes in hormone values ​​obtained from the user's blood test.

[0284] The hormone information interface 4300 can output hormone values ​​obtained from the user's blood test input through the terminal input unit 2100. As an example, the hormone information interface 4300 can output hormone values ​​obtained from the user's most recently input blood test. As a specific example, the hormone information interface 4300 can output free T4 and TSH hormone values ​​obtained from the user's most recently input blood test.

[0285] The hormone information interface 4300 may also output the date and time of the user's blood test. The hormone information interface 4300 may also output the date and time that has passed since the most recent date and time of the user's blood test.

[0286] Referring to FIG. 19, the user terminal (2000) can output a user interface (400) including an examination information input interface (4400).

[0287] An interface for inputting the user's blood test results may be output to the test information input interface 4400. For example, the test information input interface 4400 may output a separate interface for inputting the user's blood test results by hormone.

[0288] The test information input interface 4400 may further include a photo input interface 4420. When the user clicks on the photo input interface 4420, the user terminal 2000 may provide an interface for taking a photo of the user's test result form. When the user's test result form is photographed, the user terminal 2000 may perform an optical character reader (OCR) to confirm the user's hormone information.

[0289] The user interface 400 may further include a self-diagnosis input interface for receiving symptom information from the user, such as insomnia, headache, hand tremors, and difficulty concentrating.

[0290] 3. Modified embodiments of the thyroid dysfunction monitoring system (100) According to an example of this embodiment, the thyroid dysfunction monitoring system 100 can predict a user's thyroid dysfunction based on a biological signal. The thyroid dysfunction monitoring system 100 can predict a user's thyroid dysfunction by monitoring the user's heart rate information.

[0291] When the abnormal thyroid function monitoring system 100 predicts a user's thyroid function abnormality, it is necessary to distinguish it from other diseases that manifest similar symptoms (i.e., similar biological signals) in order to more accurately determine thyroid function. For example, when the abnormal thyroid function monitoring system 100 predicts a user's thyroid function based on the user's heart rate information, it is important to distinguish it from atrial fibrillation, which causes a significant increase in heart rate compared to normal.

[0292] For this reason, the thyroid dysfunction monitoring system 100 can predict thyroid dysfunction by taking into account a second factor other than the heart rate. Below, a modified embodiment of the thyroid dysfunction monitoring system 100 for more accurate prediction of thyroid dysfunction in a user will be described in detail.

[0293] The thyroid dysfunction monitoring method described below can provide more accurate thyroid dysfunction monitoring even for users who have never suffered from hyperthyroidism or hypothyroidism in the past or who have few genetic predispositions.

[0294] FIG. 20 is a flowchart for explaining the thyroid function abnormality monitoring (S300) according to an example of this embodiment.

[0295] 20, the method for monitoring thyroid function abnormalities (S300) according to this embodiment may include monitoring thyroid function according to a first factor (S3100), monitoring thyroid function according to a second factor (S3200), and determining whether thyroid function is abnormal (S3300). According to this embodiment, steps S3100, S3200, and S3300 may be performed by a monitoring server (3000).

[0296] The thyroid function monitoring by the first factor (S3100) may be performed in a similar manner to the above-described thyroid dysfunction monitoring method (S100). In one example, the thyroid function monitoring by the first factor (S3100) may be performed in a similar manner to the above-described thyroid dysfunction monitoring method (S100), by acquiring heart rate information (S1100), calculating a monitored heart rate (S1300), and comparing the heart rate with a reference heart rate to determine whether or not there is a thyroid dysfunction (S1500).

[0297] Therefore, redundant explanations regarding thyroid function monitoring by factor 1 (S3100) will be omitted.

[0298] The thyroid function monitoring according to the second factor (S3200) may be performed in the form of analyzing PPG data acquired through the device sensor unit 1400. For example, the acquired PPG data may be data acquired for extracting a heart rate used in the thyroid function monitoring according to the first factor (S3100).

[0299] FIG. 21 is a diagram illustrating the PPG data acquired through the wearable device (1000) and the analysis of the PPG data performed through the monitoring server (3000).

[0300] The monitoring server (3000) can receive PPG data acquired through the device sensor unit (1400) of the wearable device (1000). The server communication unit (3100) can receive the acquired PPG data from the user terminal (2000) and / or the wearable device (1000). The wearable device (1000) can transmit the acquired PPG data to the user terminal (2000), and the user terminal (2000) can transmit the received PPG data to the monitoring server (3000). The monitoring server (3000) can receive the PPG data acquired by the device sensor unit (1400) through the user terminal (2000).

[0301] 21, the monitoring server 3000 can check the peak interval (PI) based on the PPG data. The monitoring server 3000 can check the peak interval (PI), which is the time interval between a point where a PPG peak is confirmed and a point where the next PPG peak is confirmed in the PPG data. The monitoring server 3000 can check the change in the peak interval (PI) based on the PPG data.

[0302] In the case of thyroid dysfunction, the heart rate is seen to steadily increase or decrease over a long period of time, while in the case of atrial fibrillation, irregular heartbeats are seen to be repeated in a state in which many parts of the heart muscle contract simultaneously in an irregular and uncontrolled manner. Therefore, the accuracy of diagnosing thyroid dysfunction can be improved by classifying the peak interval (PI) of the PPG data.

[0303] According to an embodiment of the present invention, the monitoring server (3000) checks the peak interval (PI) of the PPG data in step S3200, and if the peak interval (PI) changes significantly over time, it can determine that the risk of atrial fibrillation is higher than the risk of thyroid dysfunction. As an example, if it is determined that there is thyroid dysfunction in step S3100, the monitoring server (3000) checks the peak interval (PI) of the PPG data in step S3200, and if the peak interval (PI) changes significantly over time, it can determine that the risk of atrial fibrillation is high (S3300). Steps S3200 and S3300 can be executed by the server control unit (3300).

[0304] The monitoring server (3000) checks the peak interval (PI) of the PPG data in step S3200, and if the peak interval (PI) remains almost constant, it can determine that the risk of thyroid dysfunction is higher than the risk of atrial fibrillation. For example, if it is determined that there is thyroid dysfunction in step S3100, the monitoring server (3000) checks the peak interval (PI) of the PPG data in step S3200, and if the peak interval (PI) remains almost constant regardless of time change, it can determine that there is a high risk of thyroid dysfunction (S3300).

[0305] When the thyroid dysfunction monitoring (S300) according to this embodiment is performed, an advantage can be derived that thyroid dysfunction can be predicted more accurately without adding additional hardware.

[0306] 20, the thyroid function monitoring based on the second factor (S3200) can be performed in a form of analyzing other biological information besides heart rate information acquired through the device sensor unit 1400. In one example, the wearable device 1000 can acquire temperature information of the user. The device sensor unit 1400 can sense the user's temperature, and the device communication unit 1300 can transmit the temperature information to the monitoring server 3000.

[0307] The monitoring server 3000 can calculate a reference temperature and a monitoring temperature. The reference temperature can be obtained in a manner similar to the method for obtaining reference data in step S2300, as described above. The monitoring temperature can be obtained in a manner similar to the method for obtaining monitoring data in step S1300, as described above.

[0308] The monitoring server (3000) can compare the reference temperature with the monitoring temperature to perform thyroid function monitoring (S3200).

[0309] The monitoring server (3000) according to this embodiment can determine whether a user has an abnormal thyroid function based on the user's heart rate information and temperature information. The monitoring server (3000) can simultaneously monitor the user's thyroid function based on the heart rate information and the temperature information. In other words, it can calculate the monitored heart rate and monitored temperature information for the monitoring period, compare the monitored heart rate with a reference heart rate, and compare the monitored temperature with the reference temperature, and determine whether the user has an abnormal thyroid function (S3300) based on the two resulting values.

[0310] According to another embodiment of this embodiment, the monitoring server (3000) may sequentially perform thyroid function monitoring of the user based on heart rate information and function monitoring of the user based on temperature information. In other words, if thyroid function abnormality is determined by comparing the monitored heart rate with the reference heart rate (S3100), the monitoring server may compare the monitored temperature with the reference temperature and determine whether the user has thyroid function abnormality based on the two results (S3200), ultimately determining whether the user has thyroid function abnormality (S3300). For example, if thyroid function abnormality is determined by comparing the monitored heart rate with the reference heart rate (S3100), thyroid function monitoring by comparing the monitored temperature with the reference temperature (S3200) may not be performed.

[0311] According to an example of this embodiment, the reference period when the reference heart rate is calculated and the reference period when the reference temperature is calculated may overlap. If necessary, the reference period when the reference heart rate is calculated and the reference period when the reference temperature is calculated may be the same. If necessary, the monitoring period when the monitoring heart rate is calculated and the monitoring period when the monitoring temperature is calculated may overlap. If necessary, the monitoring period when the monitoring heart rate is calculated may be longer than the monitoring period when the monitoring temperature is calculated.

[0312] 20, the second factor-based thyroid function monitoring (S3200) can be performed by requesting additional biological information through the device sensor unit 1400. As an example, if the result of S3100 indicates a possible thyroid abnormality, the monitoring server 3000 can request transmission of additional ECG waveform data, analyze the data, and perform the second factor-based thyroid function monitoring (S3200).

[0313] FIG. 22 is a flowchart for explaining thyroid function monitoring based on the second factor (S3200) according to an example of this embodiment.

[0314] If it is determined through the thyroid function monitoring based on the first factor (S3100) that the user's thyroid function may be abnormal, the monitoring server (3000) may request the input of additional data (S3210). The monitoring server (3000) may transmit a request for additional data to the user terminal (2000) to receive the input of additional data (S3210). Step S3210 is an operation performed by the server control unit (3300) through the server communication unit (3100).

[0315] In one example, the user terminal 2000 may request additional data from the wearable device 1000 in response to a request from the monitoring server 3000. The wearable device 1000 may output a notification to the user requesting input of additional data through the device output unit 1200 in response to the request from the user terminal 2000. In another example, the user terminal 2000 may output a notification to the user requesting input of additional data through the wearable device 1000 through the terminal output unit 2200.

[0316] If the additional data is ECG data, the user needs to connect both hands to at least two electrodes formed on the wearable device (1000).

[0317] FIG. 23 is a conceptual diagram for explaining a method for acquiring ECG data using a wearable device (1000) according to an example of this embodiment.

[0318] Acquisition of ECG data by the wearable device (1000) may require the left or right hand to be in contact with the first electrode (ET1) and the other hand to be in contact with the second electrode (ET2), forming a closed electrical loop around the body with both electrodes as reference.

[0319] In the wearable device (1000) shown in Figure 23, the first electrode (ET1) is formed on the back surface of the display that comes into contact with the wrist, and the second electrode (ET2) is formed on the scroll for adjusting the display, etc.

[0320] In this case, if the first electrode (ET1) is formed on the wrist of the left hand, the left hand contacts the first electrode (ET1) and the fingers of the right hand contact the second electrode (ET2), ECG sensing can be performed through the wearable device (1000).

[0321] 22, the monitoring server 3000 may prompt the user to touch the second electrode ET2 with a finger of the right hand as a request for input of additional data (S3210). If additional data is input by the user performing a specific action, the wearable device 1000 may transmit the acquired additional data to the monitoring server 3000 through the user terminal 2000.

[0322] The monitoring server 3000 may analyze the waveform of the input additional data (S3230). As an example of analyzing the waveform of the additional data, it may be possible to check whether a P wave can be distinguished.

[0323] 24(a) and (b) are diagrams for explaining a method of analyzing the waveform of ECG data (S3230) according to an example of this embodiment.

[0324] When a user shows symptoms of atrial fibrillation, the P wave in the user's PQRS wave may not be accurately distinguished. Referring to Figure 24(a), the PQRS wave is clearly displayed in the ECG data of a person who is classified as a normal person, but referring to Figure 24(b), it can be seen that values ​​other than the maximum peak value are not accurately distinguished in the ECG data of a person who is classified as having atrial fibrillation, and in particular, the P wave is not distinguished.

[0325] Continuing with FIG. 22, the monitoring server (3000) analyzes the waveform of the input additional data (S3230) and if it determines that the user's P waves are not significantly separated, it can determine that the risk of atrial fibrillation is greater than the risk of thyroid dysfunction (S3300).

[0326] The monitoring server (3000) analyzes (S3230) the waveform of the input additional data, and if it determines that the user's P waves are well distinguished, it can determine (S3300) that the risk of thyroid dysfunction is greater than the risk of atrial fibrillation.

[0327] 4. Thyroid dysfunction monitoring considering medication (S400) The majority of patients with thyroid dysfunction are treated with medication, but even those taking medications should be monitored for thyroid dysfunction.

[0328] Patients are concerned about the appropriateness of the dosage of medication they are taking and whether they are currently experiencing side effects. Therefore, the present invention discloses a method for monitoring thyroid dysfunction that can be provided to patients taking medication.

[0329] When providing thyroid dysfunction monitoring to patients taking medications, the following should be considered:

[0330] For example, if monitoring indicates that the patient has already developed "hyperactivity disorder" and is taking medication, it is unnecessary to continually warn about hyperactivity, as the patient is already suffering from hyperactivity and taking medication, and continuous warnings may encourage an excessive sense of crisis in the patient.

[0331] In another example, if monitoring reveals signs of risk of hypoglycemia when the patient is already taking medication for "hyperglycemia," this means that the amount of medication is excessive for the patient, and it is necessary to warn the user as soon as possible.

[0332] Therefore, in order to provide thyroid function abnormality monitoring to patients taking medication, it is important to perform appropriate thyroid function abnormality monitoring based on the medications taken by the user.Therefore, below we will describe a thyroid function monitoring system that takes medication into account according to an example of this embodiment.

[0333] 4.1 Receiving medication information (S4100) FIG. 25 is a diagram for explaining the thyroid function monitoring method (S400) taking into account medication according to an example of this embodiment.

[0334] The thyroid function monitoring method taking medication into consideration (S400) may include receiving medication information (S4100), selecting a monitoring algorithm (S4300), and monitoring thyroid function (S4500). According to an embodiment, steps S4100, S4300, and S4500 may be performed by the monitoring server (3000).

[0335] The monitoring server (3000) may receive (S4100) drug taking information from the user terminal (2000). The server communication unit (3100) may receive (S4100) drug taking information from the user terminal (2000). The drug taking information is information acquired through the terminal input unit (2100). If prescription information of a user is acquired through the terminal input unit (2100), the drug taking information is information transmitted to the monitoring server (3000) based on the prescription information. The drug taking information is information acquired from a separate server that manages prescription information and transmitted to the monitoring server (3000) based on the prescription information of the wearable device (1000) user.

[0336] The user terminal (2000) can receive input of at least one of the prescription date and time of the user's thyroid function-related medication, the name of the medication, the type of medication, the dosage, and the medication dosing cycle. In one example, the information can be received through the terminal input unit (2100) by the user's physical input (e.g., touch input). In another example, the information can be received through the terminal input unit (2100) by taking an image of a prescription, etc.

[0337] The user terminal (2000) can transmit medication information including at least one of the prescription date and time of medication related to the user's thyroid function, the name of the medication, the type of medication, the dosage, and the medication cycle to the monitoring server (3000).

[0338] 4.2 Monitoring Algorithm Selection (S4300) When the monitoring server 3000 receives the medication information, the monitoring server 3000 can select a monitoring algorithm (S4300). When the server control unit 3300 receives the medication information, the server control unit 3300 can select a monitoring algorithm (S4300).

[0339] FIG. 26 is a flowchart for explaining a method for selecting a monitoring algorithm (S4300) according to an example of this embodiment.

[0340] When the monitoring server (3000) receives the medication information, it can select (S4300) a monitoring algorithm to be used to determine whether the user has thyroid dysfunction (S4500).

[0341] The monitoring server (3000) can check whether the medications taken by the user include a medication classified as an "antithyroid drug" (S4311). If the medications taken by the user include a medication classified as an "antithyroid drug," the monitoring server (3000) can select the 1-1 monitoring algorithm (S4331) so that step S4500 is executed according to the 1-1 monitoring algorithm.

[0342] If the medications taken by the user do not include a medication classified as an "antithyroid drug," the monitoring server (3000) can check whether the medications taken by the user include a medication classified as a "thyroid hormone drug" (S4313).If the medications taken by the user include a medication classified as a "thyroid hormone drug," the monitoring server (3000) can select the first-second monitoring algorithm (S4333) so that step S4500 is executed according to the first-second monitoring algorithm.

[0343] If the medications taken by the user do not include medications classified as "thyroid hormone medications," the monitoring server (3000) can select a second monitoring algorithm (S4335) so that step S4500 is executed according to the second monitoring algorithm.

[0344] The monitoring server (3000) may change the order of steps S4311 and S4313 or may execute them simultaneously. In this case, if it is determined based on the medication information that both the "antithyroid drug" and the "thyroid hormone" are to be taken, the monitoring server (3000) may select the 1-1 monitoring algorithm (S4331) so that step S4500 is executed according to the 1-1 monitoring algorithm.

[0345] According to an example of this embodiment, in order for the monitoring server (3000) to check whether there are any drugs corresponding to "antithyroid drugs" and / or "thyroid hormone drugs" based on the drug administration information, information mapping drug names and drug types may be stored in the server database (3200). The monitoring server (3000) can check whether there are any drugs corresponding to "antithyroid drugs" and / or "thyroid hormone drugs" in the drug administration information by referring to the stored information on drug names and drug types.

[0346] According to another example of this embodiment, the monitoring server (3000) can check the type of drug from the drug taking information, check whether the type of drug corresponds to "antithyroid drugs" and / or "thyroid hormone drugs", and check whether the drug taking information contains any drugs that correspond to "antithyroid drugs" and / or "thyroid hormone drugs".

[0347] According to an example embodiment of this embodiment, the monitoring server (3000) can further acquire diagnostic information of the user based on the medication information. In one example, the monitoring server (3000) can classify the user into a hyperthyroidism treatment group or a hypothyroidism treatment group based on the medication information.

[0348] If it is determined in step S4311 that the medication information includes a medication corresponding to an "antithyroid medication," the monitoring server (3000) may determine that the user is included in the hyperthyroidism treatment group. If it is determined that the user is included in the hyperthyroidism treatment group, the monitoring server (3000) may select the 1-1 monitoring algorithm (S4331) so that step S4500 is performed according to the 1-1 monitoring algorithm.

[0349] If it is determined in step S4313 that the medication information includes a medication corresponding to "thyroid hormone medication," the monitoring server (3000) may determine that the user is included in the hypothyroidism treatment group. If it is determined that the user is included in the hypothyroidism treatment group, the monitoring server (3000) may select the first-second monitoring algorithm (S4333) so that step S4500 is performed according to the first-second monitoring algorithm.

[0350] If it is determined that there are no drugs corresponding to "thyroid hormone drugs" and "antithyroid drugs" in the drug taking information, the monitoring server (3000) may select a second monitoring algorithm (S4335) so that step S4500 is executed according to the second monitoring algorithm. If necessary, the monitoring server (3000) may request the necessary information from the user terminal (2000) to confirm the user's past medical history. If necessary, the monitoring server (3000) may check existing data stored in the server database (3200) to confirm the user's past medical history.

[0351] 4.3 Thyroid Dysfunction Monitoring (S4500) 25, the monitoring server (3000) can perform thyroid function monitoring (S4500) using the selected monitoring algorithm. The server control unit (3300) can perform thyroid function monitoring (S4500) using the selected monitoring algorithm.

[0352] FIG. 27 is a flowchart illustrating a method for monitoring thyroid function (S4500) by selecting the 1-1 monitoring algorithm (S4331) according to an embodiment of the present invention.

[0353] 27, thyroid function monitoring (S4500) can be performed by comparing the monitoring data with the reference data. In the following, a specific description will be given assuming that the monitoring data is the monitored heart rate and the reference data is the reference heart rate.

[0354] The monitoring server 3000 can compare the monitoring data with the reference data and determine whether the monitoring data is greater than the reference data by a first threshold value (S4511).

[0355] If the monitoring data is greater than the reference data by a first threshold or more, the monitoring server (3000) can determine that the user has an abnormal thyroid function. For example, if the monitored heart rate is greater than the reference heart rate by a first threshold or more (e.g., 10 beats), the monitoring server (3000) can determine that the user has an abnormal thyroid function. The monitoring server (3000) can predict the user's hyperthyroidism.

[0356] However, if the 1-1 monitoring algorithm is selected, since the user has already been diagnosed with hyperthyroidism and is taking medication to treat it, it would not be appropriate to continuously output a warning to the effect that hyperthyroidism is suspected and that the user should visit a hospital.

[0357] Therefore, when the first-first monitoring algorithm is selected but the monitoring data is greater than the reference data by a first threshold value or more, the monitoring server (3000) determines whether the grace period based on the date and time of prescription of the drug has elapsed (S4531), and if the grace period has elapsed, it may guide the output of a warning regarding hyperthyroidism (S4533). In one example, the grace period is three months from the date and time of prescription of the drug. In another example, the grace period is one month from the date and time of prescription of the drug. The grace period may be determined by the monitoring server (3000) that receives the drug taking information.

[0358] If it is determined in step S4533 that the user's hyperthyroidism persists, the monitoring server (3000) can transmit a signal to the user terminal (2000) (S4533) so that a warning regarding the user's thyroid abnormality is output through the terminal output unit (2200) of the user terminal (2000). In one example, the monitoring server (3000) can transmit a signal to the user terminal (2000) so that a warning such as "Visit a hospital as the dosage of medication needs to be increased" or "Visit a hospital and consult a doctor" is output through the terminal output unit (2200).

[0359] If the monitoring data is not greater than the reference data by the first threshold or more, the monitoring server 3000 may compare the monitoring data with the reference data to determine whether the monitoring data is smaller than the reference data by the second threshold or more (S4513).

[0360] If the monitoring data is smaller than the reference data by more than a second critical value, the monitoring server (3000) can determine that the user has an abnormal thyroid function. For example, if the monitored heart rate is smaller than the reference heart rate by more than a second critical value (e.g., 8 beats), the monitoring server (3000) can determine that the user has an abnormal thyroid function. The monitoring server (3000) can predict hypothyroidism in the user.

[0361] The monitoring server (3000) may be induced to output a warning about hypothyroidism (S4535) when the monitoring data is smaller than the reference data by a second critical value or more.

[0362] If it is determined in step S4535 that the user has developed hypothyroidism, the monitoring server (3000) can transmit a signal to the user terminal (2000) (S4535) so that the terminal output unit (2200) of the user terminal (2000) outputs a warning regarding the user's thyroid abnormality. In one example, the monitoring server (3000) can transmit a signal to the user terminal (2000) so that the terminal output unit (2200) outputs a warning such as "Visit a hospital as the amount of medication is deemed excessive" or "Visit a hospital and consult a doctor."

[0363] 28 is a flowchart illustrating a method for monitoring thyroid function (S4500) by selecting the first and second monitoring algorithms (S4333) according to an embodiment of the present invention. According to the embodiment, the steps of FIG. 28 can be performed by the server control unit (3300).

[0364] 28, thyroid function monitoring (S4500) can be performed by comparing the monitoring data with the reference data. In the following, a specific description will be given assuming that the monitoring data is the monitored heart rate and the reference data is the reference heart rate.

[0365] The monitoring server 3000 can compare the monitoring data with the reference data and determine whether the monitoring data is greater than the reference data by a first threshold value (S4511).

[0366] If the monitored data is greater than the reference data by a first threshold or more, the monitoring server (3000) can determine that the user has an abnormal thyroid function. For example, if the monitored heart rate is greater than the reference heart rate by a first threshold (e.g., 10 beats) or more, the monitoring server (3000) can determine that the user has an abnormal thyroid function. The monitoring server (3000) can predict the user's hyperthyroidism.

[0367] If the monitoring data is greater than the reference data by more than the first threshold value, the monitoring server (3000) can determine that the user has an abnormality in thyroid function. The monitoring server (3000) can predict the user's hyperthyroidism.

[0368] The monitoring server (3000) may be induced to output a warning about hyperthyroidism (S4532) when the monitoring data is greater than the reference data by a first threshold value or more.

[0369] If it is determined in step S4515 that the user has developed hyperthyroidism, the monitoring server (3000) can transmit a signal to the user terminal (2000) (S4532) so that a warning regarding the user's thyroid abnormality is output through the terminal output unit (2200) of the user terminal (2000). In one example, the monitoring server (3000) can transmit a signal to the user terminal (2000) so that a warning such as "visit a hospital as the amount of medication is deemed excessive" or "visit a hospital and consult a doctor" is output through the terminal output unit (2200).

[0370] If the monitoring data is not greater than the reference data by more than the first threshold, the monitoring server 3000 may compare the monitoring data with the reference data to determine whether the monitoring data is smaller than the reference data by more than the second threshold (S4513).

[0371] If the first-second monitoring algorithm is selected but the monitoring data is smaller than the reference data by more than the second threshold, the monitoring server (3000) determines whether the grace period based on the date and time of prescription of the drug has elapsed (S4534), and if the grace period has elapsed, it can guide the output of a warning regarding hyperthyroidism (S4536). In one example, the grace period is three months from the date and time of prescription of the drug. In another example, the grace period is one month from the date and time of prescription of the drug. The grace period can be determined by the monitoring server (3000) that receives the drug taking information.

[0372] If it is determined in step S4534 that the user's hypothyroidism persists, the monitoring server (3000) can transmit a signal to the user terminal (2000) (S4536) so that a warning regarding the user's thyroid abnormality is output through the terminal output unit (2200) of the user terminal (2000). In one example, the monitoring server (3000) can transmit a signal to the user terminal (2000) so that a warning such as "Visit a hospital as the dosage of medication needs to be increased" or "Visit a hospital and consult a doctor" is output through the terminal output unit (2200).

[0373] According to the embodiment of this embodiment, when the second monitoring algorithm is selected (S4335), monitoring can be performed in the same manner as before the drug taking information is input to the monitoring server (3000).

[0374] In one example, when a second monitoring algorithm is selected (S4335), the server (3000) may output the warning message when the monitored heart rate is greater than the reference heart rate by a first threshold or more, and may output the warning message when the monitored heart rate is smaller than the reference heart rate by a second threshold or more. When the second monitoring algorithm is selected (S4335), thyroid function monitoring may be performed similarly to the above-described FIG. 11. Therefore, a redundant description will be omitted.

[0375] The above has described the thyroid dysfunction monitoring taking into account medication (S400) according to an example of this embodiment. However, although the above-described thyroid dysfunction monitoring taking into account medication (S400) has been described by exemplifying a case in which risk assessments for both hypothyroidism and hyperthyroidism are performed, thyroid dysfunction monitoring can be performed to monitor only hypothyroidism or only hyperthyroidism.

[0376] Therefore, the scope of the present invention should not be limited by the specific examples described in this specification for the sake of understanding, but should be interpreted based on the interpretation of the claims of the present invention.

[0377] FIG. 29 is a diagram for explaining the timing of executing the operation of the method for monitoring abnormal thyroid function (S400) according to the example of this embodiment.

[0378] According to an example of this embodiment, the reception of medication information (S4100) can be used as a trigger to select a monitoring algorithm (S4300). When medication information is received (S4100), the monitoring server (3000) can select a monitoring algorithm (S4300).

[0379] The number of times the monitoring server (3000) selects a monitoring algorithm (S4300) may correspond to the number of times the monitoring server (3000) receives medication information (S4100), or may be greater than or equal to the number of times the monitoring server (3000) receives medication information (S4100).

[0380] After the monitoring server 3000 selects a monitoring algorithm (S4300), the monitoring server 3000 can perform thyroid function monitoring according to the monitoring algorithm (S4500). After the monitoring server 3000 selects a monitoring algorithm (S4300), the monitoring server 3000 can perform thyroid function monitoring according to the monitoring algorithm (S4500) according to the monitoring cycle until a new event occurs.

[0381] The number of times the monitoring server (3000) performs thyroid function monitoring (S4500) may be more than the number of times the monitoring server (3000) receives medication information (S4100).The number of times the monitoring server (3000) performs thyroid function monitoring (S4500) may be more than the number of times the monitoring server (3000) selects a monitoring algorithm (S4300).

[0382] For example, when a patient visits a hospital and enters prescription information into the user terminal (2000), the user terminal (2000) can transmit the entered information to the monitoring server (3000). When the monitoring server (3000) receives medication information (e.g., some information included in the prescription information) (S4100), the monitoring server (3000) can select a monitoring algorithm (S4300). Once the monitoring algorithm is selected (S4300), the monitoring server (3000) can perform thyroid function monitoring (S4500) according to a monitoring period. In more detail, for example, after the monitoring algorithm is selected (S4300), the monitoring server (3000) can perform thyroid function monitoring (S4500) once every day.

[0383] When thyroid function monitoring is performed (S4500), information on reference data (e.g., reference heart rate) may be stored in the monitoring server (3000). The reference data stored in the monitoring server (3000) may be calculated in response to receiving the user's thyroid status information (S2100). The reference data calculation period of the monitoring server (3000) and the monitoring algorithm selection period of the monitoring server (3000) are independent of each other. The reference data calculation period of the monitoring server (3000) and the monitoring algorithm selection time of the monitoring server (3000) may overlap.

[0384] 5. Medication management According to an example of this embodiment, the monitoring server (3000) can provide a medication notification based on the received medication information. The monitoring server (3000) can be induced to output a medication notification based on the medication cycle included in the medication information.

[0385] The monitoring server 3000 can transmit a signal to the user terminal 2000 to notify the user of medication intake. In one example, the user terminal 2000 can notify the user that it is time to take their medication based on the signal received from the monitoring server 3000.

[0386] FIG. 30 is a diagram illustrating a user interface 400 in a user terminal 2000 that provides advice on taking medicines according to an embodiment of the present invention.

[0387] Referring to FIG. 30, the user terminal (2000) can output a user interface (400) including a drug information input interface (4600) and a drug intake notification interface (4700).

[0388] The drug information input interface (4600) may be an interface for receiving prescription information from a user. When the user selects the drug information input interface (4600), a specific interface for receiving drug information may be further output to the user terminal (2000). In one example, the user terminal (2000) may be provided with an interface for receiving drug administration information, including at least one of the prescription date and time of a drug related to thyroid function, the name of the drug, the drug type, the drug dosage, and the drug administration cycle. In another example, the user terminal (2000) may be provided with an interface for taking a photo of the user's prescription. In yet another example, the user terminal (2000) may be provided with an interface for receiving user authentication information in order to receive the user's prescription information from a separate server.

[0389] The medication reminder interface 4700 may include an interface for checking that the medication has been taken after it has been taken. The medication reminder interface 4700 may include an interface for indicating when the medication should be taken and for guiding the user to take the medication at the appropriate time.

[0390] For example, the user terminal 2000 may be provided with an interface that notifies the user to take medicine at the appropriate time. The interface is a screen that can be viewed when a program for thyroid function monitoring is output on the user terminal 2000. The interface is a screen that is provided in a pop-up format when another program is running on the user terminal 2000.

[0391] In one example, the monitoring server (3000) can output medication recommendations more frequently than the thyroid dysfunction monitoring (S4500). In one example, if the thyroid dysfunction monitoring (S4500) is performed once a day, the medication recommendations can be performed three times a day.

[0392] In another example, the monitoring server (3000) may output medication recommendations at the same frequency as the frequency of the thyroid dysfunction monitoring (S4500). In one example, if the thyroid dysfunction monitoring (S4500) is performed once a day, the medication recommendations may be performed once a day.

[0393] As described above, the method for monitoring thyroid function abnormalities according to some embodiments in which the thyroid function monitoring system (100) includes the wearable device (1000), the user terminal (2000), and the monitoring server (3000) has been specifically described.

[0394] However, the thyroid function monitoring system 100 can be easily modified and implemented by those skilled in the art.

[0395] According to an example of this embodiment, the above-described method for monitoring thyroid dysfunction may be provided in the form of a recording medium having a program for executing the method recorded thereon and having computer-readable and executable code stored thereon.

[0396] According to an embodiment of this embodiment, the above-described thyroid function abnormality monitoring method may be provided in the form of a wearable device (1000), a user terminal (2000) and / or a monitoring server (3000) for performing the method.

[0397] According to an embodiment of this embodiment, the above-mentioned thyroid function abnormality monitoring method may be provided in the form of a thyroid function monitoring system (100) including at least one of a wearable device (1000), a user terminal (2000) and / or a monitoring server (3000) for performing the method.

[0398] In one example, the thyroid function monitoring system 100 may be implemented in a form including only a wearable device 1000 and a monitoring server 3000. In this case, information output through the user terminal 2000 may be output through the wearable device 1000. Information input through the user terminal 2000 may be input through the wearable device 1000. Information regarding the user's thyroid function abnormality determined by the monitoring server 3000 may be output through the wearable device 1000. The wearable device 1000 may perform functions via an input interface that receives specific information from the user, such as thyroid status information and medication information.

[0399] In another example, the thyroid function monitoring system 100 may be implemented in a form including only the wearable device 1000 and the user terminal 2000. In this case, the user terminal 2000 may perform the operations of the monitoring server 3000 described above by running a program stored in the terminal memory unit 2400.

[0400] In another example, the thyroid function monitoring system 100 may be implemented in a form including a wearable device 1000, a monitoring server 3000 communicating with the wearable device, and a user terminal 2000 communicating with the monitoring server 3000. In this case, the wearable device 1000 transmits acquired biological information to the monitoring server 3000, the monitoring server 3000 performs thyroid function monitoring using the acquired biological information, and the monitoring server 3000 transmits the biological information received from the wearable device 1000 to the user terminal 2000 to output heart rate information to the user terminal.

[0401] In addition, the thyroid function monitoring system 100 can be embodied in various forms.

[0402] Hereinafter, a method for determining thyroid dysfunction using skin conductance according to the present invention will be described.

[0403] 6. Monitoring thyroid dysfunction using skin conductance 31 is a block diagram of a skin conductance measuring sensor according to an embodiment, which may also be expressed as an EDA (Electrodermal Activity) sensor.

[0404] 31, the EDA sensor 5000 may include an EDA measurement unit 5100, an EDA calculation unit 5200, an EDA output unit 5300, an EDA storage unit 5400, an EDA communication unit 5500, and an EDA control unit 5600. However, the components shown in FIG. 31 are not essential, and the EDA sensor 5000 may include more or fewer components.

[0405] The EDA sensor 5000 may be included in an electronic device. The EDA sensor 5000 may be included in a wearable device 1000. For example, but not limited to, the EDA sensor 5000 may be included in a smart watch, a smart ring, a removable patch, etc.

[0406] For example, the EDA sensor 5000 can measure the skin conductance of a user wearing the wearable device 1000. In this case, the EDA measurement unit 5100 included in the EDA sensor 5000 can measure the skin conductance of the user.

[0407] According to an embodiment of this embodiment, the EDA sensor 5000 may be implemented as an integrated part of the wearable device 1000. In one example, the EDA sensor 5000 may be a sensor included in the device sensor unit 1400, and in this case, the EDA sensor 5000 may be controlled by the device control unit 1600.

[0408] Specifically, for example, the device control unit (1600) can control the EDA measurement unit (5100) to measure skin conductance, and can control the measured skin conductance to be transmitted to the user terminal (2000) through the device communication unit (1300).

[0409] The EDA measuring unit 5100 can measure the skin conductance of the user through electrodes. The EDA measuring unit 5100 can include a plurality of electrodes that can come into contact with the user's skin. For example, the EDA measuring unit 5100 can include two electrodes that serve as a positive electrode and a negative electrode, respectively.

[0410] According to an embodiment, the distance between the electrodes may be designed to vary depending on the thickness of the stratum corneum of the user's skin. For example, the distance between the electrodes may be designed to be thicker than the thickness of the stratum corneum of the skin.

[0411] In this case, a user with a thin stratum corneum can use a wearable device (1000) including an EDA sensor (5000) with smaller electrode spacing than a user with a thick stratum corneum.

[0412] According to the embodiment, the EDA measurement unit (5100) can measure the skin conductance of the user by passing a current below a certain value through the skin of the user through two electrodes.

[0413] For example, the EDA measurement unit (5100) can measure the skin conductance of the user by passing a DC current or an AC current through the electrodes to the user's skin.

[0414] When a DC current is applied, the measurement accuracy may be improved compared to when an AC current is applied, but there is a possibility that the hair roots may be damaged.

[0415] On the other hand, when AC current is applied, the measurement accuracy may be lower than when DC current is applied, but the possibility of damaging hair roots may be lower.

[0416] According to an embodiment, the EDA measurement unit 5100 may include a virtual ground, a current source, and a low-pass filter. The EDA measurement unit 5100 may further include, but is not limited to, an amplifier. In this case, the low-pass filter or amplifier may be included in the EDA measurement unit 5100 or the EDA calculation unit 5200.

[0417] The EDA measuring unit 5100 can obtain the result of sensing the skin conductance of the user through a current source connected to the electrodes, and can improve the accuracy of the skin conductance data through a low-pass filter.

[0418] The EDA measurement unit (5100) can transmit the measured result value to the EDA calculation unit (5200), the EDA output unit (5500) or the EDA storage unit (5400).

[0419] In addition, if the EDA sensor (5000) is not physically the same device as the wearable device (1000), the EDA measurement unit (5100) can transmit the measured result value (e.g., skin conductance data) to the device input unit (1100) through the EDA communication unit (5500).

[0420] The EDA calculation unit (5200) can receive the result value of the EDA measurement unit (5100). The EDA calculation unit (5200) can also process the result value of the EDA measurement unit (5100). The EDA calculation unit (5200) can obtain skin conductance data based on the result value of the EDA measurement unit (5100).

[0421] In another example, the EDA calculation unit 5200 can obtain the skin conductance data without processing the result value. In this case, the EDA measurement unit 5100 and the EDA calculation unit 5200 are not separated but are one unit or module.

[0422] According to the embodiment, the EDA calculation unit 5200 may use a filter to filter the result value of the EDA measurement unit 5100. For example, the EDA calculation unit 5200 may use a Schmitt trigger filter or a recursive moving filter to filter the result value of the EDA measurement unit 5100, but is not limited thereto and may use other filters.

[0423] According to another embodiment, the EDA calculation unit (5200) can process the result value of the EDA measurement unit (5100).

[0424] For example, the EDA calculation unit (5200) can convert the result value of the EDA measurement unit (5100) into another value using Ohm's law. Specifically, if the result value of the EDA measurement unit (5100) is resistance, the EDA calculation unit (5200) can convert the result value into voltage or current using Ohm's law. Examples of converting voltage into current or resistance and current into resistance or voltage are also possible.

[0425] For example, the EDA calculation unit 5200 can separate the result of the EDA measurement unit 5100 into tonic EDA and phasic EDA. The details of tonic EDA and phasic EDA will be described later.

[0426] Also, for example, the EDA calculation unit (5200) can convert the result value of the EDA measurement unit (5100) into SCL (skin conductance (SC) level), where SCL is the logarithm of the result value.

[0427] Furthermore, for example, the EDA calculation unit 5200 can extract parameters such as SCFr (skin conductance fluctuation rate), SCRr (skin conductance response rate), SCRh (skin conductance response habitation), SCRm (skin conductance response magnitude), SCRol (skin conductance response onset latency), SCRpl (skin conductance response peak latency), SCRd (skin conductance response duration), SCRpr (skin conductance response peak rate), and SCRrr (skin conductance response recovery rate) using the result values ​​of the EDA measurement unit 5100. The listed parameters can be understood in more detail by referring to the description of FIG. 34, and detailed description thereof will be omitted here.

[0428] Furthermore, for example, the EDA calculation unit (5200) can calculate the average value, median value, standard deviation, etc. of the result values ​​of the EDA measurement unit (5100).

[0429] Furthermore, for example, the EDA calculation unit (5200) can calculate the average value, median value, standard deviation, etc. for the SCL, SCFr, SCRr, SCRh, SCRm, SCRol, SCRpl, SCRd, SCRpr, and SCRrr.

[0430] The EDA calculation unit (5200) can transmit the skin conductance data, which is the calculation result, to the EDA output unit (5300), the EDA storage unit (5400), and the EDA communication unit (5500).

[0431] In addition, if the EDA sensor (5000) is not physically the same device as the wearable device (1000), the EDA calculation unit (5200) can transmit the calculation result to the device input unit (1100) through the EDA communication unit (5500).

[0432] The EDA output unit 5300 may receive skin conductance data calculated by the EDA calculation unit 5200. The EDA output unit 5300 may output the skin conductance data. The EDA output unit 5300 may be, but is not limited to, a visual, auditory, and / or tactile output, and may be implemented in various forms.

[0433] For example, the EDA output unit (5300) can be implemented as a display that outputs images, a speaker that outputs sounds, a haptic that generates vibrations, and / or other various types of output means.

[0434] The EDA output unit (5300) may be implemented in the form of an output interface that connects an external output device that outputs information to the wearable device (1000) instead of a device that independently outputs information to the outside.

[0435] The EDA output unit 5300 can be connected to the device output unit 1200 of the wearable device 1000. For example, the EDA output unit 5300 can communicate with the device through the EDA communication unit 5500 and be connected to the device output unit 1200.

[0436] At this time, the device output unit (1200) can output the result value of the EDA output unit (5300) through the display, speaker, haptics, or other various forms of output means of the wearable device (1000).

[0437] The EDA output unit (5300) can perform the same functions as the device output unit (1200) described above.

[0438] According to an embodiment, the EDA output section (5300) can be provided in the form of the same component as the device output section (1200) of the wearable device (1000).

[0439] The EDA storage unit 5400 can receive skin conductance data, which is the calculation result of the EDA calculation unit 5200. The EDA storage unit 5400 can store the skin conductance data.

[0440] The EDA storage unit 5400 can store various data and programs required for the operation of the EDA sensor 5000. The EDA storage unit 5400 can store information acquired by the EDA sensor 5000.

[0441] The EDA storage unit 5400 can store data temporarily or semi-permanently. Examples of the EDA storage unit 5400 include a hard disk drive (HDD), an SSD, a flash memory, a ROM, a RAM, or cloud storage. However, the EDA storage unit 5400 is not limited thereto and can be implemented with various modules for storing data.

[0442] The EDA storage unit 5400 may be provided in a form built into the EDA sensor 5000 or in a detachable form.

[0443] The EDA storage unit (5400) can perform the same function as the device memory unit (1500) described above.

[0444] In one example, the EDA storage unit (5400) can store the skin conductance data until the skin conductance data is transmitted via the device communication unit (1300).

[0445] According to an embodiment, the EDA storage unit (5400) may be provided in the form of the same component as the device memory unit (1500) of the wearable device (1000).

[0446] In addition, if the EDA sensor (5000) is not physically the same device as the wearable device (1000), the EDA storage unit (5400) can transmit the values ​​stored in the device memory unit (1500) through the EDA communication unit (5500).

[0447] The EDA communication unit 5500 may perform a role of enabling the EDA sensor 5000 to transmit / receive data to / from an external device. For example, the EDA communication unit 5500 may perform a role of enabling the EDA sensor 5000 to transmit / receive data to / from the wearable device 1000.

[0448] The EDA communication unit 5500 may include one or more modules that enable communication. The EDA communication unit 5500 may include a module that enables communication with an external device via a wired system. Alternatively, the EDA communication unit 5500 may include a module that enables communication with an external device via a wireless system.

[0449] Alternatively, the EDA communication unit (5500) may include a module that enables communication with an external device via a wired method and a module that enables communication with an external device via a wireless method.

[0450] Specifically, for example, the EDA communication unit (5500) may be configured as a wired communication module that connects to the Internet via a LAN, a mobile communication module such as LTE that connects to a mobile communication network via a mobile communication base station to send / receive data, a short-range communication module that uses a WLAN-based communication method such as Wi-Fi or a WPAN-based communication method such as Bluetooth (registered trademark) or ZigBee, a satellite communication module that uses GNSS such as GPS, or a combination of these.

[0451] The EDA communication unit 5500 can perform the same functions as the device communication unit 1300. In one example, the EDA communication unit 5500 can communicate with the user terminal 2000 and / or the monitoring server 3000.

[0452] According to an embodiment, the EDA communication unit (5500) can be provided in the form of the same component as the device communication unit (1300) of the wearable device (1000).

[0453] The EDA control unit 5600 can perform the function of controlling the overall operation of the EDA sensor 5000. The EDA control unit 5600 can perform calculations and processing of various information to control the operation of the components of the terminal.

[0454] The EDA control unit (5600) can be realized by a computer or similar device using hardware, software, or a combination of these. In terms of hardware, the EDA control unit (5600) can be provided in the form of an electronic circuit such as a CPU chip that processes electrical signals and performs control functions, and in terms of software, it can be provided in the form of a program that drives the hardware EDA control unit (5600).

[0455] For example, the EDA control unit 5600 may control the EDA measurement unit 5100 to transmit a result to the EDA calculation unit 5200, the EDA communication unit 5500, or the EDA storage unit 5400, so that a current flows through the skin through the electrodes to enable the EDA measurement unit 5100 to measure the skin conductance of the user.

[0456] According to another embodiment, the EDA control unit (5600) can control the EDA calculation unit (5200) to receive the result value of the EDA measurement unit (5100). The EDA control unit (5600) can also control the EDA calculation unit (5200) to obtain skin conductance data. The EDA control unit (5600) can also control the EDA calculation unit (5200) to transmit the skin conductance data to the EDA output unit (5300), the EDA storage unit (5400), and the EDA communication unit (5500).

[0457] Furthermore, according to the embodiment, the EDA control unit (5600) can control the EDA output unit (5300) to receive skin conductance data, which is the calculation result of the EDA calculation unit (5200). Furthermore, the EDA control unit (5600) can control the EDA output unit (5300) to output the skin conductance data.

[0458] Also, according to the embodiment, the EDA control unit 5600 can control the EDA storage unit 5400 to receive skin conductance data, and the EDA control unit 5600 can control the EDA storage unit 5400 to store the skin conductance data.

[0459] Furthermore, according to the embodiment, the EDA control unit (5600) can control the EDA communication unit (5500) to communicate with an external device.

[0460] The EDA control unit (5600) can perform the same functions as the device control unit (1600) described above.

[0461] According to an embodiment, the EDA control unit (5600) can be provided in the form of the same component as the device control unit (1600) of the wearable device (1000).

[0462] In the following, unless otherwise specified, it is understood that the operation of the EDA sensor (5000) is performed under the control of the EDA control unit (5600).

[0463] FIG. 32 shows an apparatus for measuring skin conductance according to an embodiment.

[0464] Referring to Figure 32, Figure 32(a) shows a smart watch (6100) which is a wearable device, and Figure 32(b) shows a smart ring (6200) which is a wearable device. The smart watch (6100) and / or the smart ring (6200) may be the same as or perform the same role as the wearable device (1000).

[0465] Although Figure 32 illustrates only a smart watch (6100) and a smart ring (6200) as devices capable of measuring skin conductance, these are merely a few examples described for the sake of convenience and are not limiting.

[0466] For example, a device for measuring skin conductance may be a wrist band that can be worn on a user's wrist, a wearable sock that can be worn on a user's foot in the form of a sock, a wearable patch that can be attached to a user's skin, a wearable hair band that can be worn on a user's head, a device that can be worn on a user's ear in the form of an earphone, a device that can be clipped on a user's ear, and a lens-type device that can be inserted into a user's eye.The device may be embodied in various forms, without being limited to the examples listed in this specification.

[0467] The wearable device in FIG. 32 can include the EDA sensor (5000) of FIG.

[0468] According to an embodiment, the smart watch 6100 of Figure 32(a) may include an EDA sensor 5000 in the main body 6110. For example, the main body 6110 of the smart watch 6100 may include an EDA measurement unit 5100 in a portion that contacts the user's skin, thereby measuring the user's skin conductance.

[0469] Specifically, the electrodes of the EDA measuring unit (5100) are arranged at the rear of the main body (6110) and can measure the skin conductance of the user by passing a current through the user's skin.

[0470] According to another embodiment, the smart watch 6100 of Figure 32(a) may include a portion of the EDA sensor 5000 in the main body 6110 and another portion of the EDA sensor 5000 in the band region 6120. For example, the band region 6120 of the smart watch 6100 may include the EDA measurement unit 5100 in the portion that comes into contact with the user's skin to measure the user's skin conductance.

[0471] Specifically, the electrodes of the EDA measuring unit (5100) are arranged at the rear of the band region (6120) to pass a current through the user's skin and measure the user's skin conductance.

[0472] According to an embodiment, the smart ring 6200 of Figure 32(b) may include an EDA sensor 5000. For example, the smart ring 6200 may include an EDA measuring unit 5100 in the portion that contacts the user's skin to measure the user's skin conductance.

[0473] Specifically, the electrode (6210) of the EDA measuring unit (5100) is disposed in the inner region of the smart ring (6200) and can measure the skin conductance of the user by passing a current through the user's skin.

[0474] FIG. 33 is a diagram showing a graph of skin conductance according to an embodiment.

[0475] 33, a graph of skin conductance can be shown with skin conductance data over time (7110). The skin conductance data (7110) can include a tonic component (7120) and a phasic component (7130).

[0476] According to an embodiment, the skin conductance data (7110) is data acquired by an EDA sensor (5000).

[0477] According to an embodiment, the skin conductance data (7110) is the sum of a tonic component (7120) and a phasic component (7130).

[0478] For example, the tonic component (7120) is skin conductance data related to the external environment (e.g., ambient temperature) in the skin conductance data (7110), or, for example, the tonic component (7120) is a portion of the skin conductance data that represents the skin conductance level (SCL).

[0479] Also, for example, the phasic component (7130) is skin conductance data that is associated with an external stimulus, an environmental stimulus, or a short-term event.

[0480] Or, for example, the phasic component (7130) is a portion of the skin conductance data that represents the skin conductance response (SCR).

[0481] For example, the tonic component (7120) can be obtained by subtracting the phasic component (7130) from the skin conductance data (7110), and the phasic component (7130) can be obtained by subtracting the tonic component (7120) from the skin conductance data (7110).

[0482] Also, for example, the phasic component (7130) can be obtained by a convolution operation of the response function and the skin conductance data (7110).

[0483] Additionally, the tonic component (7120) and the phasic component (7130) can be converted into units of current, resistance or voltage by Ohm's law.

[0484] According to an embodiment, the tonic component 7120 and the phasic component 7130 may be obtained through calculation by the EDA calculation unit 5200 of the EDA sensor 5000. According to an embodiment, the tonic component 7120 and the phasic component 7130 may be obtained through calculation by the server control unit 3300 of the monitoring server 3000 that receives skin conductance data. According to an embodiment, the tonic component 7120 may be used as an indicator representing the magnitude or evolution of skin conductance. The magnitude or evolution of skin conductance may be used to infer a user's biological information. For example, the monitoring server 3000 may determine a change in skin temperature or body temperature based on the overall change in the tonic component 7120.

[0485] In addition, the phasic component (7130) can be used as an index representing the degree of change, amount of change, or storm expressed by the first derivative of skin conductance. For example, the monitoring server (3000) can predict the user's mood, stress, excitement level, or changes in the autonomic nervous system based on the overall change in the phasic component (7130).

[0486] According to an example of this embodiment, the monitoring server (3000) can acquire a tonic component (7120) and / or a phasic component (7130) based on skin conductance data, and can perform thyroid function abnormality monitoring of the user by referring to the acquired tonic component (7120) and / or phasic component (7130).

[0487] In one example, the monitoring server (3000) can calculate the monitoring skin conductance based on the tonic component (7120) corresponding to the pause section, and if the calculated monitoring skin conductance is higher than the reference skin conductance, it can determine that the user has a thyroid abnormality.

[0488] FIG. 34 shows a skin conductance graph according to another embodiment.

[0489] Referring to Figure 34, various parameters that can be obtained through analysis of skin conductance data graphs are illustrated.

[0490] The monitoring server (3000) can determine the stimulus onset (7210), response onset (7220), latency (7230), response threshold (7240), peak response (7250), recovery time (7260) and / or amplitude (7270) based on the skin conductance data graph.

[0491] For example, the stimulation start point (7210) is the point at which the skin conductance data begins to increase. Also, for example, the response start point (7220) is the point at which the magnitude is greater than a certain value from the stimulation start point (7210). At this time, the difference in magnitude between the response start point (7220) and the stimulation start point (7210) is the response threshold (7240).

[0492] Also, for example, the latency period (7230) can be calculated based on the stimulus start point (7210) and the response start point (7220). Specifically, the latency period (7230) is the period from the stimulus start point (7210) to the response start point (7220).

[0493] According to the embodiment, the monitoring server (3000) can grasp the degree of change in the user's skin conductance, the user's mood, stress, or changes in the autonomic nervous system based on the length of the latency period (7230). For example, if the latency period (7230) is short, the monitoring server (3000) can grasp the degree of change in the user's skin conductance by determining that the change occurs rapidly.

[0494] In addition, for example, if the latent period (7230) is short, the monitoring server (3000) can determine whether the user's mood changes suddenly, causing stress or whether the user's autonomic nervous system changes suddenly.

[0495] According to the embodiment, the monitoring server (3000) can adjust the sensitivity to changes in skin conductance by adjusting the response threshold (7240). For example, decreasing the response threshold (7240) can increase the frequency of changes in skin conductance. Alternatively, increasing the response threshold (7240) can decrease the frequency of changes in skin conductance, allowing the monitoring server (3000) to recognize only relatively large changes as changes in skin conductance.

[0496] Also, for example, the peak point (7250) is the point in time at which the skin conductance data has the largest value after the stimulation start point (7210) or the response start point (7220). Alternatively, the peak point (7250) is the point in time at which the first derivative of the skin conductance data is 0. Alternatively, the peak point (7250) is the point in time at which the first derivative of the skin conductance data is 0 and the second derivative is a negative number.

[0497] According to this embodiment, the monitoring server (3000) can determine the maximum value of the skin conductance data for a certain period of time based on the peak point (7250), and can grasp changes in the user's mood, stress, or autonomic nervous system. For example, if the peak point (7250) is large, the monitoring server (3000) can determine that the user's mood, stress level, or autonomic nervous system is significantly affected.

[0498] Also, for example, the recovery period (7260) may refer to the time it takes for the skin conductance data to reach a constant value from the peak point (7250).

[0499] In this case, the magnitude from the peak point (7250) to the constant value is the amplitude (7270) of the skin conductance data. However, the amplitude (7270) is not the magnitude from the peak point (7250) to a constant value related to the recovery period (7260), but to another value not related to the recovery period (7260).

[0500] In this case, the recovery period (7260) may be related to the latency period (7230) or the period from the response time (7220) to the peak point (7250).

[0501] For example, a shorter latency period (7230) or the time from the response time (7220) to the peak point (7250) may result in a shorter recovery period (7260). Alternatively, a longer latency period (7230) or the time from the response time (7220) to the peak point (7250) may result in a longer recovery period (7260).

[0502] According to this embodiment, the monitoring server 3000 can determine changes in the user's mood, stress, or autonomic nervous system based on the recovery period 7260 or amplitude 7270. For example, if the recovery period 7260 is short, the monitoring server 3000 can determine that the user's mood has changed suddenly, that the user is under a lot of stress, or that the autonomic nervous system has changed suddenly.

[0503] Also, for example, if the amplitude (7270) is large, the monitoring server (3000) can determine that the user's mood has changed suddenly, that the user is under a lot of stress, or that the autonomic nervous system has changed suddenly.

[0504] According to an example of this embodiment, the monitoring server (3000) can acquire the above-mentioned parameters based on the skin conductance data, and can identify periods in which stress was high or changes in the autonomic nervous system were rapid based on the acquired parameters.

[0505] For example, when performing thyroid function monitoring, the monitoring server (3000) can calculate the monitoring skin conductance by referring to the parameters described in the description of Figure 34. The interval used to calculate the monitoring skin conductance may not overlap with the interval during which excessive stress occurs or the autonomic nervous system undergoes sudden changes.

[0506] The monitoring server may calculate SCFr, SCRm, SCRpl, SCRd, SCRpr and / or SCRrr based on the parameters mentioned in the description of FIG.

[0507] For example, SCFr is an index relating to the time it takes for the amplitude to reach the amplitude of the peak point (7250) from the amplitude at the stimulus onset point (7210). Also, for example, SCRr is an index relating to the latency period (7230).

[0508] Also, for example, SCRm is an index related to the response starting point or response threshold (7240). Also, for example, SCRol is an index related to the stimulus starting point (7210).

[0509] For example, SCRpl is an index related to the reaction initiation point (7220). For example, SCRpl is an index related to the reaction initiation point (7220) or the peak point (7250). For example, SCRd is an index related to the reaction initiation point (7220) or the latent period (7230).

[0510] Also, for example, SCRpr is an index related to the peak point (7250) or the recovery period (7260), and SCRrr is an index related to the recovery period (7260) or the amplitude (7270).

[0511] According to an example of this embodiment, the monitoring server (3000) can calculate SCFr, SCRm, SCRpl, SCRd, SCRpr and / or SCRrr and perform thyroid function monitoring by referring to the calculated parameters.

[0512] In one example, the monitoring server (3000) performs thyroid function monitoring based on skin conductance data (7110), but can also use the calculated SCFr, SCRm, SCRpl, SCRd, SCRpr and / or SCRrr as secondary reference data to perform even more accurate thyroid function monitoring.

[0513] As described above, the description of the parameters related to skin conductance has been given based on the monitoring server (3000) grasping and calculating various indicators of skin conductance data, but this is not limited to this, and the EDA calculation unit (5200) can also perform the role of the monitoring server (3000).

[0514] FIG. 35 shows a graph of skin conductance data according to an embodiment.

[0515] The skin conductance data allows the monitoring server (3000) to understand changes in the user's mood, stress, or autonomic nervous system. The skin conductance data can also be used to check whether the user is making a false statement. The skin conductance data can also be used to analyze the behavior or statements of criminals.

[0516] According to an embodiment, the skin conductance data allows the monitoring server (3000) to determine whether the user has an abnormality in thyroid function.

[0517] For example, the monitoring server (3000) can determine whether the user has hyperthyroidism or hypothyroidism based on the user's skin conductance data. Also, for example, the monitoring server (3000) can detect thyroid diseases such as thyroid cancer and thyroid inflammation based on the user's skin conductance data.

[0518] For example, the autonomic nervous system function of a user suffering from hyperthyroidism may be excited, while the autonomic nervous system function of a user suffering from hypothyroidism may be depressed. In this case, by determining whether the autonomic nervous system function is excited based on the skin conductance data, the monitoring server (3000) can determine whether the user has hyperthyroidism or hypothyroidism.

[0519] Specifically, if the user's skin conductance data for a certain period (e.g., a monitoring period) exceeds a certain value, the monitoring server (3000) can determine that the user's autonomic nervous system function is in an overexcited state compared to a person with normal function, and can determine that the user is suffering from hyperthyroidism.

[0520] In particular, if the user's skin conductance data for a certain period of time is below a certain value, the monitoring server (3000) can determine that the user's autonomic nervous system function is excessively reduced compared to a normal person, and can determine that the user is suffering from hypothyroidism.

[0521] The method of determining whether or not a user has a thyroid dysfunction using skin conductance data will be described in detail later.

[0522] When determining whether or not a user has a thyroid dysfunction based on skin conductance data, the user can determine whether or not they have a thyroid dysfunction non-invasively without undergoing a hormone test.

[0523] Furthermore, when determining whether or not a user has a thyroid dysfunction based on skin conductance data, the presence or absence of the thyroid dysfunction of the user can be determined naturally in the user's daily life.

[0524] For example, conventional testing methods for testing thyroid function (e.g., hormone tests) can cause tension in the user and affect the results, but the method for determining whether or not there is a thyroid function abnormality according to this embodiment can prevent tension in the user.

[0525] Referring to FIG. 35, the skin conductance data can be divided into data before and after the sleep start time (7310).

[0526] According to the embodiment, the sleep onset time 7310 is a time determined by user input through the wearable device 1000. For example, the user can input information regarding sleep onset into the wearable device 1000, and the sleep onset time 7310 is a time determined by the information.

[0527] According to another embodiment, the sleep start time 7310 is a time determined by the wearable device 1000. For example, the sleep start time 7310 is a time determined by multiple sensors of the wearable device 1000 that can detect the user's movements.

[0528] Specifically, the sleep start time (7310) may be determined based on a period in which the number of steps extracted from the pedometer of the wearable device (1000) is below a certain value. Also, specifically, the sleep start time (7310) may be determined based on a period in which the GPS result of the wearable device (1000) is within a certain range.

[0529] In particular, the sleep start time 7310 may be determined based on a period in which the heart rate detected by the heart rate monitor of the wearable device 1000 is below a certain value. In particular, the sleep start time 7310 may be determined based on a period in which the result of the gyroscope of the wearable device 1000 is below a certain value.

[0530] In particular, the sleep onset time 7310 may be determined based on the user's sleep pattern ascertained through the wearable device 1000. For example, the sleep onset time 7310 is the time when the user's sleep pattern transitions from a wake period to a non-wake period (e.g., REM, non-REM, SWS).

[0531] According to another embodiment, the sleep onset time (7310) is the time when the skin conductance data begins to decrease rapidly.

[0532] Specifically, the sleep onset time (7310) is the time when the skin conductance data begins to decrease by 1 μS or more.

[0533] In particular, the sleep start time 7310 is the start time of the decrease section 7440. The decrease section 7440 will be described in detail later.

[0534] The sleep start time (7310) is the time when the user is considered to have entered the sleep period, and is not limited to the examples described in this specification.

[0535] Referring to FIG. 35, the appearance of the skin conductance data may appear different before and after the sleep start time (7310).

[0536] For example, the frequency of change in skin conductance data before the sleep onset time point (7310) may be greater than the frequency of change in skin conductance data after the sleep onset time point (7310).

[0537] Also, for example, the average skin conductance data before the sleep onset time point (7310) may be greater than the average skin conductance data after the sleep onset time point (7310).

[0538] Also, for example, the maximum value of the skin conductance data before the sleep onset time point (7310) may be greater than the maximum value of the skin conductance data after the sleep onset time point (7310).

[0539] Also, for example, the minimum value of the skin conductance data before the sleep onset time point (7310) may be greater than the minimum value of the skin conductance data after the sleep onset time point (7310).

[0540] 36 is a diagram illustrating a rest period in a skin conductance data graph according to an embodiment. Referring to FIG. 36, the skin conductance data after the sleep onset point (7310) may be lower than that before the sleep onset point (7310).

[0541] According to this embodiment, the skin conductance data after the sleep start time (7310) may include a decrease section (7440) and a pause section (7450). In this case, the pause section (7450) is a section that starts after the decrease section (7440).

[0542] According to the embodiment, the resting period (7450) is a period in which the fluctuation of the skin conductance data is within a certain range (7410). For example, the resting period (7450) is a period in which the fluctuation of the skin conductance data is within the first range (7410).

[0543] For example, the size of the first range (7410) is 3 μm S. As a preferred example, the size of the first range (7410) is 2 μm S. As a more preferred example, the size of the first range (7410) is 1 μm S. However, other optimal values ​​can be selected for the first range (7410) depending on the characteristics of the user's skin, and therefore, the first range (7410) is not limited to the above values.

[0544] The decrease section (7440) is a section in which the skin conductance data decreases by more than the second range after the sleep start time (7310). In this case, the second range may be larger than the first range. For example, the second range may be 2 μS, and the first range (7410) may be 1 μS.

[0545] The decrease section 7440 is a section in which the skin conductance data after the sleep onset time 7310 decreases by more than the first range 7410. For example, the decrease section 7440 may be a section in which the skin conductance data after the sleep onset time 7310 decreases by more than 1 μS or 2 μS, but is not limited thereto, and may be a section in which the skin conductance data decreases by more than a different value.

[0546] In addition, the decrease section (7440) and the pause section (7450) can be distinguished by the pause section start time point (7430).

[0547] According to the embodiment, the pause section start time (7430) is the entry time point at which the skin conductance data is increasing among the time points at which the skin conductance data fluctuation is within the first range (7410). For example, the pause section start time (7430) is the earliest time point at which the slope is positive among the time points at which the skin conductance data fluctuation is within 1 μS.

[0548] At this time, the difference (7420) between the skin conductance data at the start of sleep (7310) and the skin conductance data at the start of the rest period (7430) may be greater than the magnitude of the first range (7410).

[0549] At this time, the calculated rest period (7450) can be recognized as the user's true sleep period. Also, the monitoring skin conductance can be calculated based on the skin conductance data in the rest period (7450). The monitoring skin conductance can be determined by the mean value, median value, standard deviation, or moving average of the skin conductance data in the rest period (7450). The monitoring server (3000) can compare the monitoring skin conductance with the reference skin conductance to determine whether the user has thyroid dysfunction.

[0550] According to an embodiment, the end point of the pause period (7450) may be a point at which the fluctuation range of the skin conductance data falls outside the first range, or may be a point at which the magnitude of the skin conductance data is greater than or less than a certain value.

[0551] 37 is a diagram illustrating pause periods in a graph of skin conductance data according to an embodiment. Referring to FIG. 37, there may be multiple pause periods (7551, 7554) after the sleep start time (7310). The monitoring server 3000 may check the multiple pause periods (7551, 7554) after the sleep start time (7310).

[0552] According to this embodiment, the skin conductance data after the sleep start time (7310) may include a decrease section (7540), a first pause section (7551), and a second pause section (7554). At this time, the first pause section (7551) and the second pause section (7554) are sections that start after the decrease section (7540).

[0553] According to the embodiment, the first pause interval (7551) and the second pause interval (7554) are intervals in which the fluctuation of the skin conductance data is within a certain range (7510).

[0554] For example, the first pause section (7551) and the second pause section (7554) are sections in which the fluctuation of the skin conductance data is within the first range (7510). Specifically, the magnitude of the first range (7510) may be, for example, 1 μS or 2 μS, but is not limited thereto and may be other values.

[0555] The decrease section (7540) is a section in which the skin conductance data after the sleep onset time (7310) decreases by more than a second range. In this case, the second range may be greater than the first range. For example, the second range is 2 μS, and the first range (7510) is 1 μS. The decrease section (7540) is a section in which the skin conductance data after the sleep onset time (7310) decreases by more than the first range (7510). For example, the decrease section (7540) may be a section in which the skin conductance data after the sleep onset time (7310) decreases by more than 1 μS or 2 μS, but is not limited thereto, and may be a section in which the skin conductance data decreases by more than a different value.

[0556] The first pause section (7551) may include a first pause section start time (7530) and a first pause section end time (7552). The monitoring server (3000) may distinguish the decrease section (7540) and the first pause section (7551) based on the first pause section start time (7530).

[0557] The second pause interval (7554) may include a second pause interval start time (7553) and a second pause interval end time (7555). The monitoring server (3000) may not extract the interval between the first pause interval end time (7552) and the second pause interval start time (7553) as a pause interval.

[0558] According to the embodiment, the start point of the first pause interval (7530) and the start point of the second pause interval (7553) are the entry points at which the skin conductance data enters an increasing trend within the interval in which the fluctuation of the skin conductance data is within the first range (7510).

[0559] For example, the first pause start time (7530) and the second pause start time (7553) are the earliest time points at which the slope is a positive number within the section in which the skin conductance data fluctuates within 2 uS.

[0560] At this time, the difference (7520) between the skin conductance data at the start of sleep (7310) and the skin conductance data at the start of the first rest period (7530) and the start of the second rest period (7553) may be greater than the magnitude of the first range (7510).

[0561] According to the embodiment, the end time of the first pause section (7552) and the end time of the second pause section (7555) may be the time points at which the fluctuation of the skin conductance data falls outside the first range (7510), or the end time points of the first pause section (7552) and the end time points of the second pause section (7555) are the time points at which the magnitude of the skin conductance data becomes equal to or greater than a certain value.

[0562] At this time, the monitoring server (3000) can determine the calculated first resting period (7551) and second resting period (7554) as the user's true sleep period. Also, the monitoring server (3000) can determine whether the user has thyroid dysfunction or not through the skin conductance data in the multiple resting periods (7551, 7554).

[0563] For example, the monitoring server (3000) can determine whether or not the user has abnormal thyroid function by calculating the mean value, median value, standard deviation, or moving average of the skin conductance data over multiple pause periods (7551, 7554).

[0564] For example, the monitoring server (3000) may use the average value of the entire skin conductance data for the first pause period (7551) and the second pause period (7554), or may use the average value of the first pause period (7551) and the average value of the second pause period (7554), respectively.

[0565] 38 is a diagram illustrating pause periods in a graph of skin conductance data according to another embodiment. Referring to FIG. 38, there may be multiple pause periods (7651, 7654) after a sleep start point (7310).

[0566] The details of the decrease section (7640) overlap with the contents of the decrease section (7540) in FIG. 37, so they will be omitted here.

[0567] Also, the details of the first pause interval (7651) and the second pause interval (7654) overlap with the details of the first pause interval (7551) and the second pause interval (7554) in FIG. 37, so they will not be repeated here.

[0568] Also, the content of the first range (7610) overlaps with the content of the first range (7510) in FIG. 37, so detailed description will be omitted.

[0569] In addition, the details regarding the start time of the first pause interval (7630), the end time of the first pause interval (7652), the start time of the second pause interval (7653), and the end time of the second pause interval (7655) overlap with the details regarding the start time of the first pause interval (7530), the end time of the first pause interval (7552), the start time of the second pause interval (7553), and the end time of the second pause interval (7555) in Figure 37, so they will not be repeated in detail.

[0570] According to an embodiment, the calculated skin conductance data in the first pause period (7651) may be different from the calculated skin conductance data in the second pause period (7654).

[0571] In one example, the maximum skin conductance of the first pause interval (7651) may be less than the maximum skin conductance of the second pause interval (7654). In another example, the minimum skin conductance of the first pause interval (7651) may be less than the minimum skin conductance of the second pause interval (7654). In yet another example, the average skin conductance of the first pause interval (7651) may be less than the average skin conductance of the second pause interval (7654).

[0572] Furthermore, without being limited thereto, the value that can be calculated based on the skin conductance data in the first pause period (7651) may be different from the value that can be calculated based on the skin conductance data in the second pause period (7654).

[0573] According to an embodiment, the value range of the skin conductance data in the first pause period (7651) may be different from the value range of the skin conductance data in the second pause period (7654).

[0574] In one example, the numerical range of the skin conductance data in the first pause section (7651) may not overlap with the numerical range of the skin conductance data in the second pause section (7654). In another example, the numerical range of the skin conductance data in the first pause section (7651) may partially overlap with the numerical range of the skin conductance data in the second pause section (7654).

[0575] Specifically, for example, the skin conductance data of the first pause section (7651) may have a value between 1uS and 2uS, and the skin conductance data of the second pause section (7654) may have a value between 2uS and 3uS.

[0576] At this time, the fluctuation of the first pause section (7651) and the fluctuation of the second pause section (7654) may be within a range of 1 μS, or the numerical ranges of the first pause section (7651) and the second pause section (7654) may be different.

[0577] The monitoring server (3000) can determine the calculated first pause interval (7651) and second pause interval (7654) as the user's true sleep interval. The monitoring server (3000) can also determine whether the user has thyroid dysfunction based on the skin conductance data in the multiple pause intervals (7551, 7554).

[0578] 39 is a diagram illustrating a pause section in a graph of skin conductance data according to another embodiment. The details of the decrease section (7740) overlap with the details of the decrease section (7440) in FIG. 36, so they will not be repeated here.

[0579] Also, the details of the pause section (7751) overlap with the contents of the pause section (7450) in FIG. 36, so they will be omitted here.

[0580] Also, the content of the first range (7710) overlaps with the content of the first range (7410) in FIG. 36, so detailed description will be omitted.

[0581] 39, a pause period 7751 and noise periods 7760 and 7770 may occur after a sleep start time 7310. For example, a first noise period 7760 and a second noise period 7770 may occur after the sleep start time 7310.

[0582] According to the embodiment, the fluctuation of the first noise section (7760) and the fluctuation of the second noise section (7770) may be within the first range (7710).

[0583] However, when determining whether or not the user has thyroid dysfunction, the monitoring server (3000) may use the skin conductance data in the pause period (7751) but not use the skin conductance data in the noise period (7760, 7770).

[0584] Skin conductance data in the noise section (7760, 7770) is difficult to consider as the user's true sleep section, and therefore must be removed when determining whether the user has thyroid dysfunction.

[0585] According to the embodiment, the fluctuations of the first noise section (7760) are within the first range (7710) like the fluctuations of the pause section (7751), but the numerical range of the first noise section (7760) may be different from the numerical range of the pause section (7751).

[0586] In this case, the minimum value of the first noise section (7760) may be greater than the first limit value (7780). Therefore, the fluctuation of the skin conductance data in a certain section is within the first range (7710), but if the minimum value of the certain section is greater than or equal to the first limit value (7780), the certain section can be calculated as a noise section.

[0587] In addition, the average value of the first noise section (7760) may be greater than the first limit value (7780). Therefore, if the fluctuation of the skin conductance data of a certain section is within the first range (7710), and the average value of the certain section is greater than or equal to the first limit value (7780), the certain section can be calculated as a noise section.

[0588] According to the embodiment, the fluctuations of the second noise section (7770) are within the first range (7710) like the fluctuations of the pause section (7751), but the numerical range of the second noise section (7770) can be different from the numerical range of the pause section (7751).

[0589] In this case, the maximum value of the second noise section (7770) may be smaller than the second limit value (7790). Therefore, if the fluctuation of the skin conductance data in a certain section is within the first range (7710), but the maximum value of the certain section is equal to or smaller than the second limit value (7790), the certain section can be calculated as a noise section.

[0590] In addition, the average value of the second noise section (7770) may be smaller than the second limit value (7790). Therefore, if the fluctuation of the skin conductance data of a certain section is within the first range (7710), but the average value of the certain section is equal to or smaller than the second limit value (7790), the certain section can be calculated as a noise section.

[0591] However, without being limited to the above examples, if the fluctuation of skin conductance data for a certain period is within the first range (7710), but the absolute value of the certain period is a value that is not found in the user's true sleep period, the certain period can be extracted as a noise period.

[0592] Skin conductance data in a section extracted as a noise section may be excluded from the rest section even if the fluctuation falls within the first range (7710).

[0593] When the monitoring server (3000) checks for a pause period for calculating the monitoring skin conductance, it may check for a pause period that begins after a period in which the skin conductance decreases above the first range (7710) and fluctuates within the first range (7710).

[0594] If the calculated value of some of the confirmed sections deviates from a predetermined reference value, the monitoring server (3000) can exclude the section from the pause section and calculate the monitoring skin conductance based on the skin conductance data corresponding to the pause section.

[0595] 40 is a diagram illustrating a pause section in a graph of skin conductance data according to another embodiment. The details of the decrease section (7840) overlap with the details of the decrease section (7440) in FIG. 36, so they will not be repeated here.

[0596] Also, the details of the pause section (7850) overlap with the contents of the pause section (7450) in FIG. 36, so they will be omitted here.

[0597] Also, the content of the first range (7810) overlaps with the content of the first range (7410) in FIG. 36sw, so detailed description will be omitted.

[0598] 40, a decrease section 7840 and a pause section 7850 may be present after a sleep start point 7310. The decrease section 7840 and the pause section 7850 may be separated before and after the pause section start point 7830.

[0599] Apart from the pause start time (7430) in FIG. 36, the pause start time (7830) in FIG. 40 can be defined by other criteria.

[0600] According to the embodiment, the pause period start time (7830) is the earliest time point in the fixed period in which the fluctuation of the skin conductance data is within the first range (7810). At this time, the skin conductance data at the pause period start time (7830) is below a fixed value.

[0601] At this time, the difference (7860) between the skin conductance data at the start of sleep (7310) and the skin conductance data at the start of the rest period (7830) may be greater than the magnitude of the first range (7810).

[0602] In addition, the sleep interval (7850) may include an end point of the sleep interval, which is the latest point in the predetermined interval. The skin conductance data at the end point of the sleep interval may have a value that is above or below a certain value and is difficult to be considered as a true sleep interval of the user.

[0603] FIG. 41 is a diagram illustrating pauses in a graph of skin conductance data according to yet another embodiment.

[0604] Referring to FIG. 41, the monitoring server (3000) can calculate the amount of change in skin conductance data based on the skin conductance data.

[0605] According to an embodiment, the variation of the skin conductance data may be obtained through first differentiation of the skin conductance data, for example, based on the slope of the skin conductance data.

[0606] There is a difference in the amount of change in skin conductance data before and after the sleep onset time point 7310. For example, the average amount of change before the sleep onset time point 7310 may be greater than the average amount of change after the sleep onset time point 7310.

[0607] According to the embodiment, there is a difference in the storm region of the skin conductance data before and after the sleep start time 7310. In this case, the storm region can be calculated based on the degree of change frequency of the skin conductance data.

[0608] In this case, a storm area is an area with high frequency. For example, a storm area may refer to an area with 4-10 peaks per minute (4-10 peaks / min).

[0609] For example, there may be more storm regions before the sleep onset time (7310) than there are storm regions after the sleep onset time (7310). Also, for example, there may be more frequency of storm regions before the sleep onset time (7310) than there are frequency of storm regions after the sleep onset time (7310).

[0610] Also, for example, the average value of the storm region peak before the sleep onset (7310) may be greater than the average value of the storm region peak after the sleep onset (7310).

[0611] At this time, the monitoring server (3000) can determine whether the user has thyroid dysfunction or not based on the average value of the amount of data change, the frequency of data change, the number of storm areas, or the frequency of occurrence of storm areas.

[0612] For example, if the above parameters are above a certain value, the monitoring server (3000) can determine that the user is suffering from hyperthyroidism. Also, if the above parameters are below a certain value, the monitoring server (3000) can determine that the user is suffering from hypothyroidism.

[0613] According to the embodiment, the extraction of the pause period after the sleep start time 7310 is related to the user's sleep pattern, which is information obtained from an external device or the wearable device 1000.

[0614] For example, the user's sleep patterns can be obtained by polysomnography (PSG) or by sensing multiple sensors (e.g., accelerometers) included in the smartwatch.

[0615] At this time, the user's sleep pattern may be divided into, but not limited to, REM, N-REM1 (non-REM1), N-REM2 (non-REM2) or SWS (Slow-wave sleep) stages.

[0616] For example, the REM period, N-REM1 period, or a combination of these may be extracted as the rest period from the user's sleep pattern, but is not limited thereto. The monitoring server (3000) may extract as the rest period a period in which the user's sleep pattern is determined to be stable.

[0617] Also, for example, the probability of a storm region occurring in the N-REM2 section or the SWS section may be high. In this case, the monitoring server (3000) may exclude the N-REM2 section or the SWS section from the pause section. Alternatively, the monitoring server (3000) may assign a lower weight to the N-REM2 section or the SWS section among the pause sections.

[0618] According to another embodiment, the method for extracting pauses may take into account the user's sleep patterns in addition to the skin conductance data.

[0619] For example, the monitoring server (3000) can primarily extract rest periods from skin conductance data after the sleep start time (7310) using the methods described in Figures 36 to 40.

[0620] Also, for example, the monitoring server 3000 can obtain information about the user's sleep pattern from an external device or the wearable device 1000. In this case, the information about the sleep pattern can be obtained through a heart rate sensor or a motion sensor of the wearable device 1000 or the external device.

[0621] At this time, the monitoring server (3000) can extract the rest period secondarily from the rest period extracted first, excluding the portion where the user's sleep pattern is the SWS period and / or N-REM2 period.

[0622] Alternatively, the monitoring server 3000 may secondarily extract, from the primarily extracted resting period, a portion of the user's sleep pattern that corresponds to a REM period, as the resting period.

[0623] Therefore, the monitoring server (3000) can determine whether the user has abnormal thyroid function by treating the secondarily extracted pause period as a true pause period and determining the skin conductance data in the pause period.

[0624] FIG. 42 shows a flowchart of a pause extraction method according to an embodiment.

[0625] Referring to FIG. 42, the method for extracting a pause interval according to the embodiment may include a step of extracting a first interval (S610), a step of checking a second interval (S620), a step of comparing the size of the first interval with a predetermined value (S630), and a step of including the first interval in the pause interval (S640).

[0626] According to the embodiment, the monitoring server 3000 executes the step of extracting a first interval (S610). At this time, the step of extracting the first interval (S610) may include extracting an interval in which the variation of the skin conductance data is within a first range as the first interval.

[0627] For example, the monitoring server (3000) can receive skin conductance data from the wearable device (1000) or the EDA sensor (5000) and extract the first interval.

[0628] According to another embodiment, the EDA calculation unit (5200) can receive skin conductance data from the EDA measurement unit (5100) and extract the first section.

[0629] In this case, the difference between the maximum and minimum values ​​of the extracted first section may be equal to or smaller than the size of the first range. For example, the difference between the maximum and minimum values ​​of the first section is within 3 μS. Specifically, the difference between the maximum and minimum values ​​of the first section is 2 μS.

[0630] At this time, the start point of the extracted first section is the pause section start time (7430) described with reference to FIG. 36 or the pause section start time (7830) described with reference to FIG.

[0631] According to another embodiment, the monitoring server (3000) or the EDA calculation unit (5200) can extract the first section using the phasic component (7130) of the skin conductance data.

[0632] For example, the first section is a section in which the fluctuation or differential value of the phasic component (7130) is within a first range. Also, for example, the first section is a section in which the maximum value of the phasic component (7130) is equal to or less than a predetermined value. Specifically, the first section is a section in which the maximum value of the phasic component (7130) is equal to or less than 2 μS.

[0633] According to the embodiment, the monitoring server 3000 or the EDA calculation unit 5200 executes the step of checking the second interval (S620). At this time, the step of checking the second interval may include checking an interval in which the skin conductance data before the first interval decreases by more than the magnitude of the first range.

[0634] At this time, the second section is the decreasing section (7440) described with reference to Figure 36. Also, at this time, the starting point of the second section is the sleep start point (7310) described with reference to Figure 36.

[0635] In this case, the end point of the second section may be the start point of the pause section (7430) described with reference to Figure 36. Alternatively, the end point of the second section may be a point prior to the start point of the pause section (7430) in Figure 36.

[0636] In this case, the difference between the skin conductance data at the start and end of the second section may be equal to or greater than the magnitude of the first range.

[0637] According to the embodiment, the monitoring server 3000 or the EDA calculation unit 5200 performs a step of comparing the value of the first interval. At this time, the step of comparing the magnitude of the first interval with a predetermined value (S630) may include a step of checking whether the magnitude of the first interval is greater than a first value (A) and less than a second value (B).

[0638] For example, step S630 may include a step of checking whether the minimum value of the first section is greater than a first value (A) by the monitoring server 3000 or the EDA calculation unit 5200. Also, for example, step S630 may include a step of checking whether the maximum value of the first section is less than a second value (B) by the monitoring server 3000 or the EDA calculation unit 5200.

[0639] According to another embodiment, the monitoring server (3000) or the EDA calculation unit (5200) can confirm the magnitude of the first section using the tonic component (7120) of the skin conductance data.

[0640] For example, the monitoring server 3000 or the EDA calculation unit 5200 may check whether the minimum value of the tonic component 7120 during the first interval is greater than a first value A. Also, for example, the monitoring server 3000 or the EDA calculation unit 5200 may check whether the maximum value of the tonic component 7120 during the first interval is less than a second value B.

[0641] Also, for example, the monitoring server (3000) or the EDA calculation unit (5200) can check whether the average value of the tonic component (7120) during the first section is between the first value (A) and the second value (B).

[0642] If step S630 is not performed, it may not be possible to extract the rest period from the user's true sleep period.

[0643] For example, when a user is awakened by a hot external environment during sleep and is motionless in an awake state, the fluctuation of the first interval may be within the first range, and a second interval may exist before the first interval.

[0644] In this case, the value of the first interval may be greater than the second value B. In this case, since the user is not asleep but is awake, this interval may need to be excluded from the sleep interval.

[0645] In addition, for example, when a user is woken up by a cold external environment during sleep and is in an awake state and motionless, the value of the first interval may be smaller than the first value (A). In this case, since the user is in an awake state, this interval may need to be excluded from the sleep interval.

[0646] If the value of the first interval falls within the predetermined range, step S640 is executed. If not, step S610 is executed to extract a new first interval.

[0647] According to the embodiment, the monitoring server 3000 may include the first section confirmed through steps S610 to S630 in the pause section (S640).

[0648] The first section may be included in the pause section, and a step of determining whether or not the user has thyroid dysfunction based on skin conductance data in the pause section may be performed later.

[0649] According to the embodiment, the thyroid function of the user can be monitored through skin conductance data, and in this case, the thyroid function monitoring method of FIG.

[0650] According to the embodiment, when skin conductance information is acquired (S1100) through the EDA sensor (5000), monitoring data is calculated (S1300) and abnormalities in the user's thyroid function can be determined (S1500).

[0651] The wearable device 1000 can acquire skin conductance information of a user. The wearable device 1000 can acquire skin conductance information of a user wearing the wearable device 1000. At this time, acquisition of the skin conductance information can be performed at regular intervals.

[0652] For example, the device sensor unit (1400) of the wearable device (1000) includes an EDA sensor (5000), and the EDA sensor (5000) can be used to acquire the user's skin conductance information in a first cycle.

[0653] Also, for example, the device sensor unit 1400 of the wearable device 1000 includes a motion sensor, and the motion information of the user can be acquired in the second cycle using the motion sensor.

[0654] At this time, the first period and the second period may be the same or different.

[0655] The wearable device 1000 can transmit the user's skin conductance information to the user terminal 2000. For example, the wearable device 1000 can acquire the user's skin conductance information and simultaneously transmit it to the user terminal 2000.

[0656] Also, for example, the wearable device 1000 may transmit the acquired user skin conductance information set (Set) to the user terminal 2000 at a predetermined interval. At this time, the interval at which the wearable device 1000 acquires the user skin conductance information may be shorter than the interval at which the wearable device 1000 transmits the user skin conductance information.

[0657] The wearable device 1000 can transmit one or more types of user biometric information to the user terminal 2000. In one example, the biometric information transmitted to the user terminal 2000 is skin conductance information. In another example, the biometric information transmitted to the user terminal 2000 is skin conductance information and movement information.

[0658] The biological information transmitted by the wearable device 1000 may also be related to other information. In one example, the biological information transmitted by the wearable device 1000 is skin conductance information over time.

[0659] In another example, the biological information transmitted by the wearable device 1000 is time-related skin conductance information and time-related movement information. In yet another example, the biological information transmitted by the wearable device 1000 is in a form in which time, skin conductance information, and movement information are related to each other.

[0660] According to the embodiment, the monitoring server (3000) can calculate the monitoring data of FIG. 5 based on the acquired skin conductance information (S1300).

[0661] Specifically, the monitoring data may be calculated according to the method for calculating the monitoring data (S1300) of FIG.

[0662] The monitoring server 3000 may check the sleep period (S1310). For example, the sleep period may include a portion of the time point after the sleep start time (7310) in FIG.

[0663] Here, the method of checking the pause period (S1310) can be replaced with the method of calculating the pause period described with reference to FIG.

[0664] For example, the predetermined conditions in S1310 may include the conditions in steps S610 to S630 of FIG.

[0665] Specifically, the predetermined condition may include whether the variation of skin conductance data in one section is within a first range. The predetermined condition may also include whether there is a second section in which skin conductance data from the previous section decreases by more than the magnitude of the first range. The predetermined condition may also include whether the magnitude of one section is greater than a first value and less than a second value.

[0666] Therefore, a detailed description of the execution of the pause interval confirmation step (S1310) will be omitted.

[0667] According to the embodiment, the monitoring server (3000) may extract skin conductance information corresponding to the pause period (S1330).

[0668] For example, the monitoring server (3000) may extract (S1330) skin conductance information corresponding to one or more confirmed pause periods. Also, for example, the monitoring server (3000) may extract (S1330) skin conductance information corresponding to one or more confirmed pause periods included in the monitoring period.

[0669] According to the embodiment, the monitoring server 3000 may calculate monitoring data (S1350). At this time, the monitoring server 3000 may calculate monitoring data (S1350) based on the extracted skin conductance information.

[0670] At this time, the extracted skin conductance information may be extracted based on the skin conductance data in the pause periods of FIGS.

[0671] For example, the monitoring server (3000) can calculate the average value of multiple skin conductance data corresponding to each of multiple confirmed pause periods included in the monitoring period into the monitoring data.

[0672] Furthermore, for example, the monitoring server (3000) can calculate, as monitoring data, the median of median values ​​of a plurality of skin conductance data corresponding to each of a plurality of pause periods included in the monitoring period.

[0673] Also, for example, the monitoring server (3000) can exclude the maximum and minimum values ​​from the multiple skin conductance data corresponding to each of the multiple pause sections included in the monitoring period, and calculate the calculated values ​​of the remaining skin conductance data into the monitoring data.

[0674] According to this embodiment, the monitoring server 3000 may use reference data based on skin conductance data when determining whether the user has thyroid dysfunction. The basic description of the reference data overlaps with the content of Fig. 7, so a detailed description will be omitted. In this case, the method of calculating the reference data in Fig. 7 may be followed.

[0675] The monitoring server 3000 may receive (S2100) thyroid status information from the user terminal 2000. According to the embodiment, the user terminal 2000 may receive the user's thyroid status information through the terminal input unit 2100.

[0676] In this case, the thyroid status information is information about thyroid hormone levels obtained by a blood test or the like of the user, or information about the thyroid status obtained through a medical interview regarding the user's symptoms.

[0677] The user terminal (2000) can transmit the input thyroid status information to the monitoring server (3000). When the monitoring server (3000) receives the thyroid status information (S2100), it can calculate reference data (S2300).

[0678] The calculation period for the reference data is the period during which the user's thyroid function corresponds to "normal" according to the thyroid status information.

[0679] For example, if the input thyroid status information corresponds to the normal range, a predetermined period before and after the time point when the thyroid status information is input can be determined as the calculation period for the reference data.

[0680] A detailed description of the thyroid status information will be omitted as it overlaps with the content of FIG.

[0681] According to the embodiment, the user's thyroid function abnormality can be determined based on the reference data, and in this case, the reference data calculation method of FIG.

[0682] The monitoring server 3000 may determine a calculation period for the reference data (S2310). The calculation period for the reference data is the same as that described in FIG. 8, so a detailed description thereof will be omitted.

[0683] The monitoring server (3000) can check the pause period corresponding to the determined calculation period of the reference data (S2320).

[0684] Here, the method of checking the pause interval (S2320) can be replaced by the method of calculating the pause interval described with reference to Fig. 42. Therefore, redundant description of the specific operations performed in the step of checking the pause interval (S2320) will be omitted.

[0685] According to the embodiment, the monitoring server (3000) may extract skin conductance information corresponding to the pause period (S2330).

[0686] For example, the monitoring server (3000) may extract (S2330) skin conductance information corresponding to one or more confirmed pause periods. Also, for example, the monitoring server (3000) may extract (S2330) skin conductance information corresponding to one or more confirmed pause periods included in the monitoring period.

[0687] According to the embodiment, the monitoring server 3000 may calculate reference data (S2360). At this time, the monitoring server 3000 may calculate reference data (S2360) based on the extracted skin conductance information.

[0688] At this time, the extracted skin conductance information may be extracted based on the skin conductance data in the pause periods of FIGS.

[0689] For example, the monitoring server (3000) can calculate the average value of multiple skin conductance data corresponding to each of multiple confirmed pause periods included in the monitoring period as reference data.

[0690] Furthermore, for example, the monitoring server (3000) can calculate the median of the median values ​​of a plurality of skin conductance data corresponding to each of a plurality of pause periods included in the monitoring period as the reference data.

[0691] Also, for example, the monitoring server (3000) can exclude the maximum and minimum values ​​from the multiple skin conductance data corresponding to each of the multiple pause sections included in the monitoring period, and calculate the calculated values ​​of the remaining skin conductance data as reference data.

[0692] According to the embodiment, when the monitoring server (3000) receives thyroid status information that is outside the normal range, the reference data can be calculated by the reference data calculation method of FIG.

[0693] When the monitoring server 3000 receives thyroid status information that is out of the normal range, the basic explanation of the method for calculating the reference data overlaps with the content of FIG. 9, so detailed explanation will be omitted.

[0694] The monitoring server (3000) can calculate reference data by correcting the reference period data based on the received thyroid status information (S2350).

[0695] For example, the monitoring server (3000) can calculate how much ng / dL the user's hormone values ​​should increase / decrease to correspond to the normal range based on the received thyroid status information, and estimate the amount of change in skin conductance that will accompany the increase / decrease.

[0696] At this time, the monitoring server (3000) can calculate reference data by adding or subtracting the estimated skin conductance change amount to or from the period data (S2350). The monitoring server (3000) can store data necessary for correcting the reference period data.

[0697] For example, the monitoring server (3000) may store data relating to the correlation between hormone levels and skin conductance data of multiple users.

[0698] Specifically, the monitoring server (3000) may store statistical data on approximately how many microseconds of skin conductance data increase when hormone levels increase by approximately 0.1 ng / dL.

[0699] According to the embodiment, the monitoring server (3000) can compare (S1510) the monitoring data calculated from the skin conductance data with the reference data to determine (S1500) whether the user has an abnormality in thyroid function.

[0700] The method for determining whether the user has thyroid gland dysfunction (S1500) will be described in detail below since it overlaps with the method described in FIG.

[0701] According to the embodiment, the monitoring server (3000) can compare the monitoring data with the reference data. The comparison algorithm is the same as that shown in FIG. 11, so a detailed description thereof will be omitted.

[0702] FIG. 43 is a graph showing skin conductance data according to the wearing state of the wearable device (1000) according to the embodiment.

[0703] 43, the skin conductance data may vary depending on the state of the user wearing the wearable device 1000. The monitoring server 3000 can confirm or determine whether the user is wearing the wearable device 1000 or whether the user is wearing the wearable device 1000 correctly through the skin conductance data.

[0704] For example, the appearance of skin conductance data when the user is wearing the wearable device (1000) may differ from the appearance of skin conductance data when the user is not wearing the device.

[0705] Furthermore, for example, when a user is wearing the wearable device (1000), the skin conductance data when the user wears the wearable device (1000) correctly may differ from the skin conductance data when the user does not wear the device correctly (for example, when the EDA measurement electrodes are not in contact with the skin).

[0706] 43, between the first time point (t1) and the second time point (t2), the user may not be wearing the wearable device 1000. Alternatively, between the first time point (t1) and the second time point (t2), the user may be wearing the wearable device 1000 but not wearing it correctly.

[0707] For example, the first time point (t1) is the time point when the user takes off the wearable device (1000), and the second time point (t2) is the time point when the user puts on the wearable device (1000) again.

[0708] At this time, if the user removes the wearable device (1000), skin conductance data may not be acquired during the period when the device is not being worn.

[0709] For example, when the EDA electrode is in contact with the user's skin, the first time point (t1) is the time when the EDA electrode is no longer in contact, and when the EDA electrode is not in contact with the user's skin, the second time point (t2) is the time when the EDA electrode is brought into contact.

[0710] Also, for example, the first point in time (t1) is the point in time at which the moving average of the skin conductance data suddenly decreases (for example, by more than a certain value), and the second point in time (t2) is the point in time at which the moving average of the skin conductance data suddenly increases after the first point in time (t1).

[0711] According to an embodiment, the skin conductance data between the first time point (t1) and the second time point (t2) is less than a certain value (v1). For example, the skin conductance data between the first time point (t1) and the second time point (t2) can have a value less than 0.01 uS or less than 0.

[0712] According to another embodiment, the fluctuation of the skin conductance data between the first time point (t1) and the second time point (t2) is less than or equal to a predetermined value, for example, the slope or derivative of the skin conductance data between the first time point (t1) and the second time point (t2) is less than or equal to a predetermined value.

[0713] Also, for example, the variation in skin conductance data between a first time point (t1) and a second time point (t2) may be less than the variation in skin conductance data before the first time point (t1) and / or after the second time point (t2).

[0714] According to another embodiment, the average or median value of the skin conductance data between the first time point (t1) and the second time point (t2) is less than a certain value than the average or median value of the skin conductance data before the first time point (t1) and / or after the second time point (t2).

[0715] According to another embodiment, the average or median value of the skin conductance data between the first time point (t1) and the second time point (t2) may be less than the average or median value of the skin conductance data during the rest period.

[0716] Specifically, the average or median value of the skin conductance data between the first time point (t1) and the second time point (t2) may have a difference of about 2 uS to 5 uS from the average or median value of the skin conductance data during the rest period.

[0717] Additionally, the mean or median skin conductance data between the first time point (t1) and the second time point (t2) is within 1 uS.

[0718] At this time, the monitoring server (3000) may perform a step of calculating the average or median value of the skin conductance data between the first time point (t1) and the second time point (t2) and comparing it with the average or median value of the skin conductance data in the resting period.

[0719] At this time, if the skin conductance data value between the first time point (t1) and the second time point (t2) is smaller than the skin conductance data value in the rest period and is below a certain value (e.g., 1 uS, 0.1 uS, or 0.01 uS), the monitoring server (3000) can determine that the period between the first time point (t1) and the second time point (t2) is a state in which the wearable device (1000) is not worn or is not worn correctly.

[0720] According to an embodiment, before performing the step (S610) of extracting the first section of Figure 42, a step of first determining whether the wearable device (1000) is being worn or whether it is being worn correctly may be performed.

[0721] For example, the monitoring server (3000) may first determine whether the wearable device (1000) is being worn or whether it is being worn correctly before performing the step (S610) of extracting the first section.

[0722] If the wearable device 1000 is worn or worn correctly, steps S610 to S640 can be performed. However, if the wearable device 1000 is not worn or worn incorrectly, step S610 may not be performed.

[0723] As described above, the configuration and features of the present invention have been described based on embodiments and examples, but the present invention is not limited to the above description, and it is obvious to those skilled in the art to which the present invention pertains that various changes or modifications can be made within the scope of the concept of the present invention, and therefore, such changes or modifications fall within the scope of the claims of the present invention.

Claims

1. 1. A method for determining whether to output a warning message regarding a user's thyroid dysfunction, comprising: receiving medication information of the user from an external device, wherein the medication information includes at least one of prescription date and time of a medication related to thyroid function, a medication name, a medication type, a medication dosage, and a medication administration cycle; selecting a monitoring algorithm to be used for determining whether to output the warning message based on the medication taking information, wherein the monitoring algorithm is a first monitoring algorithm or a second monitoring algorithm different from the first monitoring algorithm; determining whether to output the warning message based on the selected monitoring algorithm; The step of determining whether to output a warning message comprises: determining, when the selected monitoring algorithm is the first monitoring algorithm, to output the warning message when the monitored heart rate is greater than a reference heart rate by a first critical value or more; and, when the selected monitoring algorithm is the second monitoring algorithm, to output the warning message when the monitored heart rate is smaller than the reference heart rate by a second critical value or more; The reference heart rate is calculated based on the user's thyroid hormone level and the user's heart rate, or based on the user's heart rate for multiple consecutive days; the monitored heart rate is calculated based on the heart rate of the user during a pause period; The pause section is selected based on information regarding the user's exercise state.

10. A method for determining whether to output a warning message regarding abnormal thyroid function in a user, comprising:

2. The step of selecting a monitoring algorithm comprises: classifying the user into a hyperthyroidism treatment group or a hypothyroidism treatment group based on the medication taking information; 2. The method for determining whether to output a warning message regarding abnormal thyroid function of a user according to claim 1.

3. The step of selecting a monitoring algorithm comprises: selecting the first monitoring algorithm if the user is classified into the hypothyroidism treatment group.

3. The method for determining whether to output a warning message regarding abnormal thyroid function of a user according to claim 2.

4. The step of selecting a monitoring algorithm comprises: selecting the second monitoring algorithm if the user is classified into a hyperthyroidism treatment group.

3. The method for determining whether to output a warning message regarding abnormal thyroid function of a user according to claim 2.

5. The step of determining whether to output a warning message comprises: If the selected monitoring algorithm is the first monitoring algorithm, when the monitored heart rate is smaller than the reference heart rate by the second critical value or more, checking whether a predetermined period has elapsed based on the prescription date and time; and a step of determining whether to output the warning message if the predetermined period has elapsed; 4. The method for determining whether to output a warning message regarding abnormal thyroid function of a user according to claim 3.

6. The step of determining whether to output a warning message comprises: If the selected monitoring algorithm is the second monitoring algorithm, when the monitored heart rate is greater than the reference heart rate by at least the first critical value, checking whether a predetermined period has elapsed based on the prescription date and time; and a step of determining whether to output the warning message if the predetermined period has elapsed; 5. The method for determining whether to output a warning message regarding abnormal thyroid function of a user according to claim 4.

7. before receiving the medication taking information from the external device, determining whether to output the warning message when the monitored heart rate is greater than the reference heart rate by at least the first critical value; and determining whether to output the warning message when the monitored heart rate is lower than the reference heart rate by at least the second critical value; 2. The method for determining whether to output a warning message regarding abnormal thyroid function of a user according to claim 1.

8. The pause section is determined based on a section in which the user's step count is 0 and continues for a predetermined period of time or more, or The pause section is determined based on a section in which the user's acceleration remains at 0 for a predetermined period of time or more.

2. The method for determining whether to output a warning message regarding abnormal thyroid function of a user according to claim 1.

9. further comprising calculating the reference heart rate; The step of calculating the reference heart rate includes: When the thyroid hormone values ​​are received, determining whether the received thyroid hormone values ​​are within a normal range; and If the thyroid hormone level is within a normal range, calculating the baseline heart rate based on resting heart rates on multiple consecutive days including the day the thyroid hormone level was tested.

2. The method for determining whether to output a warning message regarding abnormal thyroid function of a user according to claim 1.

10. The step of calculating the reference heart rate includes: If the thyroid hormone levels are outside the normal range, calculating a current heart rate based on resting heart rates on consecutive days including the day the thyroid hormone level was tested; and and estimating the reference heart rate when the user has normal thyroid function based on the thyroid hormone value and the calculated current heart rate. The method for determining whether to output a warning message regarding abnormal thyroid function of a user according to claim 9.

11. the step of selecting the monitoring algorithm is performed each time the medication taking information is received; the step of determining whether to output a warning message is performed daily after the step of selecting is performed; the step of determining whether to output a warning message is performed a greater number of times than the step of selecting a monitoring algorithm; 2. The method for determining whether to output a warning message regarding abnormal thyroid function of a user according to claim 1.

12. The method further includes determining whether to output a medication notification based on the medication cycle, the step of determining whether to output a medication notification is performed more times than the step of determining whether to output a warning message; The method for determining whether to output a warning message regarding abnormal thyroid function of a user according to claim 11.

13. The method further includes receiving heart rate information of the user every second period from the external device that measures the heart rate of the user every first period, The second period is longer than the first period.

2. The method for determining whether to output a warning message regarding abnormal thyroid function of a user according to claim 1.

14. If it is determined that the warning message should be output, the method further includes transmitting a signal to the external device to output the warning message through a display unit of the external device.

2. The method for determining whether to output a warning message regarding abnormal thyroid function of a user according to claim 1.

15. a program recorded thereon for carrying out the method according to any one of claims 1 to 14; A recording medium having stored thereon code that can be read and executed by a computer.

16. a communication unit that receives biological information acquired from a user of the wearable device from an external device; a control unit that selects a monitoring algorithm based on the user's medication information received through the communication unit, determines whether to output a warning message based on the selected monitoring algorithm, and controls a signal to be transmitted through the communication unit when it is determined that the warning message should be output, wherein: The medication information includes at least one of prescription date and time of medication related to thyroid function, medication name, medication type, medication dosage, and medication cycle; the monitoring algorithm is a first monitoring algorithm or a second monitoring algorithm; the first monitoring algorithm is an algorithm for determining whether to output the warning message when the monitored heart rate is greater than a reference heart rate by a first threshold value or more; the second monitoring algorithm is an algorithm for determining whether to output the warning message when the monitored heart rate is lower than the reference heart rate by a second threshold value or more; the reference heart rate is calculated based on the user's thyroid hormone level and the user's heart rate, or is calculated based on the user's heart rate for multiple consecutive days; the monitored heart rate is calculated based on the heart rate of the user during a pause period; The pause section is selected based on information regarding the user's exercise state. A monitoring server characterized by:

17. a communication unit that receives biological information acquired from a user of a wearable device from the wearable device; an input unit for receiving medication information of a user, wherein the medication information includes at least one of a prescription date and time of a medication related to thyroid function, a medication name, a medication type, a medication dosage, and a medication administration cycle; a control unit that selects a monitoring algorithm based on the medication information, determines whether to output a warning message based on the selected monitoring algorithm, and controls the output unit to output a warning message regarding thyroid dysfunction of the user when the output of the warning message is determined, wherein: the monitoring algorithm is a first monitoring algorithm or a second monitoring algorithm; the first monitoring algorithm is an algorithm for determining output of the warning message when the monitored heart rate is greater than the reference heart rate by a first threshold value or more; the second monitoring algorithm is an algorithm for determining whether to output the warning message when the monitored heart rate is lower than the reference heart rate by a second threshold value or more; the reference heart rate is calculated based on the user's thyroid hormone level and the user's heart rate, or is calculated based on the user's heart rate for multiple consecutive days; the monitored heart rate is calculated based on the heart rate of the user during a pause period; The pause section is selected based on information regarding the user's exercise state. A user terminal characterized by:

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