Health monitoring method

Abstract

To provide a health monitoring method enabling a subject to normally monitor daily health without relatively imposing a burden on the subject, helping prevent and improve lifestyle-related diseases, and realizing health and longevity while maintaining sleep quality.SOLUTION: A health monitoring method includes: a biological information acquisition step for using at least a hemodynamometer, a thermometer, and a scale to acquire biological information of a subject; an action information acquisition step for acquiring action information to be body activity information from a wearable device; a health diagnosis result information acquisition step for acquiring at least a brain image and health diagnosis result information including an in vivo endoscopic image; an analysis step for analyzing the acquired biological information, action information and health diagnosis result information about the subject; and an estimating step for using the result of the analysis, the biological information, action information and health diagnosis result information about the subject to estimate a future health condition of the subject.SELECTED DRAWING: Figure 27

Description

[Technical Field]

[0001] The present invention relates to a health monitoring system and a health monitoring method, and more particularly to a health monitoring method suitable for preventing and improving lifestyle-related diseases while maintaining the quality of sleep. [Background technology]

[0002] Recently, research into the relationship between lifestyle-related diseases and sleep has progressed, and it has been shown that lack of sleep has a wide range of effects, including daytime sleepiness and fatigue, an increase in physical and mental complaints such as headaches, emotional instability, and reduced work efficiency related to impaired attention and judgment. Furthermore, it has become clear that when various sleep problems, including sleep deprivation, become chronic, they are associated with an increased risk of developing obesity, high blood pressure, type 2 diabetes, heart disease, and cerebrovascular disease, as well as worsening symptoms, and are involved in an increased mortality rate.

[0003] In the daily health monitoring method, the subject's biological information can be monitored using a biometric device, such as a blood pressure monitor, a weight scale, a smartphone-compatible predictive thermometer, a wristband-type life recorder, a pocket-type activity monitor, an upper arm hoseless blood pressure monitor, or a body composition monitor.

[0004] For example, many of the biometric devices mentioned above are designed to work with various devices from the same manufacturer, and cannot be used in combination with devices from other manufacturers. Also, while wristband-type life recorders can measure sleep 24 hours a day, they have the problem of poor accuracy.

[0005] The results of an annual health check are not enough.

[0006] Frequent medical checkups and diagnoses at hospitals can lead to early detection of illness, but medical expenses can be enormous.

[0007] It is desirable to realize a health monitoring method that enables healthy longevity by performing health monitoring as simply as possible, thereby reducing medical expenses and leading to healthy longevity while maintaining a healthy lifestyle.

[0008] Furthermore, as a recent trend, research is also being conducted into monitoring the spatiotemporal internal environment using cybernetic avatars. For example, an information processing system using cybernetic avatars has been proposed in which an avatar is assigned to an operator to provide a predetermined service (Patent Document 1). [Prior art documents] [Patent documents]

[0009] [Patent Document 1] Japanese Patent Application Publication No. 2023-145956 [Patent Document 2] Japanese Patent Publication No. 2022-40919 [Patent Document 3] Patent No. 6719788 [Patent Document 4] Japanese Patent Application Publication No. 2018-81397 [Patent Document 5] Japanese Patent Application Laid-Open No. 2024-40773 Summary of the Invention [Problem to be solved by the invention]

[0010] The biometric device of Patent Document 2 mentioned above can measure the sleep of a subject, but has the problem of low measurement accuracy. There is also a method in which the subject stays overnight at a hospital and undergoes an overnight polysomnogram test, but while the measurement accuracy is accurate, the problem is that it places too much strain on the subject. The EEG measurement device of Patent Document 2 is useful because it allows sleep measurement at home and achieves measurement accuracy almost similar to that of an overnight polysomnogram test. However, this is insufficient because lifestyle-related diseases are not caused solely by sleep problems.

[0011] The methods described in Patent Documents 3 and 4 above are said to lead to improvements in lifestyle-related diseases, but are not something that can be easily done at home.

[0012] In the above Patent Document 5, weight data is used to generate information on improving lifestyle-related diseases, but there is a problem in that the improvement effect is limited when weight data is used alone.

[0013] The present invention has been devised in view of the above-mentioned problems, and an object of the present invention is to provide a health monitoring system and health monitoring method that enable constant daily health monitoring with relatively little burden on the subject, that helps prevent and improve lifestyle-related diseases while maintaining sleep quality, and that leads to a long and healthy life.

[0014] Another object of the present invention is to provide a health monitoring system and a health monitoring method that are capable of maintaining the quality of sleep and monitoring health and pre-illness with high accuracy.

[0015] Another object of the present invention is to provide a health monitoring method that enables visualization of health monitoring results, encourages behavioral changes in subjects, and enables them to live long, healthy lives.

[0016] It is still another object of the present invention to provide a health monitoring method that can predict the current health condition or the point at which a future disease will worsen. [Means for solving the problem]

[0017] In order to achieve the above-mentioned object, the health monitoring system of the first invention is a health monitoring system that uses a non-invasive biometric device to measure the biometrics of a subject, and has a measurement means that is remotely operated from the outside to grasp the internal condition of the body, such as the organs and digestive system, and measures spatiotemporal internal body environment information using an in-vivo cybernetic avatar that can interact with the body, and a visualization means that visualizes the spatiotemporal internal body environment information measured by the measurement means, and is characterized in that the visualization means synthesizes and displays the measurement results from the biometric device and the measurement results of the spatiotemporal internal body environment information from the in-vivo cybernetic avatar.

[0018] The health monitoring system according to the second invention is the same as that of the first invention, and is characterized in that it has an electroencephalogram (EEG) measuring device for measuring sleep at home, and uses the electroencephalogram (EEG) measuring device to perform a sleep test equivalent to a polysomnogram test.

[0019] The health monitoring system according to the third invention is characterized in that, in the first or second invention, the in vivo cybernetic avatar is a capsule-type device, a helical ring-type device, a stent-type device, or a combination thereof.

[0020] A health monitoring system according to a fourth aspect of the present invention is the health monitoring system of the first or second aspect of the present invention, further comprising a body-wearable device, and the body-wearable device measures the amount of exercise of the subject.

[0021] The health monitoring method of the fifth invention is a health monitoring method using a non-invasive biometric device to measure the biometrics of a subject, and includes a measurement step in which the device is remotely operated from the outside to grasp the internal condition of the body, such as the organs and digestive system, and measures spatiotemporal internal environment information using an in-vivo cybernetic avatar that can interact with the body, and a visualization step in which the spatiotemporal internal environment information measured by the measurement step is visualized.In the first or second invention, the visualization step is characterized in that it combines and displays the measurement results by the biometric device and the measurement results of the spatiotemporal internal environment information by the in-vivo cybernetic avatar.

[0022] The health monitoring method of the sixth aspect of the present invention is characterized by comprising a biometric information acquisition step of acquiring a subject's biometric information using at least a sphygmomanometer, a thermometer, and a weight scale; a behavioral information acquisition step of acquiring behavioral information, which is physical activity information, from a body-wearable device; a health checkup result information acquisition step of acquiring health checkup result information including at least brain images and in-vivo endoscopic images; an analysis step of analyzing the acquired biometric information, behavioral information, and health checkup result information of the subject; and an estimation step of estimating the subject's future health condition using the results of the above analysis and the subject's biometric information, behavioral information, and health checkup result information.

[0023] The health monitoring method according to the seventh invention is the same as that of the sixth invention, and further comprises an electroencephalogram (EEG) measurement step of measuring the electroencephalogram (EEG) of the subject's sleep state at home, and is characterized in that the electroencephalogram (EEG) measurement step performs a sleep test equivalent to a polysomnogram test. [Effects of the Invention]

[0024] According to the present invention having the above configuration, it is possible to realize a health monitoring system and health monitoring method that enables constant daily health monitoring with relatively little burden on the subject, helps prevent and improve lifestyle-related diseases while maintaining sleep quality, and leads to a long and healthy life.

[0025] According to the present invention, it is possible to realize a health monitoring system and a health monitoring method that are capable of maintaining the quality of sleep and monitoring health and pre-illness with high accuracy.

[0026] Furthermore, according to the present invention, it is possible to realize a health monitoring method that makes it possible to visualize health monitoring results, encourage behavioral changes in subjects, and achieve healthy longevity.

[0027] According to the present invention, a health monitoring method can be realized that can predict the current health state or the location where a disease will worsen in the future. [Brief explanation of the drawings]

[0028] [Figure 1] FIG. 1 is a functional block diagram of a health monitoring system according to an embodiment of the present invention. [Figure 2] FIG. 2 is a functional block diagram of a health monitoring system according to an embodiment of the present invention. [Figure 3] FIG. 3 is a diagram showing an example of the spatiotemporal internal body environment information according to the embodiment of the present invention. [Figure 4] FIG. 4 is a diagram showing an example of a combined display of measurement results. [Figure 5] FIG. 5(a) is an external configuration diagram showing an example of the electroencephalogram measuring device of FIG. 1, and FIG. 5(b) is a diagram showing an example of a device main body that records and transmits sleep measurement data. [Figure 6] FIG. 6 shows an external view of a capsule endoscope as an in-vivo sensor and a configuration example of the tip portion. [Figure 7] Figure 7(a) is a diagram showing an example of a helical-type device as an in vivo sensor, Figure 7(b) is a diagram showing an example of a ring-type device as an in vivo sensor, and Figure 7(c) is a diagram showing an example of a stent-type device as an in vivo sensor. [Figure 8] FIG. 8 is a system configuration diagram of a cloud server connected to a mobile communication terminal via a network. [Figure 9]FIG. 9 is a diagram showing an example of a mobile communication terminal to which the present invention is applied. [Figure 10] FIG. 10 is a diagram showing an example of a personal computer to which the present invention is applied. [Figure 11] FIG. 11 is a diagram showing an example of a body display area in an embodiment of the present invention. [Figure 12] FIG. 12 is a diagram showing an example of an in vivo cybernetic avatar display area of ​​the present invention. [Figure 13] FIG. 13 is a diagram showing an example of the three major disease avatar display. [Figure 14] FIG. 14 is a diagram showing an example of the sleep state display area. [Figure 15] FIG. 15 is a diagram showing criteria to be displayed in the physical activity display area. [Figure 16] FIG. 16 is a flowchart illustrating a health monitoring method according to an embodiment of the present invention. [Figure 17] FIG. 17 is a flowchart illustrating a method for health monitoring in a biometric device. [Figure 18] FIG. 18 is a flowchart illustrating a method for health monitoring in a body-worn device. [Figure 19] FIG. 19 is a diagram showing examples of other lifestyle-related diseases. [Figure 20] FIG. 20 is a diagram showing an example of a physical activity display area in an embodiment of the present invention. [Figure 21] FIG. 21 is a diagram showing an example of personal health checkup data (part 1) utilized in an embodiment of the present invention. [Figure 22] FIG. 22 is a diagram showing an example of personal health checkup data (part 2) utilized in an embodiment of the present invention. [Figure 23] FIG. 23 is a diagram showing the configuration of an online collaboration system for sharing health monitoring results of subjects. [Figure 24] FIG. 24 is a diagram showing an example of measurement data of the body-wearable device. [Figure 25]FIG. 25 is a diagram for explaining the process of combining measurement data from various measurement devices. [Figure 26] FIG. 26 is a functional block diagram of a health management application according to an embodiment of the present invention. [Figure 27] FIG. 27 is a flowchart illustrating a health monitoring method according to an embodiment of the present invention. [Figure 28] FIG. 28 is a flowchart illustrating a health monitoring method according to another embodiment of the present invention. [Figure 29] FIG. 29 is a schematic block diagram of an integrated data analysis service system to which the present invention is applied. [Figure 30] FIG. 30 is a schematic block diagram of a biometric device verification service system to which the present invention is applied. DETAILED DESCRIPTION OF THE INVENTION

[0029] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, exemplary embodiments of the present invention will be described with reference to the accompanying drawings.

[0030] First, health monitoring systems 100a and 100b according to embodiments of the present invention will be described with reference to FIGS. 1, 2, 3 and 4. FIG.

[0031] Fig. 1 is a functional block diagram of a health monitoring system according to an embodiment of the present invention, Fig. 2 is a functional block diagram of a health monitoring system according to an embodiment of the present invention, Fig. 3 is a diagram showing an example of spatiotemporal internal body environment information according to an embodiment of the present invention, Fig. 4 is a diagram showing an example of a composite display of measurement results.

[0032] As shown in FIG. 1, the health monitoring system 100a of this embodiment includes a biometric device 1, an in-vivo CA (cybernetic avatar) measurement unit 2, a measurement result synthesis unit 3, and a visualization unit 4.

[0033] As shown in Fig. 2, the health monitoring system 100b of this embodiment includes a biometric device 1, an in-vivo CA (cybernetic avatar) measurement unit 2, a measurement result synthesis unit 3, a visualization unit 4, and an electroencephalogram (EEG) measurement device 5. The health monitoring system 100b shown in Fig. 2 is the same as that shown in Fig. 1 except for the electroencephalogram (EEG) measurement device 5.

[0034] The biometric device 1 is a device that non-invasively measures the biometric information of a subject in daily life. Examples of the biometric device 1 include a blood pressure monitor, a thermometer, and a weight scale. While the example in FIG. 1 shows them as an integrated device, they may be configured as separate devices and connected to the health monitoring system 100a via wireless communication. The biometric device may also include a blood glucose meter, which collects blood from a fingertip or the like and measures it, so it also includes a non-invasive measuring device. The biometric device 1 may perform measurements every day and store the measurement data, or may perform measurements every other day or every week.

[0035] The in-vivo CA measurement unit 2 is remotely controlled from the outside to grasp the internal conditions of the body, such as the internal organs and digestive system, and measures spatiotemporal internal environment information using an in-vivo cybernetic avatar (CA) that can interact with the body.

[0036] The measurement result synthesizing unit 3 synthesizes the measurement result of the biometric device 1 and the measurement result of the in-vivo CA measuring unit 2.

[0037] The visualization unit 4 visualizes the biometric data combined by the measurement result combination unit 3 and the spatiotemporal internal body environment information measured by the in vivo CA measurement unit 2, and displays them on a mobile communication terminal or the like. The visualization unit 4 may be configured to combine and display the measurement results from the biometric device 1 and the spatiotemporal internal body environment information measured by the in vivo CA measurement unit 2. An example of the spatiotemporal internal body environment information is shown in Figure 3. The spatiotemporal internal body environment information may include in vivo pH measurement information, in vivo imaging information, core body temperature information, sleep state information, blood glucose level information, etc. Other biometric information may also be included. Figure 4 shows an example of the visualization display by the visualization unit 4. Figure 4 shows an example of a display screen, for example, on a mobile communication terminal, and is composed of a body display area, an in vivo cybernetic avatar display area, a sleep state display area, and a physical activity display area. The body display area is an area where daily biometric data is displayed. The in vivo cybernetic avatar display area is an area where in vivo spatiotemporal internal body environment information is displayed. The sleep state display area is an area where the sleep state throughout the night on a specific day is displayed. This embodiment is characterized by its visualization display format. While various measurement results may be displayed as is, visualizing the subject's health status and pre-disease state using avatars provides an opportunity for the subject to change their behavior. In particular, lifestyle-related diseases are caused by daily lifestyle habits (diet, exercise, smoking, stress, etc.), and therefore, unless the subject improves their lifestyle, they will not be able to achieve a long and healthy life. Therefore, by displaying the subject's health status and pre-disease state using avatars, visualization that appeals to the subject's visual sense is provided. Naturally, numerical information and trend displays similar to conventional methods may also be used. Recent research has revealed that sleep plays an important role in preventing lifestyle-related diseases. Therefore, to maintain sleep quality, a well-known electroencephalogram (EEG) measuring device 5 that can be easily measured at home is used. The electroencephalogram measuring device 5 is described below.

[0038] The EEG measurement device 5 measures the sleep state of a subject at home or elsewhere. An example of the EEG measurement device 5 is shown in Figures 5(a) and 5(b). The EEG measurement device 5 consists of an EEG measurement unit worn on the subject's head and a device main unit 20 that records and stores the measurement results. The EEG measurement unit includes a horizontally elongated section 6 equipped with a left center forehead electrode 6a, a reference electrode 6b, and a right center forehead electrode 6c. The vertically elongated section 8 (flat cable) extends vertically from the horizontally elongated section 6. A flat cable 9 branches from the vertically elongated section 8 (flat cable) to the left and right arms, each equipped with a left ear electrode 7a and a right ear electrode 7b at its tip. The flat cable 8 has a connector 10 at its tip for connecting to the device main unit 20. Its simple and unique shape allows it to be easily worn by a single subject in a short time and is available at low cost. Its design also takes into consideration comfort and does not disturb sleep. This allows the subject to use the device easily at home, enabling sleep assessment under normal circumstances. Furthermore, the forehead electrodes (6a, 6b, 6c) are integrally formed on the sheet-like horizontal portion 6, and their relative positions are fixed. Therefore, simply attaching the sheet-like horizontal portion 6 to the forehead allows the electrodes to be positioned in the same relative positions each time. By attaching the forehead electrodes (6a, 6b, 6c) to the subject's forehead, eye movements can be detected in addition to brain waves. Furthermore, by attaching the left ear electrode 7a and the right ear electrode 7b behind the subject's left and right ears, neck electromyography can be detected in addition to brain waves. Therefore, eye movements and neck electromyography signals can be used in addition to brain waves, improving the accuracy of sleep assessment.

[0039] As shown in Fig. 6(b), the device main body 20 has a control unit 21, a connector unit 22, a storage unit 23, an operation unit 24, a display unit 25, and a battery 26. A known device (Patent Document 2) can be used as the electroencephalogram measuring device shown in Figs. 6(a) and 6(b). Details are as in Patent Document 2, so a description thereof will be omitted.

[0040] Although wearable devices can measure sleep, their accuracy is low, so the results can only be used as a reference. While going to a hospital for an overnight polysomnogram test provides accurate sleep measurements, the method requires the subject to stay overnight at the hospital, placing a significant burden on the subject. Using the simple EEG measurement device described above to perform measurements at home is relatively less invasive and highly accurate, allowing for accurate measurement of sleep quality.

[0041] 6(a) and 6(b) are diagrams showing the configuration of a capsule endoscope 30 as an in-vivo CA sensor used in the in-vivo CA measurement unit 2. FIG.

[0042] As shown in Figures 6(a) and 6(b), the capsule endoscope 30 includes a distal end cover 31, LED illuminators 32a, 32b, 32c, and 32d, and an objective lens 34. Although not shown, the capsule endoscope 30 also includes a CMOS sensor element, electrical circuitry, and an internal battery that capture images by receiving light from the LED illuminators 32a, 32b, 32c, and 32d through the objective lens 34. The capsule endoscope 30 is housed in a case that does not consume power so that it can be used only when the capsule endoscope is in use. When the capsule endoscope 30 is removed from the case, the power switch is turned on. Measurements are performed using a wireless communication sensor for capturing image information from the capsule endoscope 30 and a device for recording signals from the wireless communication sensor. Measurements typically take approximately eight hours. Figures 6(a) and 6(b) are similar to known devices, so a detailed description is omitted.

[0043] Figure 7(a) is a diagram showing an example of a helical-type device as an in vivo sensor, Figure 7(b) is a diagram showing an example of a ring-type device as an in vivo sensor, and Figure 7(c) is a diagram showing an example of a stent-type device as an in vivo sensor.

[0044] As shown in FIG. 7(a), a helical device 40 has a sensor 41 that measures information such as pH in the living body, core body temperature, and blood sugar level.

[0045] As shown in FIG. 7(b), the ring-shaped device 42 has biosensors 42, 43, and 44 that measure information such as pH in the living body, core body temperature, and blood sugar level.

[0046] As shown in FIG. 7(c), the stent-type device 42 has biosensors 47, 48, and 49 that measure information such as pH in the living body, core body temperature, and blood sugar level.

[0047] In the above embodiment, an in vivo sensor has been described, but the present invention can be implemented by applying the intracellular sensing technology disclosed in Japanese Patent Nos. 5551355 and 5526345.

[0048] FIG. 8 is a system configuration diagram of a cloud server 63 connected to a mobile communication terminal 50 and a network 62.

[0049] As shown in FIG. 8, the mobile communication terminal 50 has a CPU 51, a ROM 52, a RAM 53, a display 54, a biometric authentication memory unit 55, an ID / password authentication unit 56, a touch input unit 57, a camera 58, a speaker 59, a microphone 60, and a communication interface 61.

[0050] Mobile communication terminal 50 is connected to cloud server 63 via network 62, such as an internetwork or a broadband network. In such a communication system, a health monitoring system may be implemented using software, and health monitoring may be performed by installing a health monitoring app 72 on a mobile communication terminal 70 as shown in FIG. 9 and executing the health monitoring app 72. The example in FIG. 9 shows a state in which an icon for health monitoring app 72 is displayed on screen 71. FIG. 10 shows an example of a personal computer. The personal computer includes a camera 73, a display 74, a screen 75 that displays the visualized information in the display format shown in FIG. 4, and a fingerprint authentication unit 76 displayed on the keyboard. Biometric authentication may be performed using face authentication using camera 73.

[0051] The visualization display of the present invention will be described below. FIG. 11 shows an example of the body display area. In the example of FIG. 11, blood pressure 120 / 80 mmHg, pulse rate 60 bpm, body temperature 36.8°C, and weight 54 kg are displayed. A minimum amount of personal information is registered in the health monitoring app when a user registers. For example, this information may include height, age, gender, and BMI. Other information may also be registered. An individual's health checkup results (see FIGS. 21 and 22) may be digitized and stored on a mobile communication device or the like, and a comparison display may be performed. For example, if the weight has increased significantly compared with the weight in the health checkup results, the user may not be healthy, but may be predicted to be in pre-illness (1) or pre-illness (2) (here, pre-illness (2) indicates a worse health condition than pre-illness (1)), and an estimate may be displayed.

[0052] Figure 12 shows an example of a display pattern displayed in the in-vivo cybernetic avatar display area. The in-vivo CA measurement unit 2 associates the type of in-vivo CA measured with the associated body part to create an avatar, showing the healthy and pre-illness states. Since the subjects are not experts, simply displaying the measurement information numerically encourages behavioral change in the subjects. When an in-vivo sensor is placed in the brain, the displayed image of the brain avatar changes depending on the healthy, pre-illness (1) and pre-illness (2) states. For a healthy state, an image of a smiling brain is displayed; for pre-illness (1), an image of the brain appearing to be in slight distress is displayed; and for pre-illness (2), an image of the brain appearing to be in distress is displayed.

[0053] Similarly, if an in-vivo sensor is placed in the heart, the avatar image of the heart is changed and displayed as healthy, pre-disease (1), or pre-disease (2). If an in-vivo sensor is placed in the stomach, the avatar image of the stomach is changed and displayed as healthy, pre-disease (1), or pre-disease (2). Furthermore, if an in-vivo sensor is placed in the intestine, the avatar image of the intestine is changed and displayed as healthy, pre-disease (1), or pre-disease (2). The types of in-vivo CA may be configured to create and display avatar images for each organ.

[0054] In this way, displaying an in vivo cybernetic avatar appeals intuitively to the subject's visual sense, and is expected to promote behavioral change.

[0055] Figure 13 shows example display patterns for avatars of the three major diseases. Examples of the diseases shown are cancer, cerebral infarction, and heart disease. For cancer, the healthy state shows an avatar image attacking cancer cells, the pre-disease (1) state shows an avatar image in which the cancer cells are slightly gaining momentum, and the pre-disease (2) state shows an avatar image in which the cancer cells are also gaining momentum. For cerebral infarction, the healthy state shows an avatar image in which red blood cells are swimming freely through blood vessels, the pre-disease (1) state shows an avatar image in which the blood vessels have slightly developed arteriosclerosis, making it difficult for the red blood cells to swim, and the pre-disease (2) state shows an avatar image in which the blood vessels have developed arteriosclerosis, making it difficult for the red blood cells to swim. For heart disease, the healthy state shows an avatar image in which the heart's blood vessels are slightly clogged, the pre-disease (1) state shows an avatar image in which the heart's blood vessels are slightly clogged, causing some pain to the heart, and the pre-disease (2) state shows an avatar image in which the heart's blood vessels are clogged and enlarged, causing pain to the heart.

[0056] FIG. 14 shows an example of a display pattern for the sleep status display area. It illustrates the relationship between sleep status and health, pre-illness (1), and pre-illness (2). Sleep status data measured by the EEG measurement device shown in FIG. 6 is uploaded to the cloud server 63, and a report of the EEG analysis service results is received. Sleep status is then categorized into three stages, for example, "light sleep," "moderate sleep," and "deep sleep." Light sleep indicates a high risk of illness, so pre-illness (2) is displayed. Moderate sleep indicates disrupted lifestyle habits, so pre-illness (1) is displayed. Deep sleep indicates that sleep quality is maintained, so "healthy" is displayed. Since sleep quality assessment can be performed at home, similar to an overnight polysomnography test, it is effective in preventing and improving lifestyle-related diseases. FIG. 19 shows an example of a lifestyle-related disease. While FIG. 14 describes the display of avatar images for the three major diseases, similar avatar images may also be displayed for hypertension, dementia, diabetes, hyperlipidemia, hyperuricemia, pulmonary hypertension, and other conditions.

[0057] Figure 15 shows a list of recommendations in the 2023 Physical Activity and Exercise Guide for Health Promotion. The list of recommendations in Figure 15 is used as the standard for the amount of physical activity displayed in the physical activity display area. In Figure 15, the subjects are divided into elderly people, adults, and children, and physical activity (including daily activities and exercise) and sedentary behavior. For elderly people, daily activities are walking or physical activity of an equivalent intensity (3 METs or more) for 40 minutes or more per day (6,000 steps or more per day), and exercise is multi-component exercise such as aerobic exercise, strength training, balance exercise, and flexibility exercise for 3 days or more per week, and strength training for 2-3 days per week.

[0058] For example, if the subject is elderly and 67 years old, the recommended daily step count is 6,000 steps or more. Therefore, the physical activity display area shown in Figure 20 shows that the number of steps for daily activities was 7,500, exceeding the recommended value and displaying "OK." Exercise options are displayed, such as one aerobic exercise (OK), three strength training sessions (OK), and two balance exercise sessions (OK). The exercise display is calculated by converting the exercise into METs and exercise. METs are a unit that expresses the intensity of physical activity as a multiple of resting activity. For example, sitting and resting corresponds to 1 MET, and normal walking corresponds to 3 METs. Exercise is a unit that expresses the amount of physical activity. It is calculated by multiplying the intensity of physical activity (METs) by the duration of physical activity (hours). The more intense the physical activity, the shorter the time required for one exercise. Energy expenditure (kcal) is calculated by 1.05 x exercise (METs x hours) x body weight (kg).

[0059] FIG. 16 is a flowchart illustrating a health monitoring method according to an embodiment of the present invention. First, the in vivo CA measurement step is executed (step 101). By remotely controlling the device from the outside, the internal conditions of the body, such as the internal organs and digestive system, are grasped, and spatiotemporal internal environment information is measured using an in-vivo cybernetic avatar that can interact with the body (step 102). Next, a measurement result recording step is executed (step 103). Next, the recorded measurement results are transmitted to the mobile communication terminal (step 104). A measurement result synthesis step is executed to synthesize and display the measurement results from the biometric device and the measurement results of the spatiotemporal internal body environment information from the in-vivo cybernetic avatar (step 105). The measurement results combined in the measurement result combining step 105 are visualized as shown in FIG. 4 in a visualization step (step 106).

[0060] FIG. 17 is a flowchart illustrating a method for health monitoring in a biometric device. First, a biometric measurement step is performed (step 201). Next, a measurement result recording step is executed (step 202). Next, the recorded measurement results are transmitted to the mobile communication terminal (step 203). Next, the recorded measurement results are transmitted to the mobile communication terminal (step 204).

[0061] FIG. 18 is a flowchart illustrating a health monitoring method using a body-worn device, and FIG. 24 shows an example of a body-worn device capable of 24-hour continuous monitoring. Devices that can be used 24 hours a day include ring-type (finger ring type), watch-type, and tracker-type devices, and data is not compatible between manufacturers. For example, a ring-type device manufactured by Company A has a health app A dedicated to Company A and can utilize physical activity data A. Similarly, a watch-type device manufactured by Company B has a health app B dedicated to Company B and can utilize physical activity data B. A tracker device manufactured by Company C has a health app B dedicated to Company C and can utilize physical activity data C. The health monitoring method of this embodiment extracts a portion of physical activity data A, B, or C obtained by measurement using these body-worn devices, combines it with in-vivo CA measurement data, and visualizes it. FIG. 25 shows an example of a combined display of measurement results. First, a physical activity measurement step is executed (step 301). Next, a measurement result recording step is executed (step 302). Next, the recorded measurement results are transmitted to the mobile communication terminal (step 303). Next, the recorded measurement results are transmitted to the mobile communication terminal (step 304).

[0062] FIG. 23 is a diagram showing the configuration of an online collaboration system for sharing health monitoring results of subjects.

[0063] As shown in FIG. 23, the online collaboration system 80 includes an online collaboration server 81, subject terminals 82a and 82b, a communication network 83, primary care doctor terminals 84a and 84b, and an administrator terminal 85.

[0064] Subject terminals 82a, 82b are connected to a family doctor terminal 84a or a hospital terminal 84b via a communication network 83, and measurement data of the subject may be shared. By sharing data, the subject's measurement data can be obtained, enabling family doctors or doctors at hospitals to provide appropriate medical care, which is expected to lead to a reduction in medical expenses.

[0065] FIG. 26 is a functional block diagram of a health management application according to an embodiment of the present invention.

[0066] As shown in FIG. 26, the health management application 400 in this embodiment has a blood pressure recording unit 401, a weight recording unit 402, a body temperature recording unit 403, a body-worn device recording unit 404, a sleep recording unit 405, a step count recording unit 406, an exercise recording unit 407, a pre-illness visualization unit 408, an external linkage unit 409, and a health management dashboard unit 401.

[0067] Health management application 400 is installed in advance on a mobile communication device such as a smartphone, a tablet PC, a portable laptop, a desktop computer, or the like, and then used. For example, it may be installed in advance on a mobile communication device, and health management application 400 may be started, and measurement data from a blood pressure monitor, a weight scale, and a thermometer may be automatically recorded using a short-range wireless communication function. Alternatively, the measurement data may be manually entered by the subject. When using health management application 400, height data, age, and gender (male or female; gender entry may be omitted if not desired) are required. The subject's name (nickname is acceptable) should also be entered, as this is the minimum information required for effective health management.

[0068] The blood pressure recording unit 401 records the subject's systolic blood pressure data and diastolic blood pressure data, which are measurement data from the sphygmomanometer. If the sphygmomanometer can measure the pulse rate, the pulse data may be recorded at the same time. It is desirable to measure multiple times, such as in the morning, afternoon, and evening. If blood pressure cannot be measured in the afternoon, the afternoon measurement may be omitted. At the very least, the measurement should be recorded once when waking up in the morning.

[0069] The weight recording unit 402 receives and records weight data from a weight scale via wireless, wired, or manual input. The weight scale may be a body composition monitor. Weight is measured and recorded at least once in the morning when the user wakes up.

[0070] If the thermometer has a communication function, the temperature recording unit 403 may automatically record the temperature data from the thermometer. If the thermometer does not have a communication function, the subject must manually input the temperature data.

[0071] The body-worn device recording unit 404 records various measurement data from the body-worn device, such as sleep measurement data, heart sound data, and pulse data.

[0072] The sleep recording unit 405 records sleep-related data from the electroencephalogram measuring device described above.

[0073] The step count recording unit 406 is linked to a pedometer and allows automatic input or manual input by the subject. Automatic input is more preferable in order to reduce the burden of recording on the subject.

[0074] The exercise recording unit 407 manually inputs whether or not muscle training or resistance exercise has been performed.

[0075] The pre-disease visualization unit 408 displays a pre-disease avatar. Various other data may also be visualized.

[0076] The external linking unit 409 has a function to send the above-mentioned measurement data to a family doctor's terminal or a hospital terminal for data sharing, or a data conversion function to convert and transfer data in order to link with other health management apps.

[0077] The health management dashboard unit 401 controls the overall health condition data, such as displaying trends of health management data, displaying various charts, etc. It also outputs reports of health management data.

[0078] The health management application 400 described above records the subject's subjective symptoms. For example, sleepiness upon waking up is evaluated on a three-point scale and recorded. This is to obtain data from the subject that cannot be obtained from measurement data and to use it to determine whether the subject is healthy or not. For example, sleepiness may be evaluated as "refreshed," "normal," or "sleepy." Other subjective symptom parameters may also be set.

[0079] In this specification, pre-illness refers to a state in which there are no subjective symptoms but tests show abnormalities, and a state in which there are subjective symptoms but tests show no abnormalities. Health refers to a state in which there are no subjective symptoms but tests show no abnormalities. Illness refers to a crossover state in which there are subjective symptoms but tests show abnormalities.

[0080] FIG. 27 is a flowchart illustrating a health monitoring method according to an embodiment of the present invention.

[0081] The health monitoring method of this embodiment comprises a biometric information acquisition step 501 of acquiring biometric information of the subject using at least a sphygmomanometer, a thermometer, and a weight scale; a behavioral information acquisition step 502 of acquiring behavioral information, which is physical activity information, from a body-wearable device; a health checkup result information acquisition step 503 of acquiring health checkup result information including at least brain images and in-vivo endoscopic images; an analysis step 504 of analyzing the acquired biometric information, behavioral information, and health checkup result information of the subject; and a health condition estimation step 505 of estimating the subject's future health condition using the results of the above analysis and the subject's biometric information, behavioral information, and health checkup result information.

[0082] At least blood pressure data, body temperature data, and weight data are acquired in the biological information acquisition step 501. Blood glucose data may also be acquired as other biological information.

[0083] In behavior information acquisition step 502, physical activity information (step count data, muscle training data, etc.) is acquired.

[0084] In the health check result information acquisition step 503, in-vivo information of the subject's brain image and in-vivo endoscopic image is acquired and used in the next analysis step.

[0085] In the analysis step 504, the acquired biological information, behavioral information, and health check result information of the subject are analyzed. Genetic testing and other information may also be added to the analysis data. By creating a health condition model using the health check big data and utilizing AI analysis technology, prediction accuracy can be improved.

[0086] In the health condition estimation step 505, the results of the above analysis and the subject's biological information, behavioral information, and health check result information are used to estimate the subject's future health condition, predict whether the subject is healthy or not, and predict areas that will deteriorate in the future.

[0087] FIG. 28 is a flowchart illustrating a health monitoring method according to another embodiment of the present invention.

[0088] The health monitoring method of another embodiment includes an electroencephalogram measuring step 601, a biological information acquiring step 602, a behavioral information acquiring step 603, a health checkup result information acquiring step 604, an analysis step 605, and a health condition estimating step 606. Here, the biological information acquiring step 602, the behavioral information acquiring step 603, the health checkup result information acquiring step 604, the analysis step 605, and the health condition estimating step 606 are similar to the biological information acquiring step 501, the behavioral information acquiring step 502, the health checkup result information acquiring step 503, the analysis step 504, and the health condition estimating step 505, respectively, so their explanations will be omitted and only the electroencephalogram measuring step 601 will be described.

[0089] In the EEG measurement step 601, EEG measurements of the subject's sleep state are performed at home. In the EEG measurement step 601, a sleep test equivalent to a polysomnogram test is performed. The EEG measurement device used is the one shown in FIG. 5. Here, by performing sleep measurement, the influence of the subject's sleep on the progression of lifestyle-related diseases can be taken into account, thereby improving the accuracy of prediction of the subject's health condition.

[0090] FIG. 29 is a schematic block diagram of an integrated data analysis service system to which the present invention is applied.

[0091] As shown in FIG. 29, the integrated data analysis service system 700 integrates a sleep analysis service 701, a brain image analysis service 702, a health checkup analysis service 703, and a pre-disease prediction service 704, providing them as a solution package to support diagnosis at hospitals and clinics. Current analysis services are provided as individual services, which poses a problem of inability to provide comprehensive assessments. This service is provided as one means to solve this problem. For example, the sleep analysis service "InSomnograf" provided by S'UIMIN Co., Ltd. can be used as the sleep analysis service 701. For example, the device disclosed in JP 2022-40919 A ​​(Patent Document 2) can be used as a brain measurement device. For example, the brain analysis service disclosed in WO 2023-190880 can be used as the brain image analysis service 702. This brain analysis service is effective because it analyzes the entire brain image and is desirable because it allows brain image analysis that is not affected by the resolution of the brain scan image.

[0092] FIG. 30 is a schematic block diagram of a biometric device verification service system to which the present invention is applied.

[0093] As shown in FIG. 30, this biometric device verification service system 800 is composed of a biometric device verification unit 801, biometric device measurement big data 802, and an AI biometric device proposal unit 803. Since biometric devices vary from manufacturer to manufacturer and have different measurement accuracy, the resulting prediction results may differ. This system verifies the biometric device using measurement big data and uses AI to propose a biometric device with guaranteed prediction accuracy. This is expected to have the effect of maintaining a consistent prediction accuracy in the AI ​​analysis results.

[0094] Thus, according to the embodiments of the present invention, it is possible to realize a health monitoring system and a health monitoring method that enable constant daily health monitoring with relatively little burden on the subject, that helps prevent and improve lifestyle-related diseases while maintaining sleep quality, and that leads to a long and healthy life.

[0095] Furthermore, according to the embodiments of the present invention, it is possible to realize a health monitoring system and a health monitoring method that are capable of maintaining the quality of sleep and monitoring health and pre-illness with high accuracy.

[0096] Furthermore, according to the embodiment of the present invention, it is possible to realize a health monitoring method that can visualize health monitoring results, encourage behavioral changes in subjects, and achieve healthy longevity.The health monitoring method of this embodiment can predict the current health status and body parts that will deteriorate in the future. [Explanation of symbols]

[0097] 1. Biometric devices 2. In vivo CA measurement section 3 Measurement result synthesis section 4 Visualization section 5. Brain wave measurement device 6. EEG measurement section 6a, 6b, 6c: Electrode 7a, 7b electrode 8, 9 Flat cable 10 Connectors 20 Device body 21 Control section 22 Connector part 23 Memory section 24 Control section 25 Display section 26 Battery 30 Capsule Endoscopy 31 Tip cover 32a, 32b, 32c, 32d LED lighting 34 Objective Lens 40 Helical Device 41 Biometric Sensors 42 Ring Devices 43, 44, 45 Biometric sensors 46 Stent-type devices 47, 48, 49 Biometric sensors 50 Mobile communication devices 51 CPU 52 ROM 53 RAM 54 Display 55 Biometric authentication memory unit 56 ID / Password Authentication Section 57 Touch input section 58 Camera 59 Speaker 60 Mike 61 Communication Interface 62 Network 63 Cloud Server 70 Mobile communication devices 71 Mobile screen 72 Health Monitoring Apps 73 Camera 74 Display 75 Computer screen 76 Biometric Authentication Department 80 Online Collaboration System 81 Online Collaboration Server 82a, 82b Subject terminal 83 Network 84a Family doctor terminal 84b Hospital terminal 85 Administrator terminal 100a, 100b Health Monitoring System 101 In vivo CA measurement steps 102 Spatiotemporal Internal Body Environment Measurement Steps 103 Measurement result recording step 104 Measurement result transmission step 105 Measurement result synthesis step 106 Visualization Steps 201 Biometric Steps 202 Measurement result recording step 203 Measurement result transmission step 204 Recording step to mobile communication terminal 301 Physical Activity Measurement Steps 302 Measurement result recording step 303 Measurement result transmission step 304 Recording step to mobile communication terminal 400 Health Management Apps 401 Blood pressure recording unit 402 Weight Recording Section 403 Body Temperature Recording Unit 404 Body-worn device recording unit 405 Sleep Recording Section 406 Step count recorder 407 Sports Recording Department 408 Mibyo Visualization Department 409 External Collaboration Department 410 Health Management Dashboard Department 501, 602 Biometric information acquisition steps 502, 603 Behavioral information acquisition steps 503, 604 Steps for obtaining health checkup result information 504, 605 Analysis steps 505, 606 Health status estimation step 601 EEG measurement steps 700 Integrated Data Analysis Service System 701 Sleep analysis service 702 Brain image analysis service 703 Health checkup analysis service 704 Future Prediction Service 800 Biometric Device Verification Department 801 Biometric Device Measurement Big Data 803 AI Biometric Device Proposal Department

Claims

1. a biometric information acquiring step of acquiring biometric information of the subject using at least a blood pressure monitor, a thermometer, and a weight scale; a behavioral information acquisition step of acquiring behavioral information, which is physical activity information, from the body-wearable device; a health checkup result information acquisition step of acquiring health checkup result information including at least a brain image and an in-vivo endoscope image; an analysis step of analyzing the acquired biological information, behavioral information, and health examination result information of the subject; an estimation step of estimating the subject's future health state using the results of the analysis and the subject's biological information, behavioral information, and health examination result information; A health monitoring method comprising:

2. 2. The health monitoring method according to claim 1, further comprising an electroencephalogram (EEG) measuring step of measuring the subject's sleep state at home, wherein the electroencephalogram measuring step performs a sleep test equivalent to a polysomnogram test.

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