Thyroid function monitoring method associated with medicine intake and monitoring server and user terminal for performing the method

Abstract

To provide a method of determining whether to output a warning message related to an abnormality of a thyroid function of a user.SOLUTION: Provided is a monitoring method of an abnormality of a thyroid function, the method including: a step S4100 of receiving medicine intake information of a user from an external device; a step S4300 of selecting a monitoring algorithm used for determining whether to output a warning message on the basis of the medicine intake information; and a step S4500 of determining whether to output the warning message on the basis of 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 executes thyroid function monitoring associated with drug administration.

[0003] The present invention also relates to a user terminal that executes thyroid function monitoring associated with drug administration.

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

Background Art

[0005] According to statistical data, in the United States, 12% of the total population is known to experience thyroid dysfunction over their lifetime, and approximately 20 million Americans are known to suffer from diseases due to thyroid dysfunction. Not only in the United States, but thyroid dysfunction induces inconvenience and complications in the lives of many people worldwide, so it is a disease that requires attention and continuous monitoring.

[0006] However, currently, in order to monitor thyroid dysfunction, it is necessary to visit a hospital for a blood test, and the test timing is often delayed due to hospital clinic reservations. For this reason, systematic monitoring becomes impossible, and actually visiting the hospital for medical treatment itself causes a time loss for the patient, and there were quite a number of patients who did not undergo tests until symptoms due to thyroid dysfunction appeared.

[0007] Such a lack of thyroid dysfunction monitoring causes various negative phenomena such as worsening the patient's symptoms and increasing the burden of treatment costs. Along with this, it has been necessary to provide a continuous and easily usable thyroid function monitoring method for patients.

[0008] In addition, in the case of patients with thyroid dysfunction, the majority are undergoing treatment therapies through drug administration, but they are concerned about the appropriateness of the drug dosage they take and whether side effects will occur in their own bodies. Along with this, it is necessary to develop a thyroid function monitoring method that can also be provided to patients taking drugs.

Summary of the Invention

Problems to be Solved by the Invention

[0009] In an embodiment of the present invention, a monitoring method for detecting the occurrence of thyroid dysfunction as a side effect associated with a patient's drug administration is provided.

[0010] In an embodiment of the present invention, a monitoring method for preventing a patient from taking an excessive amount of drugs and inducing a hospital visit is provided.

[0011] In an embodiment of the present invention, a method for predicting a user's thyroid dysfunction based on skin conductance data obtained through a wearable device is provided.

Means for Solving the Problems

[0012] In a method for determining whether to output a warning message regarding a user's thyroid dysfunction according to an embodiment of the present invention, a step of receiving drug administration information of the user from an external device, where the drug administration information includes at least one of the prescription date and time of a drug related to thyroid function, drug name, drug type, drug dosage, and drug administration cycle; a step of selecting a monitoring algorithm used to determine whether to output the warning message based on the drug administration information, where the monitoring algorithm is a first monitoring algorithm or a second monitoring algorithm different from the first monitoring algorithm; and a step of determining whether to output the warning message based on the selected monitoring algorithm, The step of determining whether to output the warning message includes, when the selected monitoring algorithm is the first monitoring algorithm, determining to output the warning message when the monitored heart rate is greater than or equal to a first critical value than the reference heart rate, and, when the selected monitoring algorithm is the second monitoring algorithm, determining to output the warning message when the monitored heart rate is less than or equal to a second critical value than the reference heart rate, The reference heart rate is calculated based on the thyroid hormone value of the user and the heart rate of the user, or is calculated based on the heart rate of the user for a plurality of consecutive days, and the monitored heart rate is calculated based on the heart rate of the user in the rest period, The rest period is selected based on information about the exercise state of the user.

Advantages 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 a patient's drug administration so that a patient who is worried about the occurrence of side effects associated with drug administration can continue treatment in a stable state.

[0014] According to the present invention, there is provided a monitoring method for monitoring a patient's continuous administration of a prescribed drug despite being treated due to the prolongation of the patient's drug administration period, preventing the patient from overdosing on the drug, and inducing the patient to visit the hospital.

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

[0016] The effects of the present invention are not limited to the effects described above, and for effects not mentioned, those skilled in the art can clearly understand them from this specification and the attached drawings.

Brief Description of the Drawings

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[0018] The above-described objects, features, and advantages of the present invention will become more apparent through the following detailed description with respect to the accompanying drawings. However, the present invention can be variously modified and can have various embodiments. Hereinafter, specific embodiments will be given and illustrated in the drawings and described in detail.

[0019] In the drawings, the thicknesses of layers and regions are exaggerated for clarity of explanation. When an element or layer is described as "on" another element or layer, it includes not only directly on top of another element or layer but also cases where other layers or other elements intervene in between. The same reference numbers described throughout the specification generally indicate the same components. Also, the same components of functions within the scope of the same idea shown in the drawings of each embodiment are described using the same reference signs.

[0020] When a detailed description of well-known functions or configurations related to the present invention is determined to be unnecessary for the gist of the present invention, the detailed description thereof is omitted. Also, the numbers (for example, first, second, etc.) used in the description process of this specification are merely identification symbols for distinguishing one component from another component.

[0021] In addition, in the following description, the suffixes "module" and "section" for components are given in consideration of the ease of preparing the specification, and they do not have meanings or roles that are distinguishable 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, including the steps of receiving the user's drug administration information from an external device, where the drug administration information includes at least one of the prescription date and time of a drug related to thyroid function, the drug name, the drug type, the drug volume, and the drug administration cycle; selecting a monitoring algorithm used to determine whether to output the warning message based on the drug administration information, where 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, when the selected monitoring algorithm is the first monitoring algorithm, determining to output the warning message when the monitored heart rate is greater than or equal to a first critical value than the reference heart rate, and when the selected monitoring algorithm is the second monitoring algorithm, determining to output the warning message when the monitored heart rate is less than or equal to a second critical value than the reference heart rate. Further, the reference heart rate is calculated based on the user's thyroid hormone value and the user's heart rate, or calculated based on the user's heart rate for a plurality of consecutive days, the monitored heart rate is calculated based on the user's heart rate during a rest period, and the rest 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 the user's thyroid function abnormality, the step of selecting the monitoring algorithm includes classifying the user into a hyperthyroidism treatment group or a hypothyroidism treatment group based on the drug-taking information.

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

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

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

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

[0028] In a method for determining whether to output a warning message regarding the user's thyroid function abnormality, before receiving the drug-taking information from the external device, when the monitored heart rate is greater than or equal to a first critical value than the reference heart rate, determining whether to output the warning message, and when the monitored heart rate is less than or equal to a second critical value than the reference heart rate, determining whether to output the warning message.

[0029] In a method for determining whether to output a warning message regarding the user's thyroid function abnormality, the rest period is determined based on a period that continues for a predetermined time or more in a state where the number of steps of the user is 0, or the rest period is determined based on a period that continues for a predetermined time or more in a state where the acceleration of the user is 0.

[0030] In a method for determining whether to output a warning message regarding the user's thyroid function abnormality, further including a step of calculating the reference heart rate, the step of calculating the reference heart rate includes a step of checking whether the received thyroid hormone value is within the normal range when the thyroid hormone value is received, and when the thyroid hormone value is within the normal range, calculating the reference heart rate based on the heart rates during the rest periods of a plurality of consecutive days including the test day of the thyroid hormone value.

[0031] In a method for determining whether to output a warning message regarding the user's thyroid function abnormality, the step of calculating the reference heart rate includes a step of calculating the current heart rate based on the heart rates during the rest periods of a plurality of consecutive days including the test day of the thyroid hormone value when the thyroid hormone value is out of the normal range, and a step of estimating the reference heart rate when the user's thyroid function is normal based on the thyroid hormone value and the calculated current heart rate.

[0032] In a method for determining whether to output a warning message regarding the user's thyroid function abnormality, the step of selecting the monitoring algorithm is executed each time the drug administration information is received, the step of determining whether to output the warning message is executed daily after the execution of the step of selection, and the step of determining whether to output the warning message is executed more times than the step of selecting the monitoring algorithm.

[0033] In a method for determining whether to output a warning message regarding the user's thyroid function abnormality, the method further includes a step of determining whether to output a notification of drug administration based on the drug administration cycle, and the step of determining whether to output the notification of drug administration is executed more times than the step of determining whether to output the warning message.

[0034] In a method for determining whether to output a warning message regarding the user's thyroid function abnormality, the method further includes a step of receiving the user's heart rate information every second cycle from the external device that measures the user's heart rate every first cycle, and the second cycle is longer than the first cycle.

[0035] In a method for determining whether to output a warning message regarding the user's thyroid function abnormality, if it is determined to output the warning message, the method further includes a step of transmitting a signal to the external device so as to output the warning message through a display unit of the external device.

[0036] According to an embodiment of the present invention, there is provided a recording medium storing a computer-readable code, and a program is recorded thereon for executing the method according to any one of the above items.

[0037] According to an embodiment of the present invention, there is provided a monitoring server, including: a communication unit that receives biometric information acquired from a user of a wearable device from an external device; and a control unit that selects a monitoring algorithm based on the drug administration 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 transfer a signal through the communication unit when the output of the warning message is determined. Here, the drug administration information includes at least one of a prescription date and time of a drug related to thyroid function, a drug name, a drug type, a drug volume, and a drug administration cycle. The monitoring algorithm is a first monitoring algorithm or a second monitoring algorithm. The first monitoring algorithm is an algorithm that determines the output of the warning message when the monitored heart rate is greater than or equal to a first critical value than the reference heart rate. The second monitoring algorithm is an algorithm that determines the output of the warning message when the monitored heart rate is less than or equal to a second critical value than the reference heart rate. The reference heart rate is calculated based on the thyroid hormone value of the user and the heart rate of the user, or is calculated based on the heart rate of the user for consecutive multiple days. The monitored heart rate is calculated based on the heart rate of the user in a rest period, and the rest period is selected based on information on the exercise state of the user.

[0038] According to an embodiment of the present invention, there is provided a user terminal, including: a communication unit that receives biometric information acquired from a user of a wearable device from the wearable device; an input unit that receives the user's drug administration information, where the drug administration information includes at least one of a prescription date and time of a drug related to thyroid function, a drug name, a drug type, a drug volume, and a drug administration cycle; a control unit that selects a monitoring algorithm based on the drug administration information and determines whether to output a warning message based on the selected monitoring algorithm, and when the output of the warning message is determined, controls to output a warning message related to abnormal thyroid function of the user through an output unit, where the monitoring algorithm is a first monitoring algorithm or a second monitoring algorithm, the first monitoring algorithm is an algorithm that determines the output of the warning message when the monitored heart rate is greater than or equal to a first critical value than a reference heart rate, the second monitoring algorithm is an algorithm that determines the output of the warning message when the monitored heart rate is less than or equal to a second critical value than the reference heart rate, the reference heart rate is calculated based on the user's thyroid hormone value and the user's heart rate, or is calculated based on the user's heart rate for a continuous plurality of days, the monitored heart rate is calculated based on the user's heart rate in a rest period, and the rest period is selected based on information about the user's exercise state.

[0039] According to an embodiment of the present invention, there is provided a method for predicting thyroid function abnormality of a user by using a wearable device worn on a part of the user's body. The method includes the steps of: obtaining skin conductance data of the user through the wearable device that measures the skin conductance of the user; extracting a rest period during which the skin conductance data changes within a critical range during a predetermined period based on the skin conductance data; and comparing a reference skin conductance with a monitored skin conductance to predict thyroid function abnormality of the user. The rest period is a period that starts after a decreasing period during which the skin conductance data decreases by a magnitude greater than or equal to the magnitude of the critical range. The reference skin conductance is calculated based on the skin conductance data during the rest period during a reference period, and the monitored skin conductance is calculated based on the skin conductance data during the rest period during a monitoring period. Here, the monitoring period is a period for determining whether the thyroid function of the user is abnormal, and the reference period and the monitoring period do not overlap with each other.

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

[0041] Here, the measurement period 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, based on the result of the predicting step, it includes the step of transmitting the message to the wearable device, and the message includes a first warning message when the monitored skin conductance is greater than a first value than the reference skin conductance, and includes a second warning message when the monitored skin conductance is less than a second value than the reference skin conductance, the first warning message may include information related to hyperthyroidism, and the second warning message may include information related to hypothyroidism.

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

[0045] Here, if the output frequency of the first warning message or the second warning message exceeds a predetermined number during a certain period, the message may include a comment suggesting to seek the opinion of an expert.

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

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

[0048] Here, the monitored skin conductance can be obtained based on at least one of the rest periods.

[0049] Here, the reference skin conductance can be obtained based on at least one of the rest periods when the thyroid function of the user is normal.

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

[0051] Here, the skin conductance data during the rest period may be below a predetermined value.

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

[0053] Here, the change frequency of the skin conductance data before the decreasing section is larger than the change frequency of the skin conductance data after the decreasing section, and the change frequency can be based on the differential value of the skin conductance data.

[0054] Here, the change frequency of the skin conductance before the decreasing section is larger than the change frequency of the skin conductance during the decreasing section, and the change frequency can be based on the differential value of the skin conductance data.

[0055] Here, the change frequency of the skin conductance data during the wearing period is larger than the change frequency 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 change frequency can be based on the differential value of the skin conductance data.

[0056] Here, the difference in skin conductance before and after the wearing period is larger than the difference in skin conductance before and after the rest period. 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 earlier than the second time point included in the monitoring period.

[0058] In addition, a program for executing the method according to any one of the above items is recorded, and a recording medium in which computer-readable code is stored can be provided.

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

[0060] The thyroid function monitoring system (100) is a system that senses the user's biological signals through the 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 an embodiment of the present invention.

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

[0063] However, the components illustrated in FIG. 1 are not essential components, and the thyroid function monitoring system (100) can have more components or fewer components than this.

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

[0065] The wearable device (1000) can sense the user's biological information. As an 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 still another example, the wearable device (1000) can sense the user's heart rate information and the user's temperature information. As still another example, the wearable device (1000) can sense the user's skin conductance information. Of course, it is not limited to the examples listed in this specification, and the wearable device (1000) can sense one or more biological information corresponding to the user's biological signals.

[0066] The wearable device (1000) can transmit the sensed biological information to the user terminal (2000) and / or the monitoring server (3000). As an 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 an example, the wearable device (1000) can transmit the user's heart rate information to the user terminal (2000). As another example, the wearable device (1000) can transmit the user's movement information to the user terminal (2000). As still another example, the wearable device (1000) can transmit the user's temperature to the user terminal (2000). As yet another example, the wearable device (1000) can 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 regarding the external environment.

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

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

[0071] The wearable device (1000) can transmit the sensed first biological information to the user terminal (2000) in association with other second biological information different from the first biological information.

[0072] As an example, the wearable device (1000) can transmit to the user terminal (2000) in the form of a dataset in which multiple types of biological signals (for example, heart rate information, temperature information) are related over time.

[0073] The user terminal (2000) can execute a determined operation based on the biological information received from the wearable device (1000).

[0074] As an example, if information on the user's thyroid state is input through the terminal input unit (2100), the user terminal (2000) can transmit the information on the user's thyroid state to the monitoring server (3000).

[0075] As another example, the user terminal (2000) can transmit the received biological information to the monitoring server (3000) according to determined conditions. As a specific example, if biological information is received, the user terminal (2000) can transmit it to the monitoring server (3000). As another specific example, the user terminal (2000) can store the received biological information and transmit the biological information stored at a determined period to the monitoring server (3000).

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

[0077] The monitoring server (3000) can transmit the 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 to the user terminal (2000) based on the result of thyroid function monitoring. The user terminal (2000) can cause a warning regarding the user's thyroid function to be output 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 schematically described.

[0080] In FIG. 1, a schematic diagram of a system in which the wearable device (1000) and the user terminal (2000) are communicably connected and the user terminal (2000) and the monitoring server (3000) are communicably connected is shown, but the connection relationship between the respective components can be implemented with modifications.

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

[0082] In addition, although FIG. 1 shows a case where the number of user terminals (2000) is one, the monitoring server (3000) can be implemented in a form connected to each user terminal (2000) of a plurality of users. Also, one user can use the thyroid function monitoring system (100) using one user terminal (2000), one user can also use the thyroid function monitoring system (100) using a plurality of user terminals (2000), and a plurality of users can also 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 the embodiments of the present invention will be specifically described.

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

[0085] As shown in Figure 2, the wearable device (1000) can 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 shown in Figure 2 are not essential, and the wearable device (1000) can have more components or fewer components than this.

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

[0087] The device input unit (1100) can be implemented by commonly used user input devices. By way of example, the device input unit (1100) includes traditional forms of keypads, keyboards, mice, as well as touch sensors that sense a user's touch, microphones that receive voice signal inputs, cameras that recognize gestures through video recognition, proximity sensors such as illuminance sensors and infrared sensors that sense the user's approach, motion sensors that recognize the user's movements through acceleration sensors and gyro sensors, and / or various other forms of input means that sense or receive various forms of user input.

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

[0089] Alternatively, instead of autonomously sensing the user's input, the device input unit (1100) may be embodied in the form of an input interface (such as a USB port, a PS / 2 port, etc.) that connects an external input device for receiving the user's input to the wearable device (1000).

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

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

[0092] The device output unit (1200) can be embodied by a display that outputs video, a speaker that outputs sound, haptics that generates vibrations, and / or other various forms of output means.

[0093] Here, "display" means a broad - sense video display device that includes all diverse forms capable of executing a liquid crystal display (LCD), a light - emitting diode (LED) display, an organic light - emitting diode (OLED) display, a flat panel display (FPD), a transparent display, a curved display, a flexible display, a three - dimensional display (3D display), a holographic display, a projector, and / or other video output functions.

[0094] Alternatively, the device output unit (1200) can also be embodied in the form of an output interface (such as a USB port, a PS / 2 port, etc.) that connects an external output device for outputting information to the wearable device (1000) instead of autonomously outputting information externally by itself.

[0095] The device output unit (1200) may also be in an integrated form with the device input unit (1100). As an example, when the device output unit (1200) is a display, the device output unit (1200) is in the form of a touch display integrally configured with the in - touch sensor that is the device input unit (1100).

[0096] The device communication unit (1300) can play a role in enabling the wearable device (1000) to transmit / receive data with 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) can include one or more communicable modules. The device communication unit (1300) can include a module that enables communication with an external device through a wired method. Or, the device communication unit (1300) can include a module that enables communication with an external device through a wireless method. Or, the device communication unit (1300) can include a module that enables communication with an external device through a wired method and a module that enables communication with an external device through a wireless method.

[0098] Looking at specific examples, the device communication unit (1300) can be composed of a wired communication module connected to the Internet or the like through a LAN (Local Area Network), a mobile communication module such as LTE (Long Term Evolution) that connects to a mobile communication network through a mobile communication base station and transmits / receives data, a short-range communication module that uses a communication method in the WLAN (Wireless Local Area Network) series such as Wi-Fi or a communication method in the WPAN (Wireless Personal Area Network) series such as Bluetooth (registered trademark) or Zigbee (registered trademark), a satellite communication module that uses a GNSS (Global Navigation Satellite System) such as GPS (Global Positioning System), or a combination of these.

[0099] The device sensor unit (1400) can execute 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 the heart rate information of the user.

[0100] The device sensor unit (1400) can include one or more modules capable of acquiring the user's biological information. As a specific example, the device sensor unit (1400) includes a PPG sensor module that acquires information related to the heartbeat (e.g., heart rate) using an optical method, an ECG sensor module that acquires information related to the heartbeat (e.g., electrocardiogram) through an electrical method, a temperature sensor module that acquires information related to temperature in a contact / non-contact manner, a motion sensor module that acquires information related to the user's movement using an acceleration sensor, a gyro sensor, and / or a step detection sensor, etc., and an EDA sensor module that acquires information related to the activity of the sympathetic nervous system using skin conductance, or can be composed of a combination thereof. Of course, it is not limited to this, and the device sensor unit (1400) can be embodied by various sensors for acquiring the user's biological information.

[0101] According to an example of this embodiment, the device sensor unit (1400) can execute a function of acquiring information related to the external environment of the wearable device (1000). As an example, the device sensor unit (1400) can 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 operation of the wearable device (1000). The device memory unit (1500) can store the information acquired by the wearable device (1000).

[0103] As an example, the biological information acquired by the device sensor unit (1400) is stored in the device memory unit (1500). As another example, the device memory unit (1500) can store an operation program (OS: Operating System) for driving the wearable device (1000), various programs driven or used by the wearable device (1000) to execute thyroid function monitoring, and various data related to the 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, flash memory (1400, flash memory), a read - only memory (ROM), a random access memory (RAM), or cloud storage. Of course, it is not limited to this, and the device memory unit (1500) can be embodied by various modules for storing data.

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

[0106] The device control unit (1600) can execute a function of comprehensively controlling the overall operation of the wearable device (1000). The device control unit (1600) can execute calculations and processing of various information to control the operations of the components of the terminal.

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

[0108] According to an example, the device control unit (1600) can control the device sensor unit (1400) to sense the user's biological signal.

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

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

[0111] The wearable device (1000) according to this embodiment may be a wearable wristband that is worn on the user's wrist to acquire biological information, or a wearable sock that is worn on the user's foot in the form of a sock to acquire biological information, or a wearable ring that is worn on the user's finger to acquire biological information, or a wearable patch that is attached to the user's skin to acquire biological information, or a wearable headband that is worn on the user's head to acquire biological information, or a wearable device that is worn on the user's ear in the form of an earring or hung in the form of earphones, or a wearable lens that is inserted into the user's eye. Of course, it is not limited to the examples listed in this specification and can be implemented 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 FIG. 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 FIG. 3 are not essential, and the user terminal (2000) can have more or fewer components than this.

[0114] Similar to the device input unit (1100) of the wearable device (1000) described above, the terminal input unit (2100) can execute a function of acquiring information from a user.

[0115] Similar to the device output unit (1200) of the wearable device (1000) described above, the terminal output unit (2200) can execute a function of outputting information so that a user can confirm it.

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

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

[0118] Similar to the device control unit (1600) of the wearable device (1000) described above, the terminal control unit (2500) can execute a function of comprehensively controlling the overall operation of the user terminal (2000).

[0119] According to an embodiment, the terminal control unit (2500) can process information based on the user input entered by the terminal input unit (2100), and control the processed information to be transmitted to the monitoring server (3000) through the terminal communication unit (2300). Specifically speaking, the terminal control unit (2500) can acquire thyroid state information and / or drug intake information through the terminal input unit (2100), process the corresponding information to conform to the communication format with the monitoring server (3000), and transmit it through the terminal communication unit (2300).

[0120] According to another embodiment, the terminal control unit (2500) can process the information received from the monitoring server (3000) through the terminal communication unit (2300), and provide it to the user through the terminal output unit (2200). Specifically speaking, the terminal control unit (2500) can receive the information associated with the result of thyroid function monitoring through the terminal communication unit (2300), and make the judgment result for thyroid function abnormality be output through the terminal output unit (2200) in order to warn the user's thyroid function.

[0121] Hereinafter, 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, in the terminal input unit (2100), the terminal output unit (2200), the terminal communication unit (2300), the terminal memory unit (2400) and the terminal control unit (2500), the description of overlapping modules and the like is omitted.

[0123] The user terminal (2000) according to the embodiments of the present embodiment can include not only mobile terminals such as mobile phones, smartphones, tablet PCs, laptops, PDAs (Personal Digital Assistants), PMPs (Portable Multimedia Players), and navigations, but also fixed terminals such as digital TVs, desktop computers, and kiosks. More generally, anything that can be connected to other electronic devices and / or servers through a network to 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 the present embodiment.

[0125] As shown in FIG. 4, the monitoring server (3000) can include a server communication unit (3100), a server database (3200), and a server control unit (3300). However, the components illustrated in FIG. 4 are not essential, and the monitoring server (3000) can have more components or fewer components. Also, each component of the monitoring server (3000) may be physically included in one server or may be a distributed server distributed according to each 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, regarding the server communication unit (3100), the description of duplicate modules and the like will be omitted.

[0127] According to an example of the present embodiment, the server communication unit (3100) can receive information regarding the biological information of the user of the wearable device (1000) from the user terminal (2000). According to another example of the present embodiment, the server communication unit (3100) can receive the thyroid state information of the user obtained 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, regarding the server database (3200), the description of duplicate modules and the like will be omitted.

[0129] According to an example of the present embodiment, the server database (3200) can store a monitoring algorithm, user information, and / or the biological information of the user used for 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 execute a function of comprehensively controlling the overall operation of the monitoring server (3000). Therefore, regarding the server control unit (3300), the description of duplicate modules and the like will be omitted.

[0131] According to an example, the server control unit (3300) can predict the thyroid function of the user based on the biological information of the user received through the server communication unit (3100) by 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 state 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) can select a specific monitoring algorithm from the monitoring algorithms stored in the server database (3200) based on the drug administration information received through the server communication unit (3100). The server control unit (3300) can perform thyroid function monitoring based on the selected monitoring algorithm.

[0133] Hereinafter, unless otherwise specified, the operation of the monitoring server (3000) can be interpreted as being executed under the control of the server control unit (3300).

[0134] The monitoring server (3000) according to the embodiment of the present embodiment can include computer hardware or other programs on which a thyroid function monitoring program is executed, and / or a computer program that provides services to electronic devices.

[0135] The monitoring server (3000) according to the embodiment of the present embodiment can manage or control the network to which the external terminal and the server are connected, and share software resources such as data used for thyroid function monitoring. The monitoring server (3000) can be physically a single server, or a distributed server in which multiple servers distribute their processing capacities and roles.

[0136] Hereinafter, the operation of the thyroid function monitoring system (100) according to the embodiment of the present embodiment will be specifically described.

[0137] In the specific description of the operation of the thyroid function monitoring system (100), unless otherwise mentioned, the thyroid function monitoring system (100) includes a wearable device (1000), a user terminal (2000), and a monitoring server (3000). 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 will be described.

[0138] However, this is only a specific description based on one embodiment for the convenience of explanation, and the scope of the rights of the present invention is not limited by the embodiments described in this specification. The scope of the rights of the present invention is determined by the interpretation principle of the claims.

[0139] <Operation of the thyroid function monitoring system (100)> 1. Operation of thyroid function abnormality monitoring (S100) 1-1 Monitoring of thyroid function abnormality (S100) The thyroid function monitoring system (100) of this embodiment can predict the thyroid function abnormality of a user based on the user's biological signal. According to the embodiment of this embodiment, the thyroid function monitoring system (100) can determine the thyroid function abnormality based on the user's heart rate information.

[0140] Here, "thyroid function abnormality" means the onset of any one of hyperthyroidism, hypothyroidism, and thyrotoxicosis.

[0141] Here, "predicting thyroid function abnormality" means obtaining the result information of performing thyroid function abnormality monitoring based on the user's biological information. In this specification, it may also be used interchangeably with the judgment of thyroid function abnormality, the diagnosis of thyroid function abnormality, etc.

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

[0143] As shown in FIG. 5, when biological information is acquired (S1100), calculation of monitoring data (S1300) can be performed to determine an abnormality in the thyroid function of the user (S1500). According to the embodiment, the above steps S1100, S1300, and S1500 can be executed by the monitoring server (3000).

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

[0145] The acquisition of the biological information of the user by the wearable device (1000) can be executed at regular intervals.

[0146] As an example, the device sensor unit (1400) of the wearable device (1000) includes a PPG sensor, and the wearable device (1000) can acquire the heart rate information of the user using the PPG sensor in the first period. The device sensor unit (1400) of the wearable device (1000) includes a motion sensor, and the wearable device (1000) can acquire the motion information of the user using the motion sensor in the second period. The first period and the second period may be the same. Of course, the first period and the second period may also be different.

[0147] The wearable device (1000) can transmit the user's biological information to the user terminal (2000). As an example, the wearable device (1000) can transmit the user's biological information to the user terminal (2000) while acquiring it. As another example, the wearable device (1000) can transmit a set of the acquired user's biological information to the user terminal (2000) at a determined period. At this time, the period in which the wearable device (1000) acquires the user's biological information may be shorter than the period in which the user's biological information is transmitted.

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

[0149] The biological information transmitted by the wearable device (1000) may be related to other information. As an example, the biological information transmitted by the wearable device (1000) may be heart rate information related to time. As another example, the biological information transmitted by the wearable device (1000) may be heart rate information related to time and exercise information related to time. As still another example, the biological information transmitted by the wearable device (1000) may be in a form related to time, heart rate information, and exercise information.

[0150] The user terminal (2000) can transmit the received biological information to the monitoring server (3000). As an example, the user terminal (2000) can transmit the user's biological information to the monitoring server (3000) while receiving it. As another example, the user terminal (2000) can transmit a set of the received biological information of multiple users to the monitoring server (3000) at a determined period. In this case, the period in which the user terminal (2000) acquires the set of biological information may be shorter than the period in which the set of biological information is transmitted.

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

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

[0153] Here, the "monitoring data" is the state data of the user for the monitoring period of the object of thyroid function judgment in one thyroid function abnormality judgment (S1500) operation.

[0154] As an example, the "monitoring data" can be calculated based on the state data of the user during the monitoring period (for example, a plurality of consecutive days before the time point of thyroid function abnormality judgment (S1500)). As another example, the "monitoring data" can be calculated based on the state data of the user for one or more intervals (for example, rest intervals) that satisfy the determined conditions during the monitoring period.

[0155] According to the examples of the present embodiment, the monitoring data can be calculated based on the state data of the user when the user is in a stable state during the monitoring period. According to other examples of the present embodiment, the monitoring data can be calculated based on the state data of the user when the user is in a resting state during the monitoring period.

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

[0157] The method (S1300) for calculating monitoring data can include confirmation of a rest period (S1310), extraction of biological information corresponding to the rest period (S1330), and calculation of monitoring data (S1350). According to an example, the above steps S1310, S1330, and S1350 can be executed by a monitoring server (3000).

[0158] The monitoring server (3000) can confirm a rest period (S1310). The server control unit (3300) can confirm a rest period (S1310). The monitoring server (3000) can confirm at least one rest period during a determined period (for example, a monitoring period) (S1310). As an example, the monitoring period may be one day (24 hours). As another example, the determined period may be multiple days (for example, 5 days). As still another example, the monitoring period may be shorter than one day.

[0159] The monitoring server (3000) can confirm a rest period corresponding to a determined condition during a determined period (S1310). As an example, the monitoring server (3000) can confirm a rest period based on the user's motion information during the monitoring period (S1310).

[0160] The determined condition for being confirmed as the rest period may be related to the user's motion information. Specifically, for example, the rest period can be determined as a period in which a predetermined time (for example, 5 minutes) has elapsed in a state where the user is not moving, based on the user's motion information. As another example, the determined condition may be related to the user's skin conductance information. Specifically, for example, the rest period can be determined as a period in which the user is determined to be sleeping, based on the user's skin conductance information.

[0161] There may be a plurality of rest periods confirmed in the S1310 stage. The monitoring period may include a plurality of rest periods. The plurality of rest periods may be discontinuous with each other. As an example, there may be a period in which the movement of the user is detected between one rest period and another rest period. In other words, the monitoring period may include one rest period, another rest period, and a period in which the movement of the user is detected.

[0162] The monitoring server (3000) can extract biological information corresponding to the rest period (S1330). The server control unit (3300) can extract biological information corresponding to the rest period (S1330). The monitoring server (3000) can extract biological information corresponding to one or more confirmed rest periods (S1330). The monitoring server (3000) can extract biological information corresponding to one or more confirmed rest periods included in the monitoring period (S1330).

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

[0164] According to an embodiment of the present embodiment, the monitoring server (3000) can extract a plurality of heart rate information corresponding to each of the plurality of confirmed rest periods included in the monitoring period.

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

[0166] In one example, the monitoring server (3000) can calculate, as monitoring data, the average value of a plurality of heart rate information corresponding to each of a plurality of confirmed rest intervals included in the monitoring period. In another example, the monitoring server (3000) can calculate, as monitoring data, the median value of the median values of a plurality of heart rate information corresponding to each of a plurality of rest intervals included in the monitoring period. In still another example, the monitoring server (3000) can calculate, as monitoring data, the calculated value of the heart rate information excluding the maximum value and the minimum value among a plurality of heart rate information corresponding to each of a plurality of rest intervals 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 the present embodiment can use reference data. Here, the "reference data" means reference data to be compared with the monitoring data when performing the determination operation of thyroid function abnormality.

[0168] According to an embodiment of the present embodiment, the reference data can be determined based on the biological information of the user when the state information regarding the thyroid function of the user corresponds to normal. According to another embodiment of the present embodiment, the reference data can be determined based on the biological information of the user in the corresponding section when the biological information of the user corresponds to the predetermined conditions.

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

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

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

[0172] According to an embodiment of the present embodiment, the user terminal (2000) can receive an input of the user's thyroid state information through the terminal input unit (2100). At this time, the thyroid state information is information regarding thyroid hormone numerical values obtained by, for example, a blood test of the user. Alternatively, the thyroid state information is information regarding the thyroid state obtained by an interview regarding the user's symptoms.

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

[0174] When the monitoring server (3000) receives (S2100) thyroid state information, it can calculate (S2300) reference data. When the server control unit (3300) receives the thyroid state information, it can calculate (S2300) reference data.

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

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

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

[0178] The monitoring server (3000) can determine the calculation period of the reference data (S2310) based on the received thyroid state information. According to this specification, the calculation period of the reference data may also be expressed interchangeably with the reference period or the reference period.

[0179] The calculation period of the reference data is the period when the user's thyroid function corresponds to "normal" according to the thyroid state information.

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

[0181] As another example, when there are multiple inputs of thyroid state information, a predetermined period can be determined as the calculation period of the reference data before and after the time point when the thyroid state information corresponding to normal is input. Specifically speaking, for example, if thyroid state information regarding normal thyroid hormone values is input on March 1st, thyroid state information regarding abnormal thyroid hormone values is input on June 1st, and thyroid state information regarding normal thyroid hormone values is input on September 1st, a predetermined period based on March 1st (for example, a 5-day period from February 27th to March 3rd) and a predetermined period based on September 1st (for example, a 5-day period from August 30th to September 3rd) can be determined as the calculation period of the reference data.

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

[0183] The "rest period in step S2320" can be determined according to the conditions corresponding to the conditions for checking the rest period in the calculation of the monitoring data. As an example, if in step S1310, the rest period is determined to be the period when a predetermined time (for example, 5 minutes) has elapsed in a state where the user is not moving, then in S2320, the rest period can also be determined to be the period when a predetermined time (for example, 5 minutes) has elapsed in a state where the user is not moving.

[0184] There may be a plurality of rest intervals confirmed through the S2320 stage. The calculation period of the reference data may include a plurality of rest intervals. The plurality of rest intervals may be discontinuous with each other. As an example, there may be an interval in which the movement of the user is sensed between one rest interval and another rest interval. In other words, the calculation period of the reference data may include one rest interval, another rest interval, and an interval in which the movement of the user can be sensed.

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

[0186] The monitoring server (3000) can extract heart rate information corresponding to the confirmed rest intervals included in the calculation period of the reference data. Alternatively, the monitoring server (3000) can extract temperature information corresponding to the confirmed rest intervals included in the calculation period of the reference data. Alternatively, the monitoring server (3000) can extract skin conductance information corresponding to the confirmed rest intervals included in the calculation period of the reference data.

[0187] According to the embodiment of the present embodiment, the monitoring server (3000) can extract a plurality of heart rate information corresponding to each of the plurality of confirmed rest intervals included in the calculation period of the reference data.

[0188] "Biological information in the S2330 section" can correspond to the biological information extracted by calculating the monitoring data. As an example, if the heart rate is extracted as biological information at the S1330 stage, the heart rate can be extracted as biological information at the S2330 stage. As another example, if the skin conductance is extracted as biological information at the S1330 stage, the skin conductance can be extracted as biological information at the S2330 stage.

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

[0190] As an example, the monitoring server (3000) can calculate, as reference data, the average value of a plurality of heart rate information corresponding to each of a plurality of confirmed rest intervals included in the calculation period of the reference data. As another example, the monitoring server (3000) can calculate, as reference data, the median of the medians of a plurality of heart rate information corresponding to each of a plurality of rest intervals included in the calculation period of the reference data. As still another example, the monitoring server (3000) can calculate, as reference data, the calculated value of the heart rate information excluding the maximum and minimum values among a plurality of heart rate information corresponding to each of a plurality of rest intervals included in the calculation period of the reference data.

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

[0192] The monitoring server (3000) can determine the calculation period of the reference data (S2310), check the rest intervals during the calculation period of the reference data (S2320), and extract the biological information of the confirmed rest intervals (S2330).

[0193] In the S2310 stage, as described above, the calculation period of the reference data is the period when the thyroid function of the user corresponds to "normal" according to the received thyroid state information.

[0194] In the S2310 stage, when the monitoring server (3000) cannot receive the thyroid state information of a user within the normal range (that is, when the thyroid state information received by the monitoring server (3000) is outside the normal range), the calculation period of the reference data is the period when the thyroid function of the user corresponds to "abnormal" according to the received thyroid state information.

[0195] As an example, when the input thyroid state information does not correspond to the normal range, a predetermined period before and after the time when the thyroid state information is input can be determined as the calculation period of the reference data.

[0196] The monitoring server (3000) can check the rest period corresponding to the determined calculation period of the reference data (S2320) and extract the biological information corresponding to the rest period (S2330). Since the above S2320 and S2330 stages have already been described in detail above, duplicate explanations are omitted.

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

[0198] As an example, the monitoring server (3000) can calculate reference time data as the average value of a plurality of heart rate information corresponding to each of the plurality of confirmed rest periods included in the calculation period of the reference data. As another example, the monitoring server (3000) can calculate reference period data as the median value of the median values of a plurality of heart rate information corresponding to each of the plurality of rest periods included in the calculation period of the reference data. As still another example, the monitoring server (3000) can calculate reference period data as the calculated value of the remaining heart rate information excluding the maximum value and the minimum value among the plurality of heart rate information corresponding to each of the plurality of rest 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 biometric information when the user's thyroid function is presumed to be "abnormal".

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

[0201] As an example, when the hormone value of the user associated with the received thyroid state information corresponds to the normal range, the monitoring server (3000) can calculate how much ng / dL should increase / decrease and estimate the change amount of the biometric information associated with the increase / decrease. The monitoring server (3000) can calculate reference data (S2350) by adding or subtracting the estimated change amount of the biometric information to / from the reference period data.

[0202] The monitoring server (3000) may also store data necessary for correcting the reference period data. As an example, the monitoring server (3000) may store data regarding the relationship between the hormone values of a number of users and the heart rate during the resting period. As a specific example, the monitoring server (3000) may also store statistical data on how much the heart rate generally increases when the hormone value increases by 0.1 ng / dL.

[0203] In this way, when the monitoring server (3000) receives the thyroid state information from the user terminal (2000), the method by which the monitoring server (3000) calculates the reference data has been specifically described.

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

[0205] According to the embodiment of the present embodiment, when there is no stored thyroid state information of the user, the monitoring server (3000) can also calculate the reference data based on the monitoring data calculated in the step S1300. Specifically, for example, the monitoring server (3000) can calculate the reference data based on the heart rate of the user obtained during a predetermined number of days. The monitoring server (3000) can calculate the reference data based on the heart rate of each user in a plurality of resting periods obtained during a predetermined number of days. At this time, the predetermined number of days is a period longer than the monitoring period.

[0206] 1-1.3.3 Timing of Calculating Reference Data According to the embodiments of the present embodiment, the reference data can be calculated by triggering the reception of thyroid state information. According to other embodiments of the present embodiment, the reference data can be calculated at a preset period for the monitoring server (3000). According to still other embodiments of the present embodiment, the reference data can be calculated when a signal for calculating the reference data is received by the monitoring server (3000) from an external device (for example, the user terminal (2000)).

[0207] According to still other embodiments of the present embodiment, the reference data can be calculated for each calculation time of the monitoring data. In other words, the monitoring server (3000) can calculate the monitoring data and newly calculate the reference data. Or, the monitoring server (3000) can newly calculate the reference data before calculating the monitoring data and compare the two data.

[0208] 1-1.4 Judgment of thyroid dysfunction (S1500) Subsequent to FIG. 5, the thyroid function monitoring (S100) according to the embodiments of the present embodiment can be executed through acquisition of biological information (S1100), calculation of monitoring data (S1300), and judgment of thyroid dysfunction (S1500).

[0209] FIG. 10 is a flowchart for explaining a method for judging thyroid dysfunction (S1500) according to the embodiments of the present embodiment.

[0210] Referring to FIG. 10, the monitoring server (3000) can compare the reference data with the monitoring data (S1510) after calculating the monitoring data (S1300). The server control unit (3300) can compare the reference data with the monitoring data (S1510).

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

[0212] As an example, the algorithm for comparing reference data and monitoring data is designed to execute a judgment of thyroid function abnormality (S1530) when the monitoring data is larger than the reference data by a predetermined numerical range or more. As another example, the algorithm for comparing reference data and monitoring data is designed to execute a judgment of thyroid function abnormality (S1530) when the monitoring data is smaller than the reference data by a predetermined numerical range or more. As still another example, the algorithm for comparing reference data and monitoring data is designed to execute a judgment of thyroid function abnormality (S1530) when the monitoring data is larger or smaller than the reference data by a predetermined numerical range or more.

[0213] If it is determined that the reference data and the monitoring data meet the preset conditions by comparing them at the S1510 stage, the monitoring server (3000) can determine that there is an abnormality in the user's thyroid function (S1530). The server control unit (3300) can compare the reference data and the monitoring data (S1510) and determine that there is an abnormality in the thyroid function (S1530) when corresponding to the determined conditions.

[0214] If it is determined in the S1530 stage 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). As an example, the user terminal (2000) can be controlled to output a warning regarding hyperthyroidism of the user through the terminal output unit (2200) in response to the signal received from the monitoring server (3000). As another example, the user terminal (2000) can be controlled to output a warning regarding hypothyroidism of the user through the terminal output unit (2200) in response to the signal received from the monitoring server (3000). As still another example, the user terminal (2000) can be controlled to output a warning regarding thyroid poisoning of the user through the terminal output unit (2200) in response to the signal received from the monitoring server (3000). As yet another example, if the user terminal (2000) receives a signal corresponding to S1530 exceeding a predetermined number of times within a certain period from the monitoring server (3000), it can be controlled to output a comment asking for expert opinions in the warning regarding thyroid poisoning of the user through the terminal output unit (2200).

[0215] The warning regarding the thyroid function abnormality includes a warning regarding the presence or absence of the disease, a warning regarding the possibility (or risk level) of the disease, an induction to visit the hospital, etc.

[0216] The warning regarding the thyroid function abnormality may be a visual output by a display panel or the like, or an auditory output by a speaker or the like, but is not limited thereto.

[0217] FIG. 11 is a flowchart for explaining an algorithm for comparing reference data and monitoring data according to an embodiment of the present embodiment.

[0218] The monitoring server (3000) can compare monitoring data with reference data. The monitoring server (3000) can compare whether the monitoring data is greater than or equal to a first critical value compared to the reference data (S1511).

[0219] If the monitoring data is greater than or equal to the first critical value compared to the reference data, the monitoring server (3000) can determine that there is an abnormality in the user's thyroid function. As an example, if the monitored heart rate is greater than or equal to the first critical value (for example, 10 times) compared to the reference heart rate, the monitoring server (3000) can determine that there is an abnormality in the user's thyroid function. The monitoring server (3000) can predict (S1531) the user's hyperthyroidism. The monitoring server (3000) can diagnose (S1531) the user's hyperthyroidism. The monitoring server (3000) can determine (S1531) the user's hyperthyroidism.

[0220] If it is determined at the S1531 stage that there is an abnormality in the user's 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 or equal to the first critical value compared to the reference data, the monitoring server (3000) can compare whether the monitoring data is less than or equal to a second critical value compared to the reference data (S1513).

[0222] When the monitoring data is smaller than the second critical value or more compared with the reference data, the monitoring server (3000) can determine that there is an abnormality in the thyroid function of the user. As an example, when the monitored heart rate is smaller than the second critical value (for example, 8 times) or more compared with the reference heart rate, the monitoring server (3000) can determine that there is an abnormality in the thyroid function of the user. The monitoring server (3000) can predict (S1531) hypothyroidism of the user. The monitoring server (3000) can diagnose (S1531) hypothyroidism of the user. The monitoring server (3000) can determine (S1531) hypothyroidism of the user.

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

[0224] FIGS. 12 to 16 are diagrams showing the clinical research content regarding the correlation between hypothyroidism and heart rate based on the prediction of thyroid function abnormality by the monitoring system according to the embodiment of the present invention.

[0225] In this clinical study, in order to confirm the relationship between hypothyroidism and heart rate, 44 patients with hypothyroidism after thyroidectomy (hereinafter referred to as clinicians) were recruited, and they were made to wear a wearable device to continuously monitor their heart rate. In this clinical study, the wearable device used was Fitbit Charge 2 TM Clinicians wearing the wearable device and the like visited the hospital three times, and the change in thyroid hormone concentration due to the interruption and maintenance of thyroid hormone agents during the progress of thyroid hormone agent treatment was compared with the change in heart rate measured by the wearable device.

[0226] Specifically, the patients who participated in this clinical study were classified into two clinical groups. The first clinical group consisted of 30 clinicians. The clinicians classified into the first clinical group visited the hospital three times. Before the first visit, and until after the second visit and at the third visit, they were taking thyroid hormone medications, but were induced not to take thyroid hormone medications during the period between the first visit and the second visit, and one month before the second visit. At the first visit and the third visit, their thyroid function was normal, and at the second visit, they had hypothyroidism. The first clinical group was classified as the Hypothyroidism group. The second clinical group consisted of 14 clinicians. The clinicians classified into the second clinical group also visited the hospital three times, but were induced to continuously take thyroid hormone medications from before the first visit until the third visit. At the first, second, and third visits, their thyroid function was normal, and the second clinical group was classified as the Control group.

[0227] Figure 12 shows the characteristics of clinical participants in each clinical group who participated in the clinical research process. In Figure 12, Age represents age, Gender represents gender, Body mass index represents the obesity index, Systolic blood pressure represents systolic blood pressure, Diastolic Blood Pressure represents diastolic blood pressure, On-site resting heart rate represents the heart rate measured by an automatic blood pressure monitor when the patient visits the hospital, Thyroid function test represents the thyroid hormone concentration measured when the patient visits the hospital, free T4 represents thyroid hormone, TSH represents thyroid stimulating hormone, Glucose represents glucose, BUN represents blood element quality, Creatinine represents creatinine, Total cholesterol represents total cholesterol, Total protein represents total protein, Albumin represents albumin, AST represents Aspartate Transaminase, ALT represents Alamine Transaminase, WBC represents white blood cells, Hemoglobin represents hemoglobin, and Platelet represents platelets respectively. In Figure 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 clinical participants belonging to the clinical group, and are displayed as (mean) ± (standard deviation).

[0228] Figure 13 shows the changes in thyroid function parameters between the first hospital visit and the second hospital visit in clinical participants who participated in the clinical research process. In Figure 13, free T4 represents the thyroid hormone concentration, TSH represents the thyroid stimulating hormone concentration, zulewski's clinical score represents the clinical score of Zulewski's hypothyroidism, On-site rHR represents the resting heart rate measured in a palliative manner in a state of rest for 15 minutes when visiting the hospital, WD-rHR represents the average value of the resting heart rate measured by a wearable device during the 5 days before the hospital visit, WD-sleepHR represents the average value of the heart rate during sleep measured by a wearable device during the 5 days before the hospital visit, and WD-2to6HR represents the average value of the heart rate between 2 am and 6 am measured by a wearable device during the 5 days before the hospital visit respectively.

[0229] In FIG. 13, Visit 1 or 3 represents the value of each characteristic based on the data of the first visit of the clinicians belonging to each clinical group. When the data for the first visit of each clinician was missing, the data for the third visit of the corresponding clinician was used to substitute for the data for the first visit for analysis. In FIG. 13, Visit 2 represents the value of each characteristic based on the data of the second visit of the clinicians belonging to each clinical group.

[0230] In FIG. 13, in the Hypothyroidism group, the numerical value of thyroid hormone (free T4) measured at the second visit decreased below the normal range (0.8 - 1.8 ng / dL) compared to the first visit. For the clinicians belonging to the corresponding clinical group, it can be seen that they are in a state of hypothyroidism at the second visit. In FIG. 13, in the Control group, the numerical values of thyroid hormone (free T4) measured at the first visit and the second visit all fall within the normal range (0.8 - 1.8 ng / dL). For the clinicians belonging to the corresponding clinical group, it can be seen that their thyroid function is normal at the first and second visits. In FIG. 13, although the numerical values of thyroid hormone (free T4) in the Control group are all within the normal range at Visit 1 or 3 and Visit 2, the free T4 thyroid hormone numerical value at the second visit decreased significantly statistically compared to the first visit. The on-site rHR could not reflect the difference in which the numerical value of thyroid hormone (free T4) in the Control group decreased significantly. However, it was found that the parameters measured by the wearable device (i.e., WD-rHR, WD-sleepHR, WD-2to6HR) are relatively more sensitive indicators that can reflect the difference in which the numerical value of thyroid hormone (free T4) in the Control group decreased significantly.

[0231] Figure 14 shows the analysis results of the relationship between free T4 thyroid hormone concentration and heart rate parameters based on the changes in the parameters of thyroid function shown in Figure 13. In Figure 14, the Unstandardized beta of On-site rHR represents the relationship between the resting heart rate measured in a state of rest for 15 minutes upon arrival at the hospital and the free T4 thyroid hormone concentration; the Unstandardized beta of WD-rHR represents the relationship between the average value of the resting heart rate measured by the wearable device during the 5 days before coming to the hospital and the free T4 thyroid hormone concentration; the Unstandardized beta of WD-sleepHR represents the relationship between the average value of the heart rate during sleep measured by the wearable device during the 5 days before coming to the hospital and the free T4 thyroid hormone concentration; the Unstandardized beta of WD-2to6HR represents the relationship between the average value of the heart rate between 2 am and 6 am measured by the wearable device during the 5 days before coming to the hospital and the free T4 thyroid hormone concentration.

[0232] From Figure 14, it was confirmed that the relationship between several parameters calculated using the heart rate obtained by the wearable device and the free T4 thyroid hormone concentration is greater than the relationship between the resting heart rate upon arrival at the hospital and the free T4 thyroid hormone concentration.

[0233] Figure 15 shows a graph representing the analysis results of the correlation between hypothyroidism and heart rate parameters based on the changes in the thyroid function parameters shown in Figure 13. Here, hypothyroidism is meant to signify the result of a doctor's diagnosis of the presence or absence of thyroid function abnormalities based on the hormone values measured at the time of hospital visit. In Figure 15, the Unstandardized beta of On-site rHR represents the correlation between the resting heart rate measured in a state of rest for 15 minutes at the time of hospital visit and the diagnosis of hypothyroidism, the Unstandardized beta of WD-rHR represents the correlation between the average value of the resting heart rate measured by a wearable device during the 5 days before hospital visit and the diagnosis of hypothyroidism, the Unstandardized beta of WD-sleepHR represents the correlation between the average value of the heart rate during sleep measured by a wearable device during the 5 days before hospital visit and the diagnosis of hypothyroidism, and the Unstandardized beta of WD-2to6HR represents the correlation between the average value of the heart rate between 2 am and 6 am at dawn measured by a wearable device during the 5 days before hospital visit and the diagnosis of hypothyroidism.

[0234] According to Figure 15, it was confirmed that the correlations between several parameters calculated using the heart rate obtained by the wearable device and the diagnosis of hypothyroidism are greater than the correlation between the resting heart rate at the time of hospital visit and the diagnosis of hypothyroidism.

[0235] According to Figures 14 and 15, it was confirmed that the correlation between the parameters calculated using the heart rate obtained by the wearable device and hypothyroidism is greater than the correlation between the resting heart rate at the time of hospital visit and hypothyroidism. This is because due to the characteristics of the wearable device, when predicting thyroid function abnormalities based on the heart rate information obtained through the wearable device, it is not to predict thyroid function abnormalities based on the "short-term" heart rate information at the time of hospital visit, but rather it is possible to predict thyroid function abnormalities based on the relatively "long-term" heart rate information obtained during daily life. Therefore, it is judged that relatively accurate prediction is possible.

[0236] Figure 16 shows the changes in mean free T4 according to the time of hospital visit, the changes in the symptom scores of hypothyroidism according to the time of hospital visit, the changes in On-site HR according to the time of hospital visit, the changes in WD-rHR according to the time of hospital visit, the changes in WD-sleepHR according to the time of hospital visit, and the changes in WD-2to6HR according to the time of hospital visit, based on the changes in thyroid function parameters at the time of hospital visit shown in Figure 13 (in order from the upper left to the right side, error bar 95% CI). In Figure 16, On-site rHR means the resting heart rate measured in a palliative manner in a state of resting for 15 minutes at the time of hospital visit, WD-rHR means the average value of the resting heart rate measured by a wearable device during the 5 days before hospital visit, WD-sleepHR means the average value of the heart rate during sleep measured by a wearable device during the 5 days before hospital visit, and WD-2to6HR means the average value of the heart rate between 2 am and 6 am measured by a wearable device during the 5 days before hospital visit. In Figure 16, among the obtained heart rate-related parameters, WD-rHR, WD-sleepHR, and WD-2to6HR were calculated based on the biological information and / or time, exercise, and sleep information obtained using a wearable device and were used.

[0237] In Figure 16, the change in symptom scores according to the time of hospital visit showed that the error bars of the Hypothyroidism group overlapped with the average value of the Control group, indicating that it was actually difficult to use "symptom scores" as a single indicator to distinguish between hypothyroidism and normal. On the contrary, in Figure 16, the heart rate-related parameters (On-site rHR, WD-rHR, WD-sleepHR, and WD-2to6HR) according to the time of hospital visit not only had the error bars of the Hypothyroidism group located below the average value of the Control group, but also the average value of the Hypothyroidism group was significantly different from the average value of the Control group, showing that heart rate-related parameters (On-site rHR, WD-rHR, WD-sleepHR, or WD-2to6HR) could be used as a single indicator to distinguish between hypothyroidism and normal.

[0238] As a result, the parameters based on the heart rate measured by the wearable device are not only utilized as an indicator for predicting hypothyroidism, but it has been proven that they show stronger predictive power compared to the conventionally used symptom scores. Also, the "correlation between the heart rate measured by the wearable device and the morbidity or recurrence rate of hypothyroidism" confirmed based on this clinical study shows that by using the monitoring system according to the embodiment of the present invention, even without the patient directly visiting the hospital, just by wearing the wearable device, it is possible to easily predict recurrence by evaluating the degree of regulation of hyperthyroidism from the change in the resting heart rate.

[0239] In this specification, the clinical research content regarding the mutual relationship between hyperthyroidism and heart rate was not separately described as the basis for the monitoring system according to the embodiment of the present invention to predict thyroid function abnormalities. This is because it is disclosed in Korean Registered Patent No. 10-2033696, and those skilled in the art can fully understand that even without duplicate descriptions of the relevant content in this specification, there is a possibility of predicting hyperthyroidism based on the heart rate information using the wearable device.

[0240] 1 - 1.5 Timing of thyroid function abnormality monitoring (S100) According to the embodiment of the present invention, the thyroid function abnormality monitoring method (S100) can include acquisition of biological information (S1100), calculation of monitoring data (S1300), and determination of thyroid function abnormality (S1500).

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

[0242] The period (i.e., the previous first period) during which the monitoring server (3000) acquires biometric information from the user terminal (2000) (S1100) may be different from the period during which the wearable device (1000) acquires biometric signals. The period during which the monitoring server (3000) acquires biometric information from the user terminal (2000) (S1100) is longer than or the same as the period during which the wearable device (1000) acquires biometric signals. Since this has already been explained above, duplicate explanations are omitted.

[0243] The calculation of monitoring data (S1300) can be executed according to a second period. The calculation of monitoring data (S1300) can be executed triggered by the acquisition of biometric information (S1100). The calculation of monitoring data (S1300) can be executed according to a period (i.e., the previous second period) preset in the monitoring server (3000). Or, if a signal for executing the S1300 step is received from an external device (e.g., the user terminal (2000)), the calculation of monitoring data (S1300) can be executed.

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

[0245] The determination of thyroid function abnormality (S1500) can be executed according to the third cycle. The determination of thyroid function abnormality (S1500) can be executed triggered by the calculation of monitoring data (S1300). The determination of thyroid function abnormality (S1500) can be executed according to the cycle (i.e., the immediately preceding third cycle) preset in the monitoring server (3000).

[0246] The cycle (i.e., the immediately preceding third cycle) in which the monitoring server (3000) determines thyroid function abnormality (S1500) may be different from the cycle (i.e., the immediately preceding second cycle) in which the monitoring server (3000) calculates monitoring data (S1300). The cycle (i.e., the immediately preceding third cycle) in which the monitoring server (3000) determines thyroid function abnormality (S1500) is longer than or the same as the cycle (i.e., the immediately preceding second cycle) in which the monitoring server (3000) calculates monitoring data (S1300).

[0247] FIG. 17 is a diagram for explaining the operation execution timing of the thyroid function abnormality monitoring method (S100) according to the embodiment of the present embodiment.

[0248] According to the embodiment of the present embodiment, the reference data can be calculated triggered by the reception of thyroid state information. The biological information can be acquired according to the first cycle. The monitoring data can be calculated according to the second cycle. The determination of thyroid function abnormality can be executed in response to the calculation of the monitoring data.

[0249] Referring to FIG. 12, if thyroid state information is received (S2100) by the monitoring server (3000), the reference data can be calculated (S2300) for the reference data calculation period (RP).

[0250] The reference data can be calculated based on the biological information of at least one rest period included in the calculation period (RP) of the reference data. The reference data can be calculated based on the heart rate of at least one rest period included in the calculation period (RP) of the reference data. The reference data calculated by the method for calculating reference data described herein can be started with a reference heart rate when the biological information used at the time of calculation includes a heart rate.

[0251] In one example, the reference heart rate can be determined based on the heart rate of the rest period by extracting the rest period of the period including the day when the thyroid hormone value of the user is within the normal range.

[0252] In another example, the reference heart rate can be determined by the estimated data for the reference period data determined based on the heart rate of the rest period by extracting the rest period of the period including the day when the thyroid hormone value of the user is out of the normal range.

[0253] In still another example, when there is no thyroid hormone value of the user, the reference heart rate can be calculated based on the heart rate of the user over a continuous number of days.

[0254] The calculation period (RP) of the reference data can also include the period before the reception (S2100) of the thyroid state information. In one example, the calculation period (RP) of the reference data can be determined to be a total of 5 days including 2 days before and 2 days after that day, based on the reception (S2100) day of the thyroid state information.

[0255] Biological information may be acquired (S1100) by the monitoring server (3000) according to the first cycle. The acquisition period (S1100) of the biological information can include an overlapping period with the calculation period (RP) of the reference data. The acquisition (S1100) of the biological information can overlap with the timing of the calculation (S1300) of the monitoring data.

[0256] The biological information acquired according to the first cycle can be stored in the monitoring server (3000). The biological information acquired according to the first cycle can be stored in the monitoring server (3000) for a predetermined period of time.

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

[0258] The monitoring data can be calculated based on the biological information of at least one rest interval included in the monitoring period (MP). The monitoring data can be calculated based on the heart rate of at least one rest interval included in the monitoring period (MP). The monitoring data calculated by the method for calculating monitoring data described in this specification, when the biological information used at the time of calculation includes the heart rate, can be disclosed as the monitoring heart rate.

[0259] In one example, the monitoring heart rate can be determined by extracting the rest intervals of the monitoring period and based on the heart rate of the rest intervals. The rest intervals can be selected based on information about the user's exercise state. The rest intervals can be selected based on an interval in which the user's step count is 0 and lasts for a predetermined time or more. The rest intervals can be selected based on an interval in which the user's acceleration is 0 and lasts for a predetermined time or more.

[0260] In another example, the monitoring heart rate can be determined by extracting the sleep intervals of the monitoring period and based on the heart rate of the sleep intervals. The sleep intervals can be selected based on information about the user's exercise state. Alternatively, the sleep intervals can be selected based on information related to breathing, noise, and biological information different from the heart rate 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 point in time. The monitoring server (3000) can calculate second monitoring data for a second monitoring period (MP) at a second point in time after a period corresponding to a second cycle has elapsed from the first point in time.

[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 periods 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 essential, the calculation period (RP) of the reference data and the monitoring period (MP) may be periods of the same length. In other words, if the calculation period (RP) of the reference data is 5 days, the monitoring period (MP) is also 5 days.

[0264] According to an example of the present embodiment, the period in which the monitoring server (3000) calculates monitoring data (S1300) may be longer than the period in which the monitoring server (3000) acquires biological information (S1100). Specifically, for example, the monitoring server (3000) can acquire biological information at 3-hour intervals and calculate monitoring data at 1-day intervals.

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

[0266] The monitoring server (3000) can execute the determination of thyroid function abnormality (S1500) according to the third cycle. The monitoring server (3000) can execute the determination of thyroid function abnormality (S1500) when the calculation of monitoring data (S1300) is completed.

[0267] When the calculation of monitoring data (S1300) is completed, the monitoring server (3000) can compare the reference data with the monitoring data (S1510). The monitoring server (3000) compares the reference data with the monitoring data (S1510), and when a predetermined condition is satisfied, it determines that there is an abnormality in the thyroid function (S1530), and can execute an appropriate operation for outputting a warning.

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

[0269] The cycle for the monitoring server (3000) to calculate the reference data (S200) may be longer than the cycle for calculating the monitoring data (S1300).

[0270] The reference data according to the embodiment of the present embodiment may be stored in the server database (3200) of the monitoring server (3000) before the calculation of the monitoring data (S1300). The monitoring server (3000) can check the reference data stored at the time of calculating the monitoring data (S1300) and determining the thyroid function abnormality (S1500). Therefore, the reference data value stored in the server database (3200) can be maintained until a new event occurs and the reference data is updated. When the thyroid state 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 function abnormality (S1500) using the corresponding reference data can be performed multiple times. Multiple monitorings (S100) of thyroid function abnormality can be performed until the reference data is calculated (S200) and other events occur.

[0272] According to the embodiment of the present embodiment, the time point of calculating the reference data (S2300) and the time point of determining thyroid function abnormality (S1500) may not overlap. The calculation period (RP) of the reference data and the monitoring period (MP) may not overlap.

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

[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 Function Abnormality Monitoring System (100) FIG. 18 and FIG. 19 are diagrams for explaining the user interface (400) in the thyroid function abnormality monitoring system (100) according to the embodiment of the present embodiment.

[0276] According to the examples of this embodiment, the user terminal (2000) can output the results associated with the monitoring of thyroid function abnormalities (S100). According to the examples of this embodiment, the user terminal (2000) can output the results associated with the determination of thyroid function abnormalities (S1500). According to the examples of this embodiment, the user terminal (2000) can output the results associated with the determination of thyroid function abnormalities (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] Based on the determination of thyroid function abnormalities (S1500), information can be output through the thyroid information interface (4100). As an example, the thyroid information interface (4100) can output information regarding the risk level of thyroid function abnormalities. In a specific example, the thyroid information interface (4100) can output information indicating that the risk of hyperthyroidism is 74% and it is a high-risk condition. The thyroid information interface (4100) can further output information for inducing a hospital visit. In a specific example, the thyroid information interface (4100) can output a message suggesting that the user consult a primary care physician because the risk of hyperthyroidism is high. In a specific example, the thyroid information interface (4100) can output a questionnaire for self-diagnosis.

[0280] The "warning" described in this specification can include providing information to the user based on the determination of thyroid function abnormality (S1500). The "warning message" described in this specification can include that the information provided to the user based on the determination of thyroid function abnormality (S1500) can be executed in visual, auditory, tactile, olfactory, and / or gustatory manners.

[0281] The heart rate information interface (4200) can output information based on the calculation of monitoring data (S1300). In one example, the heart rate information interface (4200) can output monitoring heart rate information. In a specific example, the heart rate information interface (4200) can be output so that the changing pattern of the monitoring heart rate information over time is expressed.

[0282] The heart rate information interface (4200) can output information based on the calculation of reference data (S200) together. As an example, the heart rate information interface (4200) can output reference heart rate information. In a specific example, the heart rate information interface (4200) can output together reference heart rate information, a first critical heart rate information that serves as a criterion for the determination of thyroid abnormality that is approximately greater than the first critical value from the reference heart rate, and a second critical heart rate information that serves as a criterion for the determination of thyroid abnormality that is approximately less than the second critical value from the reference heart rate.

[0283] The heart rate information interface (4200) can output other information together with the monitoring heart rate information. In a specific example, the heart rate information interface (4200) can be output so that the changing pattern of the monitoring heart rate information and the hormonal numerical values obtained from the user's blood test over time can be expressed.

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

[0285] The hormone information interface (4300) can output both the user's blood test date and time. The hormone information interface (4300) can output both the elapsed time based on the most recent user's blood test date and time.

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

[0287] The inspection information input interface (4400) can output an interface that enables the input of the user's blood test results. As an example, the inspection information input interface (4400) can output an interface that is divided so that the user's blood test results can be input for each hormone.

[0288] The inspection information input interface (4400) can further include a photo input interface (4420). When the photo input interface (4420) is clicked, the user terminal (2000) can provide an interface that allows the user to photograph the inspection result sheet as an image. When the user's inspection result sheet is photographed, the user terminal (2000) can execute optical character recognition (OCR) to confirm the user's hormone information.

[0289] The user interface (400) can further include a self-diagnosis input interface for receiving the input of the user's symptom information. Through the self-diagnosis input interface, symptom information such as insomnia, headache, hand tremors, and decreased concentration can be input.

[0290] 3. Modified Embodiment of the Thyroid Dysfunction Monitoring System (100) According to the embodiment of this embodiment, the thyroid dysfunction monitoring system (100) can predict the user's thyroid dysfunction based on the biological signal. The thyroid dysfunction monitoring system (100) can monitor the user's heart rate information to predict the user's thyroid dysfunction.

[0291] When the thyroid dysfunction monitoring system (100) predicts the user's thyroid dysfunction, in order to judge the thyroid function more accurately, it is necessary to distinguish it from other diseases with similar symptoms (that is, similar biological signals). For example, when the thyroid dysfunction monitoring system (100) predicts the user's thyroid function based on the user's heart rate information, it is important to distinguish it from atrial fibrillation in which the heart rate increases significantly compared to normal.

[0292] For this reason, the thyroid dysfunction monitoring system (100) can further consider a second factor different from the heart rate to predict thyroid dysfunction. Below, a modified embodiment of the thyroid dysfunction monitoring system (100) for more accurate prediction of the user's thyroid dysfunction will be described in detail.

[0293] According to the thyroid dysfunction monitoring method described below, more accurate thyroid dysfunction monitoring can be provided for users who have never suffered from hyperthyroidism or hypothyroidism in the past and users with few genetic factors.

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

[0295] Referring to FIG. 20, the thyroid function abnormality monitoring method (S300) according to this embodiment can include thyroid function monitoring by a first factor (S3100), thyroid function monitoring by a second factor (S3200), and abnormality determination of thyroid function (S3300). According to the embodiment, the steps of S3100, S3200, and S3300 can be executed by a monitoring server (3000).

[0296] The thyroid function monitoring by the first factor (S3100) can be executed in a manner similar to the thyroid function abnormality monitoring (S100) method described above. In one example, the thyroid function monitoring by the first factor (S3100) can be executed in a manner similar to the thyroid function abnormality monitoring (S100) method described above, such as acquiring heart rate information (S1100), calculating a monitored heart rate (S1300), and determining thyroid function abnormality by comparison with a reference heart rate (S1500).

[0297] Therefore, redundant descriptions of the thyroid function monitoring by the first factor (S3100) are omitted.

[0298] The thyroid function monitoring by the second factor (S3200) can be executed in a form of analyzing PPG data acquired through a device sensor unit (1400). As an example, the acquired PPG data may be data acquired for extraction of the heart rate used in the thyroid function monitoring by the first factor (S3100).

[0299] FIG. 21 is a diagram for explaining PPG data acquired through a wearable device (1000) and analysis of the PPG data executed through a 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, through the user terminal (2000), the PPG data acquired by the device sensor unit (1400).

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

[0302] In the case of thyroid function abnormality, the heart rate shows a gradual increase or decrease over a long period of time. In the case of atrial fibrillation, irregular heartbeats are repeated in a state where "many parts of the myocardium contract irregularly and without control simultaneously". Therefore, by classifying the peak interval (PI) of the PPG data, the accuracy of thyroid function abnormality judgment can be improved.

[0303] According to the examples of the present embodiment, the monitoring server (3000) checks the peak interval (PI) of the PPG data at step S3200. When the peak interval (PI) changes significantly over time, it can be determined that the risk of atrial fibrillation is higher than the risk of thyroid dysfunction. As an example, when it is determined that there is thyroid dysfunction at step S3100, the monitoring server (3000) checks the peak interval (PI) of the PPG data at step S3200. When the peak interval (PI) changes significantly over time, it can be determined 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 at step S3200. When the peak interval (PI) is maintained almost constant, it can be determined that the risk of thyroid dysfunction is higher than the risk of atrial fibrillation. As an example, when it is determined that there is thyroid dysfunction at step S3100, the monitoring server (3000) checks the peak interval (PI) of the PPG data at step S3200. When the peak interval (PI) is maintained almost constant regardless of the change in time, it can be determined that the risk of thyroid dysfunction is high (S3300).

[0305] When performing thyroid dysfunction monitoring (S300) according to this example, it can be derived that there is an advantage that thyroid dysfunction can be predicted more accurately without adding separate hardware.

[0306] Subsequent to FIG. 20, the thyroid function monitoring (S3200) based on the second factor can be executed in a form of jointly analyzing other biological information other than the heart rate information obtained through the device sensor unit (1400). As an example, the wearable device (1000) can acquire the temperature information of the user. The device sensor unit (1400) can sense the temperature of the user, 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 of obtaining reference data in step S2300 as described above. The monitoring temperature can be obtained in a manner similar to the method of obtaining monitoring data in step S1300 as described above.

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

[0309] The monitoring server (3000) according to the embodiment of the present invention can determine an abnormality in the thyroid function of a user based on the user's heart rate information and temperature information. The monitoring server (3000) can execute thyroid function monitoring of the user based on the heart rate information and function monitoring of the user based on the temperature information in parallel. In other words, it is possible to calculate the monitoring heart rate and monitoring temperature information for the monitoring period, compare the monitoring heart rate with the reference heart rate, compare the monitoring temperature with the reference temperature, and execute an abnormality determination (S3300) of the user's thyroid function based on the two result values.

[0310] According to another embodiment of the present embodiment, the monitoring server (3000) can sequentially execute thyroid function monitoring of the user related to the heart rate information and function monitoring of the user related to the temperature information. In other words, when an abnormality in the thyroid function is determined by comparing the monitoring heart rate with the reference heart rate (S3100), the monitoring temperature is compared with the reference temperature, and an abnormality determination (S3200) of the user's thyroid function is executed based on the two result values, and a final abnormality determination (S3300) of the user's thyroid function is executed. For example, when it is determined that there is no abnormality in the thyroid function by comparing the monitoring heart rate with the reference heart rate (S3100), the thyroid function monitoring (S3200) of comparing the monitoring temperature with the reference temperature may not be executed.

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

[0312] Following FIG. 20, the monitoring of thyroid function (S3200) by the second factor can be executed in a form that requests additional biological information through the device sensor unit (1400). As an example, when it is determined that the result by S3100 may indicate a thyroid abnormality, the monitoring server (3000) can request the transmission of additional ECG waveform data, analyze the corresponding data, and execute the monitoring of thyroid function (S3200) by the second factor.

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

[0314] When it is determined through the monitoring of thyroid function (S3100) by the first factor that there is a possibility of an abnormality in the thyroid function of the user, the monitoring server (3000) can request the input of additional data (S3210). The monitoring server (3000) can transmit a request for additional data to the user terminal (2000) in order to receive the input of additional data (S3210). The step S3210 is an operation executed by the server control unit (3300) through the server communication unit (3100).

[0315] In one example, the user terminal (2000) can request additional data from the wearable device (1000) as a response to a request from the monitoring server (3000). The wearable device (1000) can output to the user, through the device output unit (1200), a notification asking the user to input additional data as a response to the request from the user terminal (2000). In another example, the user terminal (2000) can output to the user, through the terminal output unit (2200), a notification asking the user to input additional data by the wearable device (1000).

[0316] When the additional data is ECG data, the user needs to connect both hands to each of 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 the wearable device (1000) according to an embodiment of the present embodiment.

[0318] For the acquisition of ECG data by the wearable device (1000), it may be required to form an electrical closed loop of the body with both electrodes by contacting the left or right hand with the first electrode (ET1) and the remaining hand with the second electrode (ET2).

[0319] In the wearable device (1000) illustrated in FIG. 23, the first electrode (ET1) is formed on the back surface of the display that contacts the wrist, and the second electrode (ET2) is formed on the scroll for adjusting the display or the like.

[0320] At this time, if the left hand contacts the first electrode (ET1) in a form where the first electrode (ET1) is formed on the left wrist and the right finger contacts the second electrode (ET2), ECG sensing through the wearable device (1000) can be executed.

[0321] Following FIG. 22, the monitoring server (3000) may be induced to request the user to contact the second electrode (ET2) with the fingers of the right hand as a request for input of additional data (S3210). If additional data is input by the execution of a specific operation of the user, the wearable device (1000) can transmit the acquired additional data to the monitoring server (3000) through the user terminal (2000).

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

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

[0324] When the user shows atrial fibrillation symptoms, the P wave among the PQRS waves of the user may not be accurately distinguished. Referring to FIG. 24(a), all of the PQRS waves clearly appear in the ECG data of a person distinguished as a normal person. However, referring to FIG. 24(b), it can be confirmed that other values than the maximum peak value are not accurately distinguished in the ECG data of a person distinguished as having atrial fibrillation, and in particular, the P wave is not distinguished.

[0325] Following FIG. 22, if the monitoring server (3000) analyzes the waveform of the input additional data (S3230) and determines that the P wave of the user is not clearly distinguished, it can be determined (S3300) that the risk of atrial fibrillation is higher than the risk of thyroid dysfunction.

[0326] If the monitoring server (3000) analyzes the waveform of the input additional data (S3230) and determines that the P wave of the user is well distinguished, it can be determined (S3300) that the risk of thyroid dysfunction is higher than the risk of atrial fibrillation.

[0327] 4. Monitoring of thyroid dysfunction considering drug administration (S400) In the case of patients with thyroid dysfunction, the majority are undergoing treatment therapy by taking medications. However, it is still necessary to monitor thyroid dysfunction in patients taking medications.

[0328] Patients are concerned about the appropriateness of the drug dosage they take and whether current side effects are occurring in their own bodies. Therefore, the present invention discloses a method for monitoring thyroid dysfunction that can be provided to patients taking medications.

[0329] In order to provide thyroid dysfunction monitoring for patients taking medications, the following matters need to be considered.

[0330] In one example, if the monitoring is already in a situation where the patient has hyperthyroidism and is taking medication, since the patient already has hyperthyroidism and is taking medication, it is unnecessary to continuously warn of hyperthyroidism, and continuous warnings may foster an excessive sense of crisis in the patient.

[0331] In another example, if the monitoring is already in a situation where the patient has hyperthyroidism and is taking medication, but there are visible signs of the risk of hypothyroidism, this indicates that the amount of the drug is excessive for the patient, and it is necessary to warn the user as early as possible.

[0332] Therefore, in order to provide thyroid dysfunction monitoring for patients taking medications, it is important to perform appropriate thyroid dysfunction monitoring based on the medications taken by the user. Therefore, below, a thyroid function monitoring system considering medication intake according to an example of the present embodiment will be described.

[0333] 4.1 Receiving Medication Intake Information (S4100) FIG. 25 is a diagram for explaining a method for monitoring thyroid function (S400) considering medication intake according to an example of the present embodiment.

[0334] The thyroid function monitoring method considering drug administration (S400) can include receiving drug administration information (S4100), selecting a monitoring algorithm (S4300), and thyroid function monitoring (S4500). According to an embodiment, the steps S4100, S4300, and S4500 can be executed by a monitoring server (3000).

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

[0336] The user terminal (2000) can receive at least one of the prescription date and time of the drug related to the user's thyroid function, the name of the drug, the drug type, the drug volume, and the drug administration cycle. In one example, the information can be received through the terminal input unit (2100) by the user's physical input (for example, touch input). In another example, the information can be received through the terminal input unit (2100) by photographing an image of a prescription form or the like.

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

[0338] 4.2 Selection of Monitoring Algorithm (S4300) If drug administration information is received by the monitoring server (3000), the monitoring server (3000) can select a monitoring algorithm (S4300). If drug administration information is received, the server control unit (3300) can select a monitoring algorithm (S4300).

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

[0340] If drug administration information is received by the monitoring server (3000), the monitoring server (3000) can select a monitoring algorithm (S4300) to be used for determining abnormal thyroid function (S4500) of the user.

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

[0342] If the drugs taken by the user do not include drugs classified as "antithyroid drugs", the monitoring server (3000) can check whether the drugs taken by the user include drugs classified as "thyroid hormone drugs" (S4313). If the drugs taken by the user include drugs classified as "thyroid hormone drugs", the monitoring server (3000) can select the first-2 monitoring algorithm (S4333) so that the S4500 step is executed by the first-2 monitoring algorithm.

[0343] If the monitoring server (3000) does not include a drug classified as a "thyroid hormone drug" among the drugs taken by the user, the second monitoring algorithm can be selected (S4335) so that the S4500 stage is executed by the second monitoring algorithm.

[0344] The monitoring server (3000) can change the order in which the S4311 and S4313 stages are executed or execute them simultaneously. In this case, if it is determined that all "antithyroid drugs" and "thyroid hormone drugs" are taken based on the drug administration information, the monitoring server (3000) can select the first - 1 monitoring algorithm (S4331) so that the S4500 stage is executed by the first - 1 monitoring algorithm.

[0345] According to an embodiment of the present embodiment, in order for the monitoring server (3000) to check whether there is a drug corresponding to an "antithyroid drug" and / or a "thyroid hormone drug" based on the drug administration information, information in which the name of the drug and the type of the drug are mapped can be stored in the server database (3200). The monitoring server (3000) can check whether there is a drug corresponding to an "antithyroid drug" and / or a "thyroid hormone drug" in the drug administration information with reference to the stored drug name and drug type information.

[0346] According to another embodiment of the present embodiment, the monitoring server (3000) can check the type of the drug from the drug administration information, check whether the type of the drug corresponds to an "antithyroid drug" and / or a "thyroid hormone drug", and check whether there is a drug corresponding to an "antithyroid drug" and / or a "thyroid hormone drug" in the drug administration information.

[0347] According to the examples of this embodiment, the monitoring server (3000) can further obtain the user's diagnosis information based on the drug-taking 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 drug-taking information.

[0348] In the step S4311, if it is determined that the drug-taking information includes a drug corresponding to an "anti-thyroid drug", the monitoring server (3000) can 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) can select the first monitoring algorithm 1-1 so that the step S4500 is executed by the first monitoring algorithm 1-1 (S4331).

[0349] In the step S4313, if it is determined that the drug-taking information includes a drug corresponding to a "thyroid hormone drug", the monitoring server (3000) can 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) can select the first monitoring algorithm 1-2 so that the step S4500 is executed by the first monitoring algorithm 1-2 (S4333).

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

[0351] 4.3 Monitoring of thyroid function abnormalities (S4500) Following FIG. 25, the monitoring server (3000) can execute thyroid function monitoring (S4500) according to a selected monitoring algorithm. The server control unit (3300) can execute thyroid function monitoring (S4500) according to a selected monitoring algorithm.

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

[0353] Referring to FIG. 27, thyroid function monitoring (S4500) can be performed by comparing monitoring data with reference data. Hereinafter, it will be specifically described on the assumption that the monitoring data is the monitoring 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. The monitoring server (3000) can compare whether the monitoring data is greater than or equal to a first critical value compared to the reference data (S4511).

[0355] When the monitoring data is greater than or equal to a first critical value compared to the reference data, the monitoring server (3000) can determine that there is an abnormality in the thyroid function of the user. In an example, when the monitoring heart rate is greater than or equal to a first critical value (for example, 10 times) compared to the reference heart rate, the monitoring server (3000) can determine that there is an abnormality in the thyroid function of the user. The monitoring server (3000) can predict hyperthyroidism of the user.

[0356] However, when the first monitoring algorithm is selected, since the user has already received a diagnosis of hyperthyroidism and is taking medications for the treatment of hyperthyroidism, it is not appropriate to continuously output a warning suggesting that the user visit a hospital because hyperthyroidism is suspected.

[0357] Therefore, when the first monitoring algorithm is selected and the monitoring data is greater than or equal to the first critical value compared to the reference data, the monitoring server (3000) can determine whether the grace period has elapsed based on the prescription date and time of the medication (S4531). If the grace period has elapsed, it can induce an output of a warning regarding hyperthyroidism (S4533). In one example, the grace period is three months from the prescription date and time of the medication. In another example, the grace period is one month from the prescription date and time of the medication. The grace period can be determined by the monitoring server (3000) that received the medication-taking information.

[0358] If it is determined at step S4533 that the user's hyperthyroidism persists, 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) (S4533). In one example, the monitoring server (3000) can transmit a signal to the user terminal (2000) so that a warning such as "It is necessary to increase the dosage of the medication, so please visit the hospital" or "Please go to the hospital and consult a doctor" is output through the terminal output unit (2200).

[0359] When the monitoring data is not greater than or equal to the first critical value compared to the reference data, the monitoring server (3000) can compare whether the monitoring data is less than or equal to the second critical value compared to the reference data (S4513).

[0360] When the monitoring data is smaller than the second critical value or more compared with the reference data, the monitoring server (3000) can judge that there is an abnormality in the thyroid function of the user. In one example, when the monitored heart rate is smaller than the second critical value (for example, 8 times) or more compared with the reference heart rate, the monitoring server (3000) can judge that there is an abnormality in the thyroid function of the user. The monitoring server (3000) can predict hypothyroidism of the user.

[0361] When the monitoring data is smaller than the second critical value or more compared with the reference data, the monitoring server (3000) can guide (S4535) to output a warning regarding hypothyroidism.

[0362] If it is judged at the S4535 stage that hypothyroidism of the user has developed, the monitoring server (3000) can transmit a signal (S4535) to the user terminal (2000) so that a warning regarding the thyroid abnormality of the user is output by 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 to the effect of "Since it is judged that the dosage of the medicine is excessive, please visit the hospital" or "Please go to the hospital and consult a doctor" is output through the terminal output unit (2200).

[0363] FIG. 28 is a flowchart for explaining the thyroid function monitoring (S4500) method by the selection (S4333) of the first to second monitoring algorithms of the example of the present embodiment. According to the example, the steps in FIG. 28 can be executed by the server control unit (3300).

[0364] Referring to FIG. 28, thyroid function monitoring (S4500) can be executed by comparing the monitoring data with the reference data. Hereinafter, specific description will be made 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. The monitoring server (3000) can compare whether the monitoring data is greater than or equal to a first critical value compared to the reference data (S4511).

[0366] If the monitoring data is greater than or equal to a first critical value compared to the reference data, the monitoring server (3000) can determine that there is an abnormality in the user's thyroid function. In one example, if the monitored heart rate is greater than or equal to a first critical value (for example, 10 times) compared to the reference heart rate, the monitoring server (3000) can determine that there is an abnormality in the user's thyroid function. The monitoring server (3000) can predict the user's hyperthyroidism.

[0367] If the monitoring data is greater than or equal to a first critical value compared to the reference data, the monitoring server (3000) can determine that there is an abnormality in the user's thyroid function. The monitoring server (3000) can predict the user's hyperthyroidism.

[0368] When the monitoring data is greater than or equal to a first critical value compared to the reference data, the monitoring server (3000) can induce (S4532) to output a warning regarding hyperthyroidism.

[0369] If it is determined at step S4515 that the user has developed hyperthyroidism, 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) (S4532). In one example, the monitoring server (3000) can transmit a signal to the user terminal (2000) so that a warning such as "It is determined that the dosage of the drug is excessive, so please visit the hospital" or "Please go to the hospital and consult a doctor" is output through the terminal output unit (2200).

[0370] When the monitoring data is not greater than or equal to the first critical value compared to the reference data, the monitoring server (3000) can compare whether the monitoring data is less than or equal to the second critical value compared to the reference data (S4513).

[0371] When the first - 2 monitoring algorithm is selected but the monitoring data is less than or equal to the second critical value compared to the reference data, the monitoring server (3000) determines whether the grace period has elapsed based on the prescription date and time of the drug (S4534). If the grace period has elapsed, it can induce an output of a warning regarding hyperthyroidism (S4536). In one example, the grace period is three months from the prescription date and time of the drug. In another example, the grace period is one month from the prescription date and time of the drug. The grace period can be determined by the monitoring server (3000) that received the drug administration information.

[0372] If it is determined at step S4534 that the user's hypothyroidism persists, 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) (S4536). In one example, the monitoring server (3000) can transmit a signal to the user terminal (2000) so that a warning such as "You need to increase the dosage of the drug and visit the hospital" or "Go to the hospital and consult a doctor" is output through the terminal output unit (2200).

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

[0374] In one example, when the second monitoring algorithm for monitoring is selected (S4335), the server (3000) can output the warning message when the monitored heart rate is greater than or equal to the first critical value than the reference heart rate, and can output the warning message when the monitored heart rate is less than or equal to the second critical value than the reference heart rate. When the second monitoring algorithm is selected (S4335), thyroid function monitoring can be performed similar to FIG. 11 described above. Therefore, duplicate explanations are omitted.

[0375] The above describes the monitoring of thyroid function abnormalities (S400) considering drug administration according to the examples of the present embodiment. However, the above-described monitoring of thyroid function abnormalities (S400) considering drug administration has been described by way of example in the case of performing all determinations of the risk levels for hypothyroidism and hyperthyroidism, but thyroid function abnormality monitoring can be performed so as to monitor only hypothyroidism or only hyperthyroidism.

[0376] Therefore, in this specification, the scope of the rights of the present invention should not be construed as being limited by the specific examples described for understanding, and the scope of the rights of the present invention should be construed by the interpretation of the claims of the present invention.

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

[0378] According to the example of the present embodiment, the monitoring algorithm can be selected (S4300) triggered by receiving the drug administration information (S4100). The monitoring server (3000) can select the monitoring algorithm (S4300) if the drug administration information is received (S4100).

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

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

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

[0382] In one example, when prescription information is input into the user terminal (2000) after the patient visits the hospital, the user terminal (2000) can transmit the input information to the monitoring server (3000). If the monitoring server (3000) receives the drug administration information (for example, some information included in the prescription information) (S4100), the monitoring server (3000) can select a monitoring algorithm (S4300). When the monitoring algorithm is selected (S4300), the monitoring server (3000) can perform thyroid function monitoring (S4500) according to the monitoring period. Specifically, for example, after the monitoring algorithm is selected (S4300), the monitoring server (3000) can perform thyroid function monitoring (S4500) once a day.

[0383] When performing thyroid function monitoring (S4500), information regarding reference data (for example, reference heart rate) can be stored in the monitoring server (3000). The reference data stored in the monitoring server (3000) may be calculated in response to the reception of the user's thyroid state information (S2100). The reference data calculation period of the monitoring server (3000) and the selection period of the monitoring algorithm of the monitoring server (3000) are independent of each other. The calculation period of the reference data of the monitoring server (3000) and the selection time of the monitoring algorithm of the monitoring server (3000) may have an overlapping interval.

[0384] 5 Management of Drug Administration According to the embodiment of the present embodiment, the monitoring server (3000) can provide a notification of drug administration based on the received drug administration information. The monitoring server (3000) can guide the output of a notification of drug administration based on the drug administration cycle included in the drug administration information.

[0385] The monitoring server (3000) can transmit a signal to the user terminal (2000) to provide the user with a notification of taking medicine. In one example, the user terminal (2000) can inform the user of the time to take the medicine based on the signal received from the monitoring server (3000).

[0386] FIG. 30 is a diagram for explaining the user interface (400) on the user terminal (2000) that provides advice on taking medicine according to the embodiment of the present embodiment.

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

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

[0389] The medication notification interface (4700) may include an interface that can check that the medication has been taken after taking the medication. The medication notification interface (4700) may include an interface that indicates the time of taking the medication and guides the user to take the medication at the time of taking the medication.

[0390] In one example, the user terminal (2000) can be provided with an interface that provides a notification to guide the user to take the medication according to the time of taking the medication. The corresponding interface is a confirmable screen when a program for thyroid function monitoring is output to the user terminal (2000). The corresponding interface is a screen provided in a pop-up form when another program is being driven on the user terminal (2000).

[0391] In one example, the monitoring server (3000) can output medication advice more frequently than the monitoring of thyroid function abnormalities (S4500). In one example, when the monitoring of thyroid function abnormalities (S4500) is executed once a day, the medication advice can be executed three times a day.

[0392] In another example, the monitoring server (3000) can output the same number of medication advice as the number of times of monitoring thyroid function abnormalities (S4500). In one example, when the monitoring of thyroid function abnormalities (S4500) is executed once a day, the medication advice can be executed once a day.

[0393] As described above, specific descriptions have been made for some embodiments of the method for monitoring thyroid function abnormalities in the case where the thyroid function monitoring system (100) includes the wearable device (1000), the user terminal (2000), and the monitoring server (3000).

[0394] However, the thyroid function monitoring system (100) can be deformed and implemented within an easy range for those skilled in the art.

[0395] According to the embodiments of the present embodiment, the above-described method for monitoring thyroid function abnormalities can be provided in the form of a recording medium storing computer-readable and executable code in which a program for executing the corresponding method is recorded.

[0396] According to the embodiments of the present embodiment, the above-described method for monitoring thyroid function abnormalities can be provided in the form of a wearable device (1000), a user terminal (2000), and / or a monitoring server (3000) for executing the corresponding method.

[0397] According to the embodiments of the present embodiment, the above-described method for monitoring thyroid function abnormalities can 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 executing the corresponding method.

[0398] In one example, the thyroid function monitoring system (100) can be implemented in a form including only the wearable device (1000) and the monitoring server (3000). In this case, the information output through the user terminal (2000) can be output through the wearable device (1000). The information input through the user terminal (2000) can be input through the wearable device (1000). Information regarding thyroid function abnormalities of the user determined by the monitoring server (3000) can be output through the wearable device (1000). The wearable device (1000) can execute functions through an input interface for receiving specific information from the user, such as thyroid state information and drug administration information.

[0399] In another example, the thyroid function monitoring system (100) can be embodied in a form that includes only the wearable device (1000) and the user terminal (2000). In this case, the user terminal (2000) can execute the operations of the above-described monitoring server (3000) by driving a program stored in the terminal memory unit (2400).

[0400] In yet another example, the thyroid function monitoring system (100) can be embodied in a form that includes a wearable device (1000), a monitoring server (3000) that communicates with the wearable device, and a user terminal (2000) that communicates with the monitoring server (3000). In this case, the wearable device (1000) transmits the 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) can execute an operation of transmitting the biological information received from the wearable device (1000) to the user terminal (2000) for outputting 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 function abnormality using skin conductance according to the present invention will be described.

[0403] 6 Method for Monitoring Thyroid Function Abnormality Using Skin Conductance FIG. 31 is a block diagram of a skin conductance measurement sensor according to an embodiment. The skin conductance measurement sensor can also be expressed as an EDA (Electrodermal Activity) sensor.

[0404] Referring to FIG. 31, the EDA sensor (5000) can 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 illustrated in FIG. 31 are not essential, and the EDA sensor (5000) can have more components or fewer components than that.

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

[0406] For example, the EDA sensor (5000) can measure the skin conductance of a user wearing the wearable device (1000). At this time, 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 the present embodiment, the EDA sensor (5000) can be embodied in an integrated form with the wearable device (1000). In one example, the above-described EDA sensor (5000) may be a sensor included in the device sensor unit (1400). In this case, the EDA sensor (5000) can receive the control of the device control unit (1600).

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

[0409] The EDA measurement unit (5100) can measure the skin conductance of the user through electrodes. Also, the EDA measurement unit (5100) can include a plurality of electrodes that can contact the user's skin. For example, the EDA measurement unit (5100) can include two electrodes that respectively serve as the (+) pole and the (-) pole.

[0410] According to an embodiment, the distance between the electrodes can be designed to be different according to the thickness of the user's stratum corneum. For example, the distance between the electrodes can be designed to be greater than the thickness of the stratum corneum.

[0411] At this time, a user with a thin stratum corneum can use the wearable device (1000) including the EDA sensor (5000) with a smaller distance between the electrodes than a user with a thick stratum corneum.

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

[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] Here, when a DC current flows, the measurement accuracy at that time may be improved compared to when an AC current flows, but there may also be a possibility of hair root damage.

[0415] On the other hand, when an AC current flows, the measurement accuracy at that time may be reduced compared to when a DC current flows, but there may also be a low possibility of hair root damage.

[0416] According to an embodiment, the EDA measurement unit (5100) can 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. At this time, the low-pass filter or the amplifier may be included in the EDA measurement unit (5100) or may be included in the EDA calculation unit (5200).

[0417] The EDA measurement unit (5100) can obtain a result value obtained by sensing the skin conductance of a user through a current source connected to an electrode. The EDA measurement unit (5100) can also improve the accuracy of 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] Also, when 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 (for example, 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 also obtain the skin conductance data without processing the result value. At this time, the EDA measurement unit (5100) and the EDA calculation unit (5200) are not separated and are one unit or module.

[0422] According to an embodiment, the EDA calculation unit (5200) can filter the result value of the EDA measurement unit (5100) using a filter. For example, the EDA calculation unit (5200) can filter the result value of the EDA measurement unit (5100) using a Schmitt trigger filter or a recursive moving filter, but is not limited thereto, and other filters can also be used.

[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 other values according to Ohm's law. Specifically, for example, 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 according to Ohm's law. Examples of converting voltage to current or resistance and converting current to resistance or voltage are also possible.

[0425] For example, the EDA calculation unit (5200) can separate the result value of the EDA measurement unit (5100) into Tonic EDA and Phasic EDA. 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). At this time, SCL is the logarithmic value of the result value.

[0427] Also, 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 value of the EDA measurement unit (5100). The listed parameters can be understood in more detail from the description of FIG. 34, and specific descriptions are omitted here.

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

[0429] Also, 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 calculated 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] Also, when 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) can receive the skin conductance data that is the calculation result of the EDA calculation unit (5200). The EDA output unit (5300) can output the skin conductance data. The EDA output unit (5300) may be a visual, auditory, and / or tactile output, but is not limited thereto, and can be implemented in various forms.

[0433] For example, the EDA output unit (5300) can be implemented by a display that outputs video, a speaker that outputs sound, haptics that generates vibration, and / or other various forms of output means.

[0434] Instead of a device that autonomously outputs information externally, the EDA output unit (5300) can also be implemented in the form of an output interface that connects an external output device that outputs information to the wearable device (1000).

[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 a 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 function as the aforementioned device output unit (1200).

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

[0439] The EDA storage unit (5400) can receive the skin conductance data that 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 necessary for the operation of the EDA sensor (5000). The EDA storage unit (5400) can store the 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), a solid - state drive (SSD), a flash memory, a ROM, a RAM, or cloud storage, etc. However, it is not limited thereto, and the EDA storage unit (5400) can be implemented with various modules for storing data.

[0442] The EDA storage unit (5400) can 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 aforementioned device memory unit (1500).

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

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

[0446] Also, when the EDA sensor (5000) is not physically the same device as the wearable device (1000), the EDA storage unit (5400) can transmit the value stored in the device memory unit (1500) through the EDA communication unit (5500).

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

[0448] The EDA communication unit (5500) can include one or more modules that enable communication. The EDA communication unit (5500) can include a module that enables communication with an external device through a wired method. Or, the EDA communication unit (5500) can include a module that enables communication with an external device through a wireless method.

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

[0450] Specifically, for example, the EDA communication unit (5500) can be composed of a wired communication module that connects to the Internet etc. through a LAN, a mobile communication module such as LTE that connects to a mobile communication network through a mobile communication base station and transmits / receives data, a short-range communication module that uses a WLAN series communication method such as Wi-Fi or a WPAN series communication method such as Bluetooth (registered trademark) or ZigBee, a satellite communication module that uses GNSS such as GPS, or a combination thereof.

[0451] The EDA communication unit (5500) can perform the same functions as the aforementioned 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 components as the device communication unit (1300) of the wearable device (1000).

[0453] The EDA control unit (5600) can perform a function of comprehensively 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 implemented by a computer or a similar device in the form of hardware, software, or a combination thereof. Hardware-wise, the EDA control unit (5600) is provided in the form of an electronic circuit such as a CPU chip that processes electrical signals to perform control functions, and software-wise, it can be provided in the form of a program that drives the hardware EDA control unit (5600).

[0455] According to one example, the EDA control unit (5600) can control a current to flow through the skin through an electrode so that the EDA measurement unit (5100) can measure the skin conductance of the user. Also, the EDA control unit (5600) can control the EDA measurement unit (5100) to transmit the result value to the EDA calculation unit (5200), the EDA communication unit (5500), or the EDA storage unit (5400).

[0456] Also, according to the embodiment, the EDA control unit (5600) can control such that the EDA calculation unit (5200) receives the result value of the EDA measurement unit (5100). Also, the EDA control unit (5600) can control such that the EDA calculation unit (5200) obtains data on skin conductance. Also, the EDA control unit (5600) can control such that the EDA calculation unit (5200) can transmit the data on skin conductance to the EDA output unit (5300), the EDA storage unit (5400), and the EDA communication unit (5500).

[0457] Also, according to the embodiment, the EDA control unit (5600) can control such that the EDA output unit (5300) receives the data on skin conductance, which is the calculation result of the EDA calculation unit (5200). Also, the EDA control unit (5600) can control such that the EDA output unit (5300) outputs the data on skin conductance.

[0458] Also, according to the embodiment, the EDA control unit (5600) can control such that the EDA storage unit (5400) receives the data on skin conductance. Also, the EDA control unit (5600) can control such that the EDA storage unit (5400) stores the data on skin conductance.

[0459] Also, according to the embodiment, the EDA control unit (5600) can control such that the EDA communication unit (5500) communicates with an external device.

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

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

[0462] In the following, unless otherwise specified, the operation of the EDA sensor (5000) is interpreted to be executed under the control of the EDA control unit (5600).

[0463] FIG. 32 is a diagram showing an apparatus for measuring skin conductance according to an embodiment.

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

[0465] In FIG. 32, only the smartwatch (6100) and the smart ring (6200) are illustrated as apparatuses capable of measuring skin conductance, but these are merely some examples described for convenience of explanation and are not limited thereto.

[0466] For example, the apparatus for measuring skin conductance may be a wristband that can be worn on the user's wrist, a wearable sock that can be worn on the user's foot in the form of a sock, a wearable patch that can be attached to the user's skin, a wearable hairband that can be worn on the user's head, a device that can be worn on the user's ear in the form of an earring, a device that can be sandwiched in the form of earphones, and a device in the form of a lens that can be inserted into the user's eye, and is not limited to the examples listed in this specification and can be embodied in various forms.

[0467] The wearable device in FIG. 32 can include the EDA sensor (5000) of FIG. 31.

[0468] According to an embodiment, the smartwatch (6100) in FIG. 32(a) can include an EDA sensor (5000) in the main body (6110). For example, the main body (6110) of the smartwatch (6100) can include an EDA measurement unit (5100) at a portion that touches the user's skin to measure the user's skin conductance.

[0469] Specifically, the electrodes of the EDA measurement unit (5100) are arranged at the rear portion of the main body (6110) to pass an electric current through the user's skin to measure the user's skin conductance.

[0470] According to another embodiment, the smartwatch (6100) in FIG. 32(a) can include a part of the configuration of the EDA sensor (5000) in the main body (6110) and can include another part of the configuration of the EDA sensor (5000) in the band region (6120). For example, the band region (6120) of the smartwatch (6100) can include an EDA measurement unit (5100) at a portion that touches the user's skin to measure the user's skin conductance.

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

[0472] According to an embodiment, the smart ring (6200) in FIG. 32(b) can include an EDA sensor (5000). For example, the smart ring (6200) can include an EDA measurement unit (5100) at a portion that touches the user's skin to measure the user's skin conductance.

[0473] Specifically, the electrodes (6210) of the EDA measurement unit (5100) are arranged in the inner region of the smart ring (6200) to pass an electric current through the user's skin to measure the user's skin conductance.

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

[0475] Referring to FIG. 33, the graph of skin conductance can be represented by skin conductance data (7110) over time. 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 the tonic component (7120) and the phasic component (7130).

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

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

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

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

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

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

[0484] According to an embodiment, the tonic component (7120) and the phasic component (7130) can be obtained through the operation of the EDA calculation unit (5200) of the EDA sensor (5000). According to an embodiment, the tonic component (7120) and the phasic component (7130) can be obtained through the operation of the server control unit (3300) of the monitoring server (3000) that has received the skin conductance data. According to an embodiment, the tonic component (7120) can be used as an indicator representing the magnitude or the occurrence transition of the skin conductance. The biological information of the user can be inferred by using the magnitude or the occurrence transition of the skin conductance. For example, based on the overall change of the tonic component (7120), the monitoring server (3000) can know the change in skin temperature or body temperature.

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

[0486] According to the embodiment of the present invention, the monitoring server (3000) can obtain the tonic component (7120) and / or the phasic component (7130) based on the skin conductance data, and can perform monitoring of the user's thyroid function abnormality with reference to the obtained 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 rest interval, and if the calculated monitoring skin conductance is higher than the reference skin conductance, it can be determined that the user has thyroid abnormality.

[0488] FIG. 34 is a diagram showing a skin conductance graph according to another embodiment.

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

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

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

[0492] Also, for example, the latency period (7230) can be calculated based on the stimulus onset point (7210) and the response onset point (7220). Specifically, the latency period (7230) is the period from the stimulus onset point (7210) to the response onset 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, when the latency period (7230) is short, the monitoring server (3000) can grasp that the degree of change in the user's skin conductance is rapid.

[0494] Also, for example, when the latency period (7230) is short, the monitoring server (3000) can grasp that the user's mood changes rapidly, the user is under stress, or the changes in the user's autonomic nervous system are rapid.

[0495] According to an embodiment, the monitoring server (3000) can adjust the sensitivity of the amount of change in skin conductance by adjusting a reaction threshold (7240). For example, when the reaction threshold (7240) is decreased, the frequency of change in skin conductance may increase. Also, for example, when the reaction threshold (7240) is increased, the monitoring server (3000) can recognize that the frequency of change in skin conductance has decreased and that the skin conductance has changed only for a relatively large amount of change.

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

[0497] According to an embodiment, based on the peak point (7250), the monitoring server (3000) can know the maximum value of the skin conductance data during a certain period and can grasp the change in the user's mood, stress, or autonomic nervous system. For example, when the peak point (7250) is large, the monitoring server (3000) can be recognized that the change in the user's mood is large, the amount of stress is large, or the change in the autonomic nervous system is large.

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

[0499] At this time, the magnitude from the peak point (7250) to the certain value is the amplitude (7270) of the skin conductance data. However, the amplitude (7270) is the magnitude from the peak point (7250) to another value not related to the recovery period (7260), rather than a certain value related to the recovery period (7260).

[0500] At this time, the recovery period (7260) may be related to the latent period (7230), or the period from the reaction time point (7220) to the peak point (7250).

[0501] For example, when the period from the latent period (7230) or the reaction time point (7220) to the peak point (7250) is short, the recovery period (7260) may also be short. Also, for example, when the period from the latent period (7230) or the reaction time point (7220) to the peak point (7250) is long, the recovery period (7260) may also be long.

[0502] According to the embodiment, based on the recovery period (7260) or the amplitude (7270), the monitoring server (3000) can grasp the changes in the user's mood, stress, or autonomic nervous system. For example, when the recovery period (7260) is short, the monitoring server (3000) can be grasped that the user's mood has changed rapidly, there is a lot of stress, or the change in the autonomic nervous system has become rapid.

[0503] Also, for example, when the amplitude (7270) is large, the monitoring server (3000) can be grasped that the user's mood has changed rapidly, there is a lot of stress, or the change in the autonomic nervous system has become rapid.

[0504] According to the embodiment of the present embodiment, the monitoring server (3000) can obtain the above-mentioned parameters based on the skin conductance data, and can confirm the section with a lot of stress or a rapid change in the autonomic nervous system based on the obtained parameters.

[0505] In one example, when performing thyroid function monitoring, the monitoring server (3000) can calculate the monitored skin conductance with reference to the parameters described in the description of FIG. 34. The interval used for calculating the monitored skin conductance may not overlap with the interval during which excessive stress is received or the autonomic nervous system changes rapidly.

[0506] The monitoring server can calculate SCFr, SCRm, SCRpl, SCRd, SCRpr, and / or SCRrr based on the parameters described in the description of FIG. 34.

[0507] For example, SCFr is an index for the period from the magnitude of the stimulation start point (7210) to the magnitude of the peak point (7250). Also, for example, SCRr is an index related to the latency period (7230).

[0508] Also, for example, SCRm is an index related to the reaction start point or reaction threshold (7240). Also, for example, SCRol is an index related to the stimulation start point (7210).

[0509] Also, for example, SCRpl is an index related to the reaction start point (7220). Also, SCRpl is an index related to the reaction start point (7220) or the peak point (7250). Also, for example, SCRd is an index related to the reaction start point (7220) or the latency period (7230).

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

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

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

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

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

[0515] From the skin conductance data, the monitoring server (3000) can understand the mood, stress, or changes in the autonomic nervous system of the user. Also, the skin conductance data can be used to confirm the presence or absence of false statements by the user. Further, the skin conductance data can be used to analyze the actions or statements of criminals.

[0516] According to an embodiment, based on the skin conductance data, the monitoring server (3000) can determine whether there is an abnormality in the thyroid function of the user.

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

[0518] For example, the autonomic nervous system function of a user suffering from hyperthyroidism may be excited. Also, the autonomic nervous system function of a user suffering from hypothyroidism may be decreased. At this time, if the presence or absence of the excited state of the autonomic nervous system function is grasped based on the skin conductance data, the monitoring server (3000) can determine whether the user has hyperthyroidism or hypothyroidism.

[0519] Specifically, when the skin conductance data of a user for a certain period (for example, the monitoring period) appears above a certain value, the monitoring server (3000) can grasp that the autonomic nervous system function of the user is in an over-excited state compared to a person with normal autonomic nervous system function, and can determine that the user has hyperthyroidism.

[0520] Also, specifically, when the skin conductance data of a user for a certain period appears below a certain value, the monitoring server (3000) can grasp that the autonomic nervous system function of the user is in a state of excessive decline compared to a person with normal autonomic nervous system function, and can determine that the user has hypothyroidism.

[0521] Regarding the method of determining the presence or absence of thyroid function abnormality of a user using skin conductance data, detailed content will be described later.

[0522] When determining the presence or absence of thyroid function abnormality of a user based on skin conductance data, the user can determine the presence or absence of thyroid function abnormality through non-invasion without undergoing a hormone test.

[0523] Also, when determining the presence or absence of thyroid function abnormality of a user based on skin conductance data, during the daily life of the user, the presence or absence of thyroid function abnormality of the user can be naturally determined.

[0524] For example, conventional testing methods for examining thyroid function (e.g., hormone testing) may cause the user's nervousness and affect the results, but the method for determining the presence or absence of thyroid function abnormalities according to this embodiment can prevent the user from getting nervous.

[0525] Referring to FIG. 35, skin conductance data can be segmented around the sleep start time (7310).

[0526] According to an embodiment, the sleep start time (7310) is a time point determined by the input of the user through the wearable device (1000). For example, the user can input information regarding the start of sleep into the wearable device (1000), and the sleep start time (7310) is the time point determined by the said information.

[0527] According to another embodiment, the sleep start time (7310) is a time point determined by the judgment of the wearable device (1000). For example, the sleep start time (7310) is a time point determined by a plurality of sensors of the wearable device (1000) that can detect the movement of the user.

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

[0529] Also specifically, the sleep start time (7310) can be determined by a section in which the heart rate extracted by the heart rate monitor of the wearable device (1000) is below a certain value. Also specifically, the sleep start time (7310) can be determined by a section in which the result of the gyroscope of the wearable device (1000) is below a certain value.

[0530] Specifically, the sleep start time (7310) can be determined according to the sleep pattern of the user grasped through the wearable device (1000). For example, the sleep start time (7310) is the time when the user's sleep pattern enters a section that is not a wake section (e.g., REM, non-REM, SWS) from a wake section.

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

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

[0533] Also specifically, the sleep start time (7310) is the time when the decreasing section (7440) starts. A detailed description of the decreasing section (7440) will be given later.

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

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

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

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

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

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

[0540] FIG. 36 is a diagram for explaining a rest period in a skin conductance data graph according to an embodiment. Referring to FIG. 36, the skin conductance data after the sleep start time (7310) can decrease compared to before the sleep start time (7310).

[0541] According to an embodiment, the skin conductance data after the sleep start time (7310) can include a decreasing section (7440) and a rest period (7450). At this time, the rest period (7450) is a section that starts after the decreasing section (7440).

[0542] According to an embodiment, the rest period (7450) is a section in which the fluctuation of the skin conductance data holds within a certain range (7410). For example, the rest period (7450) is a section 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 μS. As a desirable example, the size of the first range (7410) is 2 μS. As a more desirable example, the size of the first range (7410) is 1 μS. However, the first range (7410) can have other optimal values selected according to the characteristics of the user's skin, and thus is not limited to the above-described numerical values.

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

[0545] The decreasing interval (7440) is an interval in which the skin conductance data after the sleep start time (7310) decreases by the first range (7410) or more. For example, the decreasing interval (7440) may be an interval in which the skin conductance data after the sleep start time (7310) decreases by 1 μS or 2 μS or more, but is not limited thereto, and is an interval in which the skin conductance data decreases by a different numerical value or more.

[0546] Also, the decreasing interval (7440) and the rest interval (7450) can be divided by the rest interval start time (7430).

[0547] According to the embodiment, the rest interval start time (7430) is an entry time when the skin conductance data enters with an increasing trend among the times when the variation of the skin conductance data is within the first range (7410). For example, the rest interval start time (7430) is the earliest time when the slope is positive among the intervals where the variation of the skin conductance data is within 1 μS.

[0548] At this time, the difference (7420) between the skin conductance data at the sleep start time (7310) and the skin conductance data at the rest interval start time (7430) may be larger 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, based on the skin conductance data during the rest period (7450), the monitored skin conductance can be calculated. The monitored skin conductance can be determined by, for example, the average value, median, standard deviation, or moving average of the skin conductance data during the rest period (7450). The monitoring server (3000) can compare the monitored skin conductance with the reference skin conductance to determine whether the user has thyroid dysfunction.

[0550] According to the embodiment, the end point of the rest period (7450) can be the point in time when the variation range of the skin conductance data goes outside the first range. Or, the end point of the rest period (7450) can be the point in time when the magnitude of the skin conductance data is greater than or equal to, or less than, a certain value.

[0551] FIG. 37 is a diagram for explaining the rest period in a graph of skin conductance data according to the embodiment. Referring to FIG. 37, a plurality of rest periods (7551, 7554) can exist after the sleep start point (7310). The monitoring server (3000) can confirm a plurality of rest periods (7551, 7554) after the sleep start point (7310).

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

[0553] According to the embodiment, the first rest period (7551) and the second rest period (7554) are sections in which the variation of the skin conductance data holds within a certain range (7510).

[0554] For example, the first rest interval (7551) and the second rest interval (7554) are intervals in which the variation of the skin conductance data is within the first range (7510). Specifically, for example, the size of the first range (7510) may be 1 uS or 2 uS, but is not limited thereto and may be other numerical values.

[0555] The decreasing interval (7540) is an interval in which the skin conductance data after the sleep start time point (7310) decreases by a second range or more. At this time, the second range may be larger than the first range. In one example, the second range is 2 uS and the first range (7510) is 1 uS. The decreasing interval (7540) is an interval in which the skin conductance data after the sleep start time point (7310) decreases by the first range (7510) or more. For example, the decreasing interval (7540) may be an interval in which the skin conductance data after the sleep start time point (7310) decreases by 1 uS or 2 uS or more, but is not limited thereto, and is an interval in which the skin conductance data decreases by other numerical values or more.

[0556] The first rest interval (7551) can include the first rest interval start time point (7530) and the first rest interval end time point (7552). The monitoring server (3000) can distinguish the decreasing interval (7540) and the first rest interval (7551) based on the first rest interval start time point (7530).

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

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

[0559] For example, the start time (7530) of the first rest period and the start time (7553) of the second rest period are the earliest times among the times when the slope is positive within a period where the variation in the skin conductance data is within 2 uS.

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

[0561] According to the embodiment, the end time (7552) of the first rest period and the end time (7555) of the second rest period can be times when the variation in the skin conductance data deviates from the first range (7510). Or, the end time (7552) of the first rest period and the end time (7555) of the second rest period are times when the magnitude of the skin conductance data becomes equal to or greater than or equal to or less than a certain value.

[0562] At this time, the monitoring server (3000) can grasp the calculated first rest period (7551) and the second rest period (7554) as the user's true sleep period. Also, through the skin conductance data in the plurality of rest periods (7551, 7554), the monitoring server (3000) can determine the presence or absence of thyroid function abnormality of the user.

[0563] For example, based on the average value, median, standard deviation, or moving average of the skin conductance data in the plurality of rest periods (7551, 7554), the monitoring server (3000) can determine the presence or absence of thyroid function abnormality of the user.

[0564] For example, the monitoring server (3000) can also use the average value of the overall skin conductance data of the first rest period (7551) and the second rest period (7554), or can also use the average value of the first rest period (7551) and the average value of the second rest period (7554) respectively.

[0565] FIG. 38 is a diagram for explaining a rest period in a graph of skin conductance data according to another embodiment. Referring to FIG. 38, a plurality of rest periods (7651, 7654) can exist after the sleep start time (7310).

[0566] Since the content for the decreasing section (7640) overlaps with the content of the decreasing section (7540) in FIG. 37, detailed content is omitted.

[0567] Also, since the content for the first rest period (7651) and the second rest period (7654) overlaps with the content of the first rest period (7551) and the second rest period (7554) in FIG. 37, detailed content is omitted.

[0568] Also, since the content for the first range (7610) overlaps with the content of the first range (7510) in FIG. 37, detailed content is omitted.

[0569] Also, since the content for the start time (7630) of the first rest period, the end time (7652) of the first rest period, the start time (7653) of the second rest period, and the end time (7655) of the second rest period overlaps with the content of the start time (7530) of the first rest period, the end time (7552) of the first rest period, the start time (7553) of the second rest period, and the end time (7555) of the second rest period in FIG. 37, detailed content is omitted.

[0570] According to the embodiment, the calculated value of the skin conductance data in the first rest period (7651) can be different from the calculated value of the skin conductance data in the second rest period (7654).

[0571] In one example, the maximum skin conductance of the first rest interval (7651) may be smaller than the maximum skin conductance of the second rest interval (7654). In other examples, the minimum skin conductance of the first rest interval (7651) may be smaller than the minimum skin conductance of the second rest interval (7654). In still other examples, the average skin conductance of the first rest interval (7651) may be smaller than the average skin conductance of the second rest interval (7654).

[0572] Also, without being limited thereto, the numerical values calculable based on the skin conductance data in the first rest interval (7651) can be different from the numerical values calculable based on the skin conductance data in the second rest interval (7654).

[0573] According to an embodiment, the numerical range of the skin conductance data in the first rest interval (7651) can be different from the numerical range of the skin conductance data in the second rest interval (7654).

[0574] In one example, the numerical range of the skin conductance data in the first rest interval (7651) may not overlap with the numerical range of the skin conductance data in the second rest interval (7654). In other examples, the numerical range of the skin conductance data in the first rest interval (7651) can partially overlap with the numerical range of the skin conductance data in the second rest interval (7654).

[0575] Specifically, for example, the skin conductance data of the first rest interval (7651) can have a numerical value of 1 μS to 2 μS. Also, the skin conductance data of the second rest interval (7654) can have a numerical value of 2 μS to 3 μS.

[0576] At this time, the variation in the first rest interval (7651) and the variation in the second rest interval (7654) hold within a 1 μS range, or the numerical ranges of the first rest interval (7651) and the second rest interval (7654) can be different.

[0577] The monitoring server (3000) can recognize the calculated first rest interval (7651) and second rest interval (7654) as the user's true sleep interval. Also, the monitoring server (3000) can determine the presence or absence of thyroid function abnormality of the user based on the skin conductance data in a plurality of rest intervals (7551, 7554).

[0578] FIG. 39 is a diagram for explaining a rest interval in a graph of skin conductance data according to another embodiment. Since the content for the decreasing interval (7740) overlaps with the content of the decreasing interval (7440) in FIG. 36, detailed content is omitted.

[0579] Also, since the content for the rest interval (7751) overlaps with the content of the rest interval (7450) in FIG. 36, detailed content is omitted.

[0580] Also, since the content for the first range (7710) overlaps with the content of the first range (7410) in FIG. 36, detailed content is omitted.

[0581] Referring to FIG. 39, a rest interval (7751) and noise intervals (7760, 7770) can exist after the sleep start time (7310). For example, a first noise interval (7760) and a second noise interval (7770) can exist after the sleep start time (7310).

[0582] According to the embodiment, the variation of the first noise interval (7760) and the variation of the second noise interval (7770) can hold within the first range (7710).

[0583] However, when the monitoring server (3000) determines the presence or absence of thyroid function abnormality of the user, it may use the skin conductance data of the rest interval (7751), but not use the skin conductance data of the noise intervals (7760, 7770).

[0584] Since it is difficult to regard the skin conductance data in the noise interval (7760, 7770) as the user's true sleep interval, it is data that must be removed when determining the presence or absence of the user's thyroid function abnormality.

[0585] According to the embodiment, the variation of the first noise interval (7760) holds within the first range (7710) like the variation of the rest interval (7751), but the numerical range of the first noise interval (7760) may be different from the numerical range of the rest interval (7751).

[0586] At this time, the minimum value of the first noise interval (7760) may be greater than the first limit value (7780). Therefore, although the variation of the skin conductance data in a certain interval holds within the first range (7710), when the minimum value of the certain interval is greater than or equal to the first limit value (7780), the certain interval can be calculated as a noise interval.

[0587] Also, at this time, the average value of the first noise interval (7760) may be greater than the first limit value (7780). Therefore, although the variation of the skin conductance data in a certain interval holds within the first range (7710), when the average value of the certain interval is greater than or equal to the first limit value (7780), the certain interval can be calculated as a noise interval.

[0588] According to the embodiment, the variation of the second noise interval (7770) holds within the first range (7710) like the variation of the rest interval (7751), but the numerical range of the second noise interval (7770) can be different from the numerical range of the rest interval (7751).

[0589] At this time, the maximum value of the second noise interval (7770) may be less than the second limit value (7790). Therefore, although the variation of the skin conductance data in a certain interval holds within the first range (7710), when the maximum value of the certain interval is less than or equal to the second limit value (7790), the certain interval can be calculated as a noise interval.

[0590] Also, at this time, the average value of the second noise section (7770) may be smaller than the second limit value (7790). Therefore, although the variation in the skin conductance data for a certain section holds within the first range (7710), if the average value of the certain section is equal to or less 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, although the variation in the skin conductance data for a certain section holds within the first range (7710), if the absolute value of the certain section is a value not found in the user's true sleep section, the certain section can be extracted as a noise section.

[0592] Even if the variation in the skin conductance data for a section extracted as a noise section holds within the first range (7710), it may be excluded from the rest section.

[0593] When the monitoring server (3000) confirms a rest section for calculating the monitored skin conductance, it can confirm, as the rest section, a section that starts after a skin conductance decrease section of the first range (7710) or more and varies within the first range (7710).

[0594] When the calculated value of a partial section among the confirmed sections deviates from a determined reference value, the monitoring server (3000) can exclude the partial section from the rest section and calculate the monitored skin conductance based on the skin conductance data corresponding to the rest section.

[0595] FIG. 40 is a diagram for explaining a rest section in a graph of skin conductance data according to another embodiment. Since the content for the decrease section (7840) overlaps with the content of the decrease section (7440) in FIG. 36, detailed content is omitted.

[0596] Also, since the content for the rest section (7850) overlaps with the content of the rest section (7450) in FIG. 36, detailed content is omitted.

[0597] Also, since the content for the first range (7810) overlaps with the content of the first range (7410) in Fig. 36sw, detailed content is omitted.

[0598] Referring to Fig. 40, a decreasing section (7840) and a rest section (7850) can exist after the sleep start time (7310). The decreasing section (7840) and the rest section (7850) can be divided around the rest section start time (7830).

[0599] Separate from the rest section start time (7430) in Fig. 36, the rest section start time (7830) in Fig. 40 can be defined by other criteria.

[0600] According to an embodiment, the rest section start time (7830) is the earliest time during a certain period when the variation of the skin conductance data is within the first range (7810). At this time, the skin conductance data at the rest section start time (7830) is below a certain value.

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

[0602] Also, the rest section (7850) can include the rest section end time, which is the latest time during the certain period. The skin conductance data at the rest section end time can be above or below a certain value and can have a value that is difficult to regard as the user's true sleep section.

[0603] Fig. 41 is a diagram for explaining the rest section in a graph of skin conductance data according to another embodiment.

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

[0605] According to an embodiment, the amount of change in skin conductance data can be obtained through the first derivative of the skin conductance data. For example, the amount of change in skin conductance data can be obtained based on the slope of the skin conductance data.

[0606] There are differences in the amount of change in skin conductance data before and after the sleep start time point (7310). For example, the average value of the amount of change before the sleep start time point (7310) may be larger than the average value of the amount of change after the sleep start time point (7310).

[0607] According to an embodiment, there are differences in the storm regions of the skin conductance data before and after the sleep start time point (7310). At this time, the storm region can be calculated based on the degree of change frequency of the skin conductance data.

[0608] Also, at this time, the storm region is a region having a high frequency. For example, the storm region may mean a region having 4 to 10 peaks per minute (4-10peaks / min).

[0609] For example, the number of storm regions before the sleep start time point (7310) may be more than that after the sleep start time point (7310). Also, for example, the frequency of the storm region before the sleep start time point (7310) may be more than that after the sleep start time point (7310).

[0610] Also, for example, the average value of the peaks of the storm region before the sleep start time point (7310) may be much larger than the average value of the peaks of the storm region after the sleep start time point (7310).

[0611] At this time, based on the average value of the data change amount, the frequency of data change, the number of storm regions, or the occurrence frequency of the storm region, the monitoring server (3000) can determine the presence or absence of thyroid function abnormality of the user.

[0612] For example, when the above parameters are equal to or greater than a certain value, the monitoring server (3000) can determine that the user has hyperthyroidism. Also, for example, when the above parameters are equal to or less than a certain value, the monitoring server (3000) can determine that the user has hypothyroidism.

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

[0614] For example, the user's sleep pattern can be obtained by polysomnography (PSG). Also, for example, the user's sleep pattern can be obtained by sensing a plurality of sensors (e.g., an accelerometer, etc.) included in a smartwatch.

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

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

[0617] Also, for example, the storm area may have a high probability of occurring in the N-REM2 section or the SWS section. At this time, the monitoring server (3000) can exclude the N-REM2 section or the SWS section from the rest period. Or, at this time, the monitoring server (3000) can reduce the proportion of the N-REM2 section or the SWS section in the rest period.

[0618] According to another embodiment, in the method of extracting a rest period, the sleep pattern of the user can be considered together with the skin conductance data.

[0619] For example, the monitoring server (3000) can primarily extract a rest period from the skin conductance data after the sleep start time (7310) by the method described with reference to FIGS. 36 to 40.

[0620] Also, for example, the monitoring server (3000) can obtain information on the sleep pattern of the user from an external device or a wearable device (1000). At this time, the information on the sleep pattern can be obtained through a heart rate sensor or a motion sensor of the wearable device (1000) or an external device.

[0621] At this time, the monitoring server (3000) can secondarily extract a rest period from the primarily extracted rest period, excluding the portions where the sleep pattern of the user is in the SWS period and / or the N-REM2 period.

[0622] Alternatively, at this time, the monitoring server (3000) can secondarily extract the portions where the sleep pattern of the user is in the REM period as the rest period from the primarily extracted rest period.

[0623] Therefore, the monitoring server (3000) can use the secondarily extracted rest period as the true rest period and determine the abnormality of the thyroid function of the user based on the skin conductance data in the rest period.

[0624] FIG. 42 shows a flowchart of the method for extracting a rest period 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 the first interval (S610). At this time, the step of extracting the first interval (S610) may include a step of extracting, as the first interval, an interval within the first range where the variation of the skin conductance data is within the first range.

[0627] For example, the monitoring server (3000) may 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) may receive skin conductance data from the EDA measurement unit (5100) and extract the first interval.

[0629] At this time, the difference between the maximum value and the minimum value of the extracted first interval may be smaller than or equal to the size of the first range. For example, the difference between the maximum value and the minimum value of the first interval is within 3 uS. Specifically, the difference between the maximum value and the minimum value of the first interval is 2 uS.

[0630] Also, at this time, the start point of the extracted first interval is the pause interval start time point (7430) described with reference to FIG. 36 or the pause interval start time point (7830) described with reference to FIG. 40.

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

[0632] For example, the first interval is an interval in which the variation or derivative value of the phasic component (7130) is within the first range. Also, for example, the first interval is an interval in which the maximum value of the phasic component (7130) is equal to or less than a predetermined numerical value. Specifically, the first interval is an interval in which the maximum value of the phasic component (7130) is 2 uS or less.

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

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

[0635] Also, at this time, the end point of the second interval may be the start time point (7430) of the rest interval described with reference to FIG. 36. Or, the end point of the second interval is a time point before the start time point (7430) of the rest interval in FIG. 36.

[0636] Also, at this time, the difference in skin conductance data between the starting point and the end point of the second interval may be equal to or greater than the size of the first range.

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

[0638] For example, the step S630 may include a step of checking whether the minimum value of the first interval is greater than the first value (A) by the monitoring server (3000) or the EDA calculation unit (5200). Also, for example, the step S630 may include a step of checking whether the maximum value of the first interval is less than the 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 check the size of the first interval by using the tonic component (7120) of the skin conductance data.

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

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

[0642] If the step S630 is not passed, it may be impossible to extract the rest interval in the user's true sleep interval.

[0643] For example, when the user wakes up due to a hot external environment during sleep and does not move in the awake state, the variation of the first interval may be within the first range, and there may be a second interval before the first interval.

[0644] At this time, the value of the first interval may be greater than the second value (B). At this time, since the user is in an awake state rather than asleep, this interval may have to be excluded from the rest interval.

[0645] Also, for example, when the user wakes up due to a cold external environment during sleep and is awake without movement, the value of the first interval may also be smaller than the first value (A). Also at this time, since the user is in an awake state, this interval may have to be excluded from the rest interval.

[0646] When the value of the first interval falls within a predetermined numerical range, the step S640 is executed, and when it does not fall within the range, the step S610 of extracting a new first interval again can be executed.

[0647] According to the embodiment, the monitoring server (3000) can include the first interval confirmed through steps S610 to S630 in the rest interval (S640).

[0648] The step of including the first interval in the rest interval and then checking the presence or absence of thyroid function abnormality of the user based on the skin conductance data in the rest interval can be executed later.

[0649] According to the embodiment, the thyroid function of the user can be monitored through the skin conductance data. At this time, it can follow the thyroid function monitoring method of FIG. 5.

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

[0651] The wearable device (1000) can acquire the skin conductance information of the user. The wearable device (1000) can acquire the skin conductance information of the user wearing the wearable device (1000). At this time, the acquisition of the skin conductance information can be executed at a certain period.

[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 skin conductance information of the user in the first period.

[0653] Also, for example, the device sensor unit (1400) of the wearable device (1000) includes a motion sensor, and the motion sensor can be used to acquire the motion information of the user in the second period.

[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 skin conductance information of the user to the user terminal (2000). For example, the wearable device (1000) can transmit the skin conductance information of the user to the user terminal (2000) at the same time as it acquires the skin conductance information of the user.

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

[0657] The wearable device (1000) can transmit biometric information of one or more types of users 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 motion information.

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

[0659] In another example, the biometric information transmitted by the wearable device (1000) is skin conductance information related to time and motion information related to time. In still another example, the biometric information transmitted by the wearable device (1000) is in a form in which time, skin conductance information, and motion information are related.

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

[0661] Specifically, when calculating the monitoring data, the method for calculating the monitoring data of FIG. 6 (S1300) can be followed.

[0662] The monitoring server (3000) can confirm a rest period (S1310). For example, the rest period can include a part of the time points after the sleep start time point (7310) in FIG. 35.

[0663] Here, the method for confirming the rest period (S1310) can be replaced by the method for calculating the rest period described in FIG. 42.

[0664] For example, the predetermined conditions of S1310 can include the conditions in the steps S610 to S630 of FIG. 42.

[0665] Specifically, the determined conditions may include whether the variation of the skin conductance data in a section is within a first range. Further, the determined conditions may include whether there is a second section in which the skin conductance data before a section decreases by an amount greater than or equal to the size of the first range. Further, the determined conditions may include whether the size of a section is greater than or equal to a first value and less than or equal to a second value.

[0666] Therefore, redundant explanations for the specific operations executed in the step of confirming the rest period (S1310) are omitted.

[0667] According to an embodiment, the monitoring server (3000) can extract skin conductance information corresponding to a rest period (S1330).

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

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

[0670] At this time, the extracted skin conductance information can be extracted based on the skin conductance data in the rest periods of FIGS. 35 to 41.

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

[0672] Also, for example, the monitoring server (3000) can calculate, as monitoring data, the median of the medians of a plurality of skin conductance data corresponding to each of a plurality of rest intervals included in the monitoring period.

[0673] Also, for example, the monitoring server (3000) can calculate, as monitoring data, the calculated value of the remaining skin conductance data excluding the maximum value and the minimum value among a plurality of skin conductance data corresponding to each of a plurality of rest intervals included in the monitoring period.

[0674] According to the embodiment, when determining an abnormality in the thyroid function of a user, the monitoring server (3000) can use reference data based on skin conductance data. Since a basic explanation of the reference data overlaps with the content of FIG. 7, a detailed explanation is omitted. At this time, the calculation method of the reference data in FIG. 7 can be followed.

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

[0676] At this time, the thyroid state information is information regarding thyroid hormone numerical values obtained by, for example, a blood test of the user. Or, the thyroid state information is information regarding the thyroid state obtained through an interview regarding the user's symptoms.

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

[0678] The calculation period of the reference data is the period when the user's thyroid function corresponds to "normal" according to the thyroid state information.

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

[0680] Since a detailed description of the thyroid state information overlaps with the content of FIG. 7, the detailed description is omitted.

[0681] According to the embodiment, an abnormality in the user's thyroid function can be determined through the reference data. At this time, the reference data calculation method of FIG. 8 can be followed.

[0682] The monitoring server (3000) can determine the calculation period of the reference data (S2310). Since an explanation of the calculation period of the reference data overlaps with the content of FIG. 8, the detailed explanation is omitted.

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

[0684] Here, the method for checking the rest period (S2320) can be replaced by the rest period calculation method described in FIG. 42. Therefore, duplicate explanations are omitted for the specific operations performed in the step of checking the rest period (S2320).

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

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

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

[0688] At this time, the extracted skin conductance information can be extracted based on the skin conductance data in the rest intervals of FIGS. 35 to 41.

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

[0690] Also, for example, the monitoring server (3000) can calculate the median of the medians of a plurality of skin conductance data corresponding to each of a plurality of rest intervals included in the monitoring period as the reference data.

[0691] Also, for example, the monitoring server (3000) can calculate the calculated value of the remaining skin conductance data as the reference data, excluding the maximum and minimum values among a plurality of skin conductance data corresponding to each of a plurality of rest intervals included in the monitoring period.

[0692] According to the embodiment, when the monitoring server (3000) receives thyroid state information that is out of the normal range, the reference data can be calculated by the reference data calculation method of FIG. 9.

[0693] When the monitoring server (3000) receives thyroid state information that deviates from the normal range, since the basic explanation of the reference data calculation method overlaps with the content of FIG. 9, detailed explanation will be omitted.

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

[0695] For example, the monitoring server (3000) can calculate how many ng / dL should increase / decrease for the hormone value of the user associated with the received thyroid state information to correspond to the normal range, and estimate the change amount of skin conductance associated with the increase / decrease.

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

[0697] For example, data regarding the correlation between the hormone values and skin conductance data of a large number of users can be stored in the monitoring server (3000).

[0698] Specifically, statistical data regarding how much the skin conductance data increases approximately when the hormone value increases by about 0.1 ng / dL can be stored in the monitoring server (3000).

[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, and determine (S1500) the abnormality of the user's thyroid function.

[0700] Since the description of the method for determining the abnormality of the user's thyroid function (S1500) overlaps with the content of FIG. 10, a detailed description thereof will be omitted.

[0701] According to the embodiment, the monitoring server (3000) can compare the monitoring data with the reference data. Since the comparison algorithm overlaps with the content of FIG. 11, a detailed description thereof will be omitted.

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

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

[0704] For example, the skin conductance data when the user is wearing the wearable device (1000) and the skin conductance data when not wearing it may have different appearances.

[0705] Also, for example, in the state where the user is wearing the wearable device (1000), the skin conductance data when the user wears the wearable device (1000) correctly and the skin conductance data when not wearing it correctly (for example, when the EDA measurement electrode does not contact the skin) may have different appearances.

[0706] Referring to FIG. 43, between the first time point (t1) and the second time point (t2), the user may not be wearing the wearable device (1000). Or, 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 when the user removes the wearable device (1000), and the second time point (t2) is the time when the user wears the wearable device (1000) again.

[0708] At this time, when the user removes the wearable device (1000), skin conductance data may not be acquired during the non-wearing period.

[0709] Also, for example, in a state where the EDA electrode is in contact with the user's skin, the first time point (t1) is the time when the EDA electrode stops being in contact. Also, for example, in a state where the EDA electrode is not in contact with the user's skin, the second time point (t2) is the time when the EDA electrode comes into contact.

[0710] Also, for example, the first time point (t1) is the time when the moving average of the skin conductance data decreases rapidly (e.g., by a certain value or more), and the second time point (t2) is the time when the moving average of the skin conductance data increases rapidly after the first time point (t1).

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

[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 below a predetermined value. For example, the slope or derivative value of the skin conductance data between the first time point (t1) and the second time point (t2) is below a predetermined value.

[0713] Also, for example, the variation in skin conductance data between the first time point (t1) and the 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 value or median value of the skin conductance data between the first time point (t1) and the second time point (t2) is less than or equal to a certain value compared to the average value 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 still another embodiment, the average value or median value of the skin conductance data between the first time point (t1) and the second time point (t2) may be smaller than the average value or median value of the skin conductance data in the rest interval.

[0716] Specifically, the average value or median value of the skin conductance data between the first time point (t1) and the second time point (t2) can have a difference of about 2 μS to 5 μS compared to the average value or median value of the skin conductance data in the rest interval.

[0717] Also, the average value or median value of the skin conductance data between the first time point (t1) and the second time point (t2) is within 1 μS.

[0718] At this time, the monitoring server (3000) can execute the step of calculating the average value 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 value or median value of the skin conductance data in the rest interval.

[0719] At this time, when the value of the skin conductance data between the first time point (t1) and the second time point (t2) is smaller than the skin conductance data value in the rest interval and is below a certain numerical value (for example, 1 μS, 0.1 μS, or 0.01 μS), it can be determined that the interval between the first time point (t1) and the second time point (t2) is a state where the wearable device (1000) is not worn or not worn correctly.

[0720] According to the embodiment, before executing the step (S610) of extracting the first interval in FIG. 42, a step of first determining whether the wearable device (1000) is worn or is in a correctly worn state can also be executed.

[0721] For example, the monitoring server (3000) can first determine whether the wearable device (1000) is worn or is in a correctly worn state before executing the step (S610) of extracting the first interval.

[0722] If the wearable device (1000) is in a worn state or a correctly worn state, the steps S610 to S640 can be executed. However, if the wearable device (1000) is not worn or not worn correctly, the step S610 may not be executable.

[0723] As described above, for the present invention, the configuration and features have been described based on the embodiments and examples. However, the present invention is not limited to the above description, and it is obvious to those skilled in the technical field to which the present invention belongs that various changes or deformations can be made within the scope of the idea of the present invention. Therefore, such changes or deformations belong to 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 function abnormality, comprising: receiving drug-taking information of the user from an external device, the drug-taking information including at least one of a prescription date and time of a drug related to thyroid function, a drug name, a drug type, a drug dosage, and a drug taking period; selecting a monitoring algorithm to be used for determining whether to output the warning message based on the medication taking information, where 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 threshold 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 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 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.

13. A method for determining whether to output a warning message regarding a user's thyroid function abnormality, 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 drug 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 threshold or more, checking whether a predetermined period has elapsed based on the prescription date and time; 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 the first threshold value or more, checking whether a predetermined period has elapsed based on the prescription date and time; 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 if the monitored heart rate is greater than the reference heart rate by at least the first threshold 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 threshold 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 zero and continues for a predetermined period of time or more, The pause section is determined based on a section in which the user's acceleration is zero and continues 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. 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 and if the thyroid hormone level is within a normal range, calculating the baseline heart rate based on resting heart rates on a number of consecutive days including the day of the test of the thyroid hormone level.

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 of the test of the thyroid hormone level; and and estimating the reference heart rate when the user has a 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; the step of determining whether to output a warning message is performed a number of times greater than the step of selecting the 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 notification of taking a medicine based on the medicine taking period, The step of determining whether to output a notification of taking the medicine is performed a greater number of 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. receiving, for each second period, heart rate information of the user from the external device that measures the heart rate of the user for each 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 so as 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 for carrying out the method according to any one of claims 1 to 14 is recorded on the 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; and a control unit that selects a monitoring algorithm based on the user's drug taking 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 the output of the warning message is determined, wherein: The drug taking information includes at least one of a prescription date and time of a drug related to thyroid function, a drug name, a drug type, a drug dose, and a drug taking 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 smaller 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 comprising:

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 user's drug taking information, wherein the drug taking information includes at least one of prescription date and time, drug name, drug type, drug dose, and drug taking cycle of a drug related to thyroid function; and a control unit that selects a monitoring algorithm based on the medicine taking information, determines whether to output a warning message based on the selected monitoring algorithm, and controls an output unit to output a warning message regarding thyroid function abnormality 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 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 user terminal characterized by:

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