WO2023223725A1

WO2023223725A1 - Health management system and health management method

Info

Publication number: WO2023223725A1
Application number: PCT/JP2023/014962
Authority: WO
Inventors: 秋憲松本
Original assignee: パナソニックＩｐマネジメント株式会社
Priority date: 2022-05-17
Filing date: 2023-04-13
Publication date: 2023-11-23

Abstract

A health management system (100) comprises: an acquisition unit (31) that acquires an electrocardiogram signal of a user which was measured using an electrode of a toilet seat device (10); an analysis unit (34) that calculates an RR interval with respect to a section of part of the acquired electrocardiogram signal, said analysis unit (34) changing the section and calculating a plurality of RR intervals; a 3H risk estimation unit (35) that, on the basis of the plurality of calculated RR intervals, calculates a plurality of 3H risk estimation values, each of which is an estimation value of the risk that the user has at least one of hypertension, hyperglycemia, and hyperlipidemia; and a blood vessel adjustment degree estimation unit (37) that, on the basis of the plurality of calculated 3H risk estimation values, estimates a blood vessel adjustment degree which indicates a heart rate adjustment function by blood vessel contraction, and that outputs information for a presentation relating to the estimated blood vessel adjustment degree.

Description

Health management system and health management method

The present invention relates to a health management system.

Various technologies related to health management have been proposed. Patent Document 1 discloses a system that collects biological information according to a user's daily activities and optimally utilizes the data for health checkups, health management, and the like.

Japanese Patent Application Publication No. 2000-126138

The present invention provides a health management system and the like that can present information regarding health risks.

A health management system according to one aspect of the present invention includes an acquisition unit that acquires an electrocardiogram signal of a user measured using the electrode by a toilet seat device including an electrode, and a part of the acquired electrocardiogram signal. an analysis unit that calculates an RR interval for a plurality of times, the analysis unit that calculates a plurality of RR intervals by changing the interval; and an analysis unit that calculates a plurality of RR intervals by changing the interval; an estimating unit that calculates a 3H risk estimate that is an estimated value of the risk corresponding to at least one of hyperglycemia and hyperlipidemia for a plurality of times; Based on the 3H risk estimation value, a degree of vascular regulation indicating a function of regulating heart rate due to contraction of blood vessels is estimated, and information for presenting the estimated degree of vascular regulation is output.

A health management method according to one aspect of the present invention is a health management method executed by a computer, and includes a first acquisition step of acquiring an electrocardiogram signal of a user measured using the electrode by a toilet seat device including the electrode. , an analysis step of calculating an RR interval for a part of the section of the acquired electrocardiogram signal, the analysis step of changing the section and calculating the RR interval for a plurality of times; A first estimation step of calculating a plurality of 3H risk estimates, which are estimated values of the risk that the user falls under at least one of hypertension, hyperglycemia, and hyperlipidemia, based on the RR interval of and a second estimation step of estimating a degree of vascular adjustment indicating a function of adjusting heart rate due to contraction of blood vessels based on the calculated 3H risk estimate values for a plurality of times, and presenting the estimated degree of vascular adjustment. and a first output step of outputting information for.

A program according to one aspect of the present invention is a program for causing a computer to execute the health management method.

The health management system etc. of the present invention can present information regarding health risks.

FIG. 1 is a diagram showing the configuration of a health management system according to the first embodiment. FIG. 2 is an external view of the toilet seat device according to the first embodiment. FIG. 3 is an external view of a toilet seat device in which electrodes are provided on a handrail. FIG. 4 is a flowchart of an example of the operation of the health management system according to the first embodiment. FIG. 5 is a diagram showing a determination result of 3H risk by the health management system according to the first embodiment. FIG. 6 is a diagram showing an example of a display screen of the estimation result of the degree of blood vessel adjustment. FIG. 7 is a diagram showing the configuration of a health management system according to the second embodiment. FIG. 8 is a flowchart of an example of the operation of the health management system according to the second embodiment. FIG. 9 is a diagram showing an estimation formula for blood pressure contribution. FIG. 10 is a diagram showing an example of estimating blood pressure contribution and carbohydrate excess. FIG. 11 is a diagram showing an example of a display screen of the estimation result of the degree of carbohydrate excess. FIG. 12 is a diagram showing the configuration of a health management system according to the third embodiment. FIG. 13 is a flowchart of an example of the operation of the health management system according to the third embodiment. FIG. 14 is a diagram showing an example of a display screen of the estimation result of the degree of metabolic abnormality.

Hereinafter, embodiments will be specifically described with reference to the drawings. Note that the embodiments described below are all inclusive or specific examples. The numerical values, shapes, materials, components, arrangement positions and connection forms of the components, steps, order of steps, etc. shown in the following embodiments are merely examples, and do not limit the present invention. Further, among the constituent elements in the following embodiments, constituent elements that are not described in the independent claims will be described as arbitrary constituent elements.

Note that each figure is a schematic diagram and is not necessarily strictly illustrated. Furthermore, in each figure, substantially the same configurations are denoted by the same reference numerals, and overlapping explanations may be omitted or simplified.

(Embodiment 1)
[composition]
The configuration of the health management system according to the first embodiment will be described below. FIG. 1 is a diagram showing the configuration of a health management system according to the first embodiment. The health management system 100 simply estimates and estimates the 3H risk of the user based on an electrocardiogram signal (ECG Signal) measured while the user (person) is sitting on the toilet seat device 10. This is a system that can display the results on the information terminal 40. The 3H risk is a comprehensive definition of the user's risk of at least one of hypertension, hyperglycemia, and hyperlipidemia (dyslipidemia). The 3H risk estimate is useful as an index (measure) of whether a user has a tendency to develop a lifestyle-related disease.

As shown in FIG. 1, the health management system 100 includes a toilet seat device 10, a server device 30, and an information terminal 40. First, the toilet seat device 10 will be explained with reference to FIG. 2 in addition to FIG. 1. FIG. 2 is an external view of the toilet seat device 10. The toilet seat device 10 measures an electrocardiogram signal of a user sitting on the toilet seat device 10. The user can unconsciously receive an electrocardiogram signal measurement simply by sitting on the toilet seat device 10. The electrocardiogram signal here refers to an electrical signal from the heart.

As shown in FIG. 2, the toilet seat device 10 includes, for example, a toilet seat 20, a first electrode 21, a second electrode 22, and a body ground electrode 23. The toilet seat device 10 may be a device that is attached to an existing toilet bowl by replacing the toilet seat attached to the existing toilet bowl, or it may be a device that is formed integrally with the toilet bowl.

The toilet seat 20 is the part of the toilet seat device 10 that the user sits on when he relieves himself. The toilet seat 20 is a member formed of a white resin material, and also functions as a holding member that holds other components. A first electrode 21, a second electrode 22, and a body earth electrode 23 are provided on the surface of the toilet seat 20.

The first electrode 21 is an electrode provided on the surface of the toilet seat 20, and functions as a measurement electrode for measuring electrocardiogram signals. Specifically, the first electrode 21 is formed of a metal material such as silver. The first electrode 21 is preferably a silver-silver chloride electrode. As shown in FIG. 2, the first electrode 21 is provided at a portion of the toilet seat 20 where the left thigh of a user sitting on the toilet seat 20 is located, and comes into contact with the left thigh.

The second electrode 22 is an electrode provided on the surface of the toilet seat 20, and functions as a reference electrode for measuring electrocardiogram signals. The second electrode 22 is specifically formed of a metal material such as silver. Second electrode 22 is preferably a silver-silver chloride electrode. As shown in FIG. 2, the second electrode 22 is provided at a portion of the toilet seat 20 where the right thigh of a user sitting on the toilet seat 20 is located, and comes into contact with the right thigh.

The body earth electrode 23 is an electrode for applying a body earth potential to the user sitting on the toilet seat 20. At least a portion of the body ground electrode 23 is provided on the surface of the toilet seat 20 and comes into contact with the right thigh of the user sitting on the toilet seat 20. Body ground electrode 23 is formed of a metal material such as silver, for example. Body earth electrode 23 is preferably a silver-silver chloride electrode.

In the example of FIG. 2, the first electrode 21, the second electrode 22, and the body earth electrode 23 are provided on the toilet seat 20, but if the toilet seat device 10 is provided with a handrail, the first electrode 21, the second electrode 22, and the body earth electrode 23 are provided on the toilet seat 20. The electrode 21, the second electrode 22, and the body earth electrode 23 may be provided on the handrail. FIG. 3 is an external view of the toilet seat device 10 in which the first electrode 21, the second electrode 22, and the body earth electrode 23 are provided on the handrail.

Next, the server device 30 in FIG. 1 will be explained. The server device 30 is a cloud server that estimates the 3H risk of the user based on the user's electrocardiogram signal measured by the toilet seat device 10.

The server device 30 can acquire (receive) the electrocardiogram signal measured by the toilet seat device 10 from the toilet seat device 10 by communicating with the toilet seat device 10 via a wide area communication network such as the Internet. In addition, the server device 30 acquires (receives) user attribute information input to the information terminal 40 by communicating with the information terminal 40 via a wide area communication network such as the Internet, and presents the information to the information terminal 40. can be output (sent). Note that the presentation information is information for presenting (visualizing) the 3H risk determination result and various estimation results described below to the user.

Specifically, the server device 30 includes an acquisition unit 31, a preprocessing unit 32, a storage unit 33, an analysis unit 34, a 3H risk estimation unit 35, a determination unit 36, and a vascular adjustment degree estimation unit 37. Each of the acquisition unit 31, preprocessing unit 32, analysis unit 34, 3H risk estimation unit 35, determination unit 36, and vascular adjustment degree estimation unit 37 is one or more processors (hardware) included in the server device 30. is a functional component realized by executing a computer program (software) stored in a memory such as the storage unit 33.

The acquisition unit 31 acquires the user's electrocardiogram signal measured by the toilet seat device 10 using the first electrode 21, the second electrode 22, and the body earth electrode 23. The acquisition unit 31 also acquires user attribute information. The attribute information includes, for example, information indicating the user's gender, user's height, and user's weight. Note that the attribute information includes the value of BMI (Body Mass Index: BMI = weight [kg] / (height [m])^2) instead of information indicating the user's height and weight. It's okay.

The preprocessing unit 32 performs preprocessing on the electrocardiogram signal acquired by the acquisition unit 31. The preprocessing includes a process of limiting the frequency band of the electrocardiogram signal, a process of excluding a period in which the amplitude of the electrocardiogram signal is greater than or equal to a predetermined value, and a process of dividing the electrocardiogram signal into predetermined time units and storing it in the storage unit 33. This includes processing, etc.

The electrocardiogram signal is stored in the storage unit 33 by the preprocessing unit 32 for each predetermined time unit. Specifically, the storage unit 33 is realized by a HDD (Hard Disk Drive) or a semiconductor memory.

The analysis unit 34 reads the electrocardiogram signal acquired by the acquisition unit 31 and preprocessed by the preprocessing unit 32 from the storage unit 33, and calculates RR intervals (RR Intervals) from the read electrocardiogram signal. do. The RR interval is the interval from one QRS wave to the next QRS wave in the waveform of an electrocardiogram signal. The analysis unit 34 calculates the RR interval for a partial section of the electrocardiogram signal, and when the 3H risk estimate is calculated for multiple times, changes the above-mentioned section and calculates the RR interval for the multiple times.

The 3H risk estimating unit 35 generates a 3H risk estimation which is an estimated value of the risk that the user has at least one of hypertension, hyperglycemia, and hyperlipidemia, based on the calculated RR intervals for the plurality of times. Calculate the value multiple times. More specifically, the 3H risk estimating unit 35 calculates the 3H risk estimate for multiple times based on the calculated RR intervals for multiple times and the attribute information acquired by the acquiring unit 31, but if the attribute information is It is not required that it be used.

The determining unit 36 determines whether the user has a 3H risk based on the multiple 3H risk estimates calculated by the 3H risk estimating unit 35. The determination unit 36 defines the maximum value of the multiple calculated 3H risk estimated values as a representative estimated value. The determining unit 36 determines that the user has a 3H risk when the representative estimated value is greater than the threshold, and determines that the user does not have a 3H risk when the representative estimated value is less than or equal to the threshold. Further, the determination unit 36 outputs information (hereinafter also referred to as presentation information) for presenting the determination result of the presence or absence of 3H risk.

The vascular adjustment degree estimation unit 37 estimates the user's vascular adjustment degree based on the multiple 3H risk estimates calculated by the 3H risk estimation unit 35. Specifically, the vascular adjustment degree estimating unit 37 estimates the user's vascular adjustment degree based on the difference between the maximum value and the minimum value of the 3H risk estimation values for multiple times. Further, the blood vessel adjustment degree estimating unit 37 outputs information (hereinafter also referred to as presentation information) for presenting the estimated blood vessel adjustment degree.

Next, the information terminal 40 will be explained. The information terminal 40 receives the presentation information from the server device 30 and displays the content of the presentation information on the display unit 41 based on the received presentation information. The information terminal 40 is, for example, a portable information terminal such as a smartphone or a tablet terminal, but may also be a stationary information terminal such as a personal computer. Furthermore, although the information terminal 40 is realized by installing a dedicated application program for the health management system 100 on a general-purpose device, it may be a dedicated device for the health management system 100. The display unit 41 included in the information terminal 40 is realized by, for example, a display panel such as a liquid crystal panel or an organic EL (Electro Luminescence) panel.

[Operation example]
Next, an example of the operation of the health management system 100 will be described. FIG. 4 is a flowchart of an example of the operation of the health management system 100.

First, the acquisition unit 31 of the server device 30 acquires the user's electrocardiogram signal and the user's attribute information (S11). The user's electrocardiogram signal is measured by the toilet seat device 10 using the first electrode 21, the second electrode 22, and the body ground electrode 23, and is transmitted from the toilet seat device 10 to the server device 30. The user's attribute information is, for example, input into the information terminal 40 and transmitted from the information terminal 40 to the server device 30. The user attribute information is, for example, information indicating the user's gender, the user's height, and the user's weight.

Note that the acquisition route of attribute information is not particularly limited. For example, if the user's attribute information is registered in the remote controller of the toilet seat device 10, the user's attribute information may be transmitted from the remote controller or the toilet seat device 10 to the server device 30 and acquired by the acquisition unit 31. good.

Next, the preprocessing unit 32 performs preprocessing on the electrocardiogram signal acquired in step S11 (S12). Preprocessing is processing to make the electrocardiogram signal suitable for calculating the RR interval. The preprocessing unit 32 limits the frequency band of the electrocardiogram signal to a band of 5 Hz or more and 30 Hz or less, for example, by applying a filter to the electrocardiogram signal acquired in step S11. Further, as shown in FIG. 3, in the case of the toilet seat device 10 in which electrodes are provided on the handrail, the frequency band of the electrocardiogram signal is limited to a band of 1 Hz or more and 20 Hz or less.

Additionally, the preprocessing unit 32 excludes a period in which the amplitude of the electrocardiogram signal is equal to or greater than a predetermined value. Exclusion here means not considering it as valid data. Through this processing, periods in which the electrocardiogram signal would have been appropriately measured due to the user's body movements, straining during defecation, etc. are excluded. When the toilet seat 20 is provided with electrodes as shown in FIG. 2, the predetermined value is, for example, 150 μV, but may be appropriately determined empirically or experimentally according to the specifications of the toilet seat device 10. When electrodes are provided on the handrail as shown in FIG. 3, the predetermined value is, for example, 2 mV.

Furthermore, in step S12, the preprocessing section divides the two types of preprocessed electrocardiogram signals into predetermined time units and stores (stores) them in the storage section 33. The predetermined time unit is, for example, a time unit equivalent to 10 seconds.

Next, the analysis unit 34 sets a count value i for managing the number of calculations of the 3H risk estimate to i=0 and initializes it (S13). The analysis unit 34 extracts seven temporally continuous units (10 seconds x 7 = 70 seconds) from the electrocardiogram signal stored in the storage unit 33 (S14), and calculates the RR interval in each of the seven extracted units of the electrocardiogram signal. is calculated (S15). If the electrocardiogram signal is measured correctly, about 8 to 10 RR intervals are calculated from one unit (10 seconds).

Note that in step S15, the analysis unit 34 detects the R wave of the electrocardiogram signal using a predetermined peak detection algorithm (for example, Hamilton algorithm) in order to calculate the RR interval, and calculates the RR based on the detected R wave. Calculate the interval. Further, the calculated RR interval is in the range of 0.4 seconds or more and 2 seconds or less (30 to 150 bpm when converted to heart rate).

Next, the analysis unit 34 determines whether the total RR interval (total time) calculated in step S15 is 60 seconds or more (S16). This determination is made to improve the accuracy of the 3H risk estimate. If the analysis unit 34 determines that the total RR interval (total time) is less than 60 seconds (No in S16), it supplements the electrocardiogram signal (S17). For example, by extracting one or more units of the electrocardiogram signal immediately after the seven units extracted in step S14 from the storage unit 33 and calculating the RR interval in the extracted one or more units of electrocardiogram signal, the total RR interval can be set to 60. Make it more than seconds.

Note that the method for replenishing the electrocardiogram signal in step S17 is not particularly limited, and among the seven units extracted in step S14, a unit with a smaller subtotal (subtotal time) of the calculated RR intervals (for example, 80% of 10 seconds, (units of less than 8 seconds) may be excluded and the electrocardiogram signal may be supplemented.

If the analysis unit 34 determines that the total RR interval (total time) is 60 seconds or more (Yes in S16) and if the electrocardiogram signal is supplemented (S17), 3H risk estimation is performed. The unit 35 calculates a feature amount related to heart rate fluctuation based on the RR interval of 60 seconds or more in total (S18). The feature amount related to heart rate variability is sometimes called a heart rate variability parameter. Features related to heart rate variability include the average of RR intervals for a total of 60 seconds or more, variance of RR intervals for a total of 60 seconds or more, standard deviation, LF, HF, LF/HF, RMSSD (Root Mean Square of Successive Differences). , Shannon Entropy, Fisher Information, and the like. Note that LF is an abbreviation for Low Frequency, and corresponds to a power spectrum of 0.05 Hz to 0.15 Hz. HF is an abbreviation for High Frequency, and corresponds to a power spectrum of frequency components from 0.15 Hz to 0.40 Hz.

Next, the 3H risk estimation unit 35 calculates the 3H risk estimate (S19). The 3H risk estimation unit 35 calculates a 3H risk estimate using, for example, a machine learning model. This machine learning model is a model to which, for example, logistic regression is applied, and the results of a doctor's judgment of the presence or absence of 3H risk (risk: 1, This is a trained model that was constructed using data associated with ``no risk: 0'' as learning data (teacher data). When the machine learning model receives the attribute information acquired in step S11 and the feature amount related to heart rate variability calculated in step S18, it outputs a numerical value between 0 and 1 as a 3H risk estimate. For example, the higher the 3H risk, the closer the 3H risk estimate is to 1, and the lower the 3H risk, the closer to 0.

Next, the analysis unit 34 increments the count value i (i=i+1) (S20), and determines whether the count value after the increment reaches n (n is a natural number of 2 or more) (S21) . n is a numerical value corresponding to the number of calculations of the 3H risk estimate.

If the analysis unit 34 determines that the count value after incrementing has not reached n (No in S21), the processes of steps S14 to S20 are performed once again. Here, if an electrocardiogram signal corresponding to the interval from 0 seconds to 70 seconds of the entire electrocardiogram signal is extracted in the first step S14, in the second step S14, for example, one unit of time (10 seconds) is extracted. An electrocardiogram signal corresponding to the interval from 10 seconds to 80 seconds of the entire electrocardiogram signal, in which the ECG signal is shifted backward, is extracted. That is, the analysis unit 34 changes the section of the electrocardiogram signal and calculates the RR interval for the plurality of times in order to calculate the 3H risk estimate for the plurality of times. Since the time length of the electrocardiogram signal measured by the toilet seat device 10 is limited (there are few cases in which the electrocardiogram signal can be measured for a sufficiently long time), there is an overlap between the first section and the second section. A configuration means that provides the following is useful.

When the analysis unit 34 determines that the count value after incrementing has reached n (Yes in S21), the determination unit 36, based on the n times of 3H risk estimation values calculated by the 3H risk estimation unit 35, It is determined whether the user has a 3H risk (S22). For example, the determination unit 36 sets the maximum value of the n calculated 3H risk estimates as the representative estimate, determines that the user has a 3H risk when the representative estimate is larger than the threshold, and determines that the representative estimate is If it is below the threshold value, it is determined that the user does not have a 3H risk. The threshold value is, for example, 0.6, but may be appropriately determined empirically or experimentally. The representative estimated value may be a statistic (eg, minimum value, average value, median value, etc.) of the n calculated 3H risk estimated values.

FIG. 5 is a diagram showing the determination results of 3H risk by the health management system 100. The horizontal axis of FIG. Subjects 5 to 7 are subjects who have been determined in advance by a doctor or testing organization to have no 3H risk.

One point in FIG. 5 shows the 3H risk estimate calculated based on the operations in FIG. 4. When multiple points are plotted for one subject, it means that the 3H risk estimate has been calculated multiple times for that subject based on the operation in FIG. 4 .

As shown in Figure 5, subjects 1 to 4 with 3H risk all have the maximum value (representative estimate) of the 3H risk estimate exceeding the threshold (0.6), and subject 5 with no 3H risk -7, the maximum value (representative estimate) of the 3H risk estimate is all below the threshold (0.6). In this way, the determination unit 36 can determine whether the user has a 3H risk.

Next, the vascular adjustment degree estimation unit 37 estimates the user's vascular adjustment degree based on the multiple 3H risk estimation values calculated by the 3H risk estimation unit 35 (S23). The degree of vascular regulation is a parameter that indicates the ability to adjust the heart rate through constriction of blood vessels.If the blood vessels are soft (the blood vessels are flexible and easily contract), the value will be large, indicating that the ability to adjust the heart rate through constriction of the blood vessels is high. means. In addition, the degree of vascular regulation becomes a small value when the blood vessels are hard (close to the arterial effect), which means that the ability to regulate the heart rate due to the contraction of the blood vessels is low. The degree of vascular adjustment is a concept similar to the so-called vascular age (the flexibility of the outside of the blood vessel), but it is a concept that includes not only the vascular age but also the smoothness of blood flow inside the blood vessel.

When the degree of vascular regulation of the subject is high (high ability to regulate heart rate due to constriction of blood vessels), the feature amount related to heart rate variability calculated in step S18 is likely to change due to the influence of breathing, etc., and as a result, the 3H risk estimate is considered to be likely to vary. If the degree of vascular adjustment of the subject is low (the ability to adjust heart rate due to vascular contraction is low), the feature amount related to heart rate fluctuation calculated in step S18 is difficult to change due to the influence of breathing, etc., and as a result, the 3H risk estimate is considered to be less likely to vary.

Therefore, specifically, the vascular adjustment degree estimating unit 37 calculates the difference between the maximum value and the minimum value (corresponding to the two-way arrow in FIG. 5) of the 3H risk estimation values for multiple times to the numerical value of the user's vascular adjustment degree. (measure of relative change). For example, subject No. in FIG. 2, the maximum value of the 3H risk estimate is 0.74 and the minimum value is 0.66, so the degree of blood vessel adjustment is 0.08. Subject no. 3, the maximum value of the 3H risk estimate is 0.98 and the minimum value is 0.95, so the degree of blood vessel adjustment is 0.03.

Subject No. in Figure 5. 5, the maximum value of the 3H risk estimate is 0.53 and the minimum value is 0.50, so the degree of blood vessel adjustment is 0.03. Subject no. 7, the maximum value of the 3H risk estimate is 0.58 and the minimum value is 0.48, so the degree of blood vessel adjustment is 0.10.

Next, the determination unit 36 outputs information for presenting the determination result of the presence or absence of 3H risk in step S22, and the vascular adjustment degree estimating unit 37 presents the estimated vascular adjustment degree in step S23. Output information for. As a result, presentation information including these two pieces of information is output from the server device 30 (S24).

The presentation information is transmitted from the server device 30 to the information terminal 40, and the information terminal 40 (display unit 41) determines the presence or absence of 3H risk in step S22 and the blood vessel risk in step S23 based on the received presentation information. A display screen showing the adjustment degree estimation result is displayed. FIG. 6 is a diagram showing an example of such a display screen. As shown in FIG. 6, the degree of blood vessel adjustment is displayed after being qualified in stages according to the numerical value of the degree of blood vessel adjustment, for example. The degree of blood vessel regulation is, for example, qualified into three levels: good, fair, and poor.

As described above, the health management system 100 can determine whether the user has a 3H risk and can estimate the user's degree of vascular adjustment.

(Embodiment 2)
[composition]
The configuration of the health management system according to the second embodiment will be described below. FIG. 7 is a diagram showing the configuration of a health management system according to the second embodiment. The health management system 100a includes a toilet seat device 10, a server device 30a, and an information terminal 40. The difference from the health management system 100 according to the first embodiment is the functional components of the server device 30a.

Specifically, the server device 30a includes, in addition to an acquisition unit 31, a preprocessing unit 32, a storage unit 33, an analysis unit 34, a 3H risk estimation unit 35, a determination unit 36, and a blood vessel regulation degree estimation unit 37, It includes a contribution estimation section 38 and a carbohydrate excess estimation section 39a. Each of the blood pressure contribution estimating unit 38 and the carbohydrate excess estimating unit 39a is configured by one or more processors (hardware) included in the server device 30a using a computer program (software) stored in a memory such as the storage unit 33. ) is a functional component realized by executing

In the health management system 100a, the acquisition unit 31 acquires the user's blood pressure value in addition to the user's electrocardiogram signal and the user's attribute information.

Based on the acquired blood pressure value of the user, the blood pressure contribution estimating unit 38 calculates a blood pressure that indicates the extent to which the blood pressure contributes to the representative estimated value of the user's 3H risk (the maximum value of the 3H risk estimated values for multiple times). Estimate the contribution.

The sugar excess degree estimating unit 39a estimates the sugar excess degree based on the difference between the representative estimated value of the user's 3H risk and the estimated blood pressure contribution. Furthermore, the carbohydrate excess degree estimating unit 39a outputs information for presenting the estimated carbohydrate excess degree.

[Operation example]
Next, an example of the operation of the health management system 100a will be described. FIG. 8 is a flowchart of an example of the operation of the health management system 100a.

First, the acquisition unit 31 of the server device 30a acquires the user's blood pressure value in addition to the user's electrocardiogram signal and the user's attribute information (S11a). The user's blood pressure values include a systolic blood pressure value (so-called upper blood pressure value) and a diastolic blood pressure value (so-called lower blood pressure value). The user's blood pressure value is, for example, input into the information terminal 40 and transmitted from the information terminal 40 to the server device 30a. The acquisition route of the user's blood pressure value is not particularly limited. The user's blood pressure value may be transmitted to the server device 30a from a blood pressure sensor or a server device that manages the user's blood pressure value, and may be acquired by the acquisition unit 31.

The processing of steps S12 to S23 is similar to the operation example of the first embodiment (FIG. 4).

Next to step S23, the blood pressure contribution estimating unit 38 determines whether the blood pressure is higher than the representative estimated value of the user's 3H risk (the maximum value of the multiple 3H risk estimated values) based on the acquired blood pressure value of the user. The degree of blood pressure contribution indicating the degree of contribution is estimated (S25). The blood pressure contribution degree is a numerical value corresponding to a part of the representative estimated value of the user's 3H risk, and is a parameter indicating how much risk there is due to vascular factors in the representative estimated value. The blood pressure contribution degree has a correlation with the average blood pressure value [mmHg], which is the average of the acquired blood pressure values (systolic blood pressure value [mmHg]/diastolic blood pressure value [mmHg]), and the average blood pressure value The higher the value, the higher the contribution to blood pressure.

The inventor considered the order relationship between the user's average blood pressure value (described later) and the 3H risk (FIG. 10) from the nature of calculating the 3H risk estimate using a machine learning model. Considering the blood pressure contribution as a linear function of the average blood pressure value, the normal blood pressure value is 120/80 (average blood pressure value 100 [mmHg]), and the blood pressure value in the guidance area is 130/85 (average blood pressure value 107.5 [mmHg]). ), by setting the blood pressure contribution degree for the diagnostic standard blood pressure value 140/90 (average blood pressure value 115 [mmHg]), an estimation formula (regression formula) as shown in Fig. 9 y = 0.0133x - 0.8333 I got it. FIG. 9 is a diagram showing an estimation formula for blood pressure contribution. Note that such an estimation formula is an example, and the estimation formula may be appropriately determined empirically or experimentally.

The blood pressure contribution estimating unit 38 estimates the blood pressure contribution by substituting the average blood pressure value determined by the blood pressure value acquired in step S11a into such an estimation formula. For example, subject No. in FIG. 3's blood pressure value is 126/91. The blood pressure contribution estimating unit 38 uses subject no. 3 can be estimated to be 0.62 (square mark on the right side of FIG. 9). Furthermore, subject No. in FIG. 4's blood pressure value is 117/70. The blood pressure contribution estimating unit 38 uses subject no. 4 can be estimated to be 0.41 (square mark on the left side of FIG. 9).

Next, the sugar excess degree estimating unit 39a estimates the sugar excess degree based on the difference between the representative estimated value of the user's 3H risk and the estimated blood pressure contribution (S26a). The sugar excess degree is a numerical value corresponding to another part of the representative estimated value of the user's 3H risk. While the blood pressure contribution is a parameter that indicates the risk of blood vessel factors in the representative estimated value, the carbohydrate excess degree is a parameter that indicates the risk of blood factors in the representative estimated value.

More specifically, the degree of carbohydrate excess is a parameter that indicates how much risk there is due to excessive carbohydrate (glucose) in the blood in the representative estimated value. The degree of carbohydrate excess can also be said to indicate the degree of excess of blood sugar and glycogen in the user's body when the user ingests carbohydrates from food. Furthermore, the degree of carbohydrate excess can also be considered to be a parameter indicating the viscosity (stickiness) of blood. Furthermore, since sugar in the blood passes through glycogen and the excess is converted to neutral fat, sugar excess can be considered a concept that indicates a condition common to both hyperglycemia and hyperlipidemia. can.

Specifically, the sugar excess degree estimating unit 39a estimates the difference between the representative estimated value of the user's 3H risk and the estimated blood pressure contribution as a numerical value of the sugar excess degree. For example, subject No. in FIG. 3, and subject no. 4, blood pressure contribution and carbohydrate excess are estimated as shown in FIG. FIG. 10 is a diagram showing an example of estimating blood pressure contribution and carbohydrate excess.

As shown in FIG. 10, subject No. The representative estimate of the 3H risk for 3 is 0.97, and the blood pressure contribution is 0.62. Then, subject no. The degree of carbohydrate excess in No. 3 is 0.97-0.62=0.35. Also, subject no. The representative estimate of the 3H risk for 5 is 0.72, and the blood pressure contribution is 0.41. Then, subject no. The degree of carbohydrate excess in No. 3 is 0.72-0.41=0.31.

Next, the determination unit 36 outputs information for presenting the determination result of the presence or absence of 3H risk in step S22, and the vascular adjustment degree estimating unit 37 presents the estimated vascular adjustment degree in step S23. Output information for. Furthermore, the carbohydrate excess degree estimating unit 39a outputs information for presenting the carbohydrate excess degree estimated in step S26a. As a result, presentation information including these three pieces of information is output from the server device 30a (S27a).

The presentation information is transmitted from the server device 30a to the information terminal 40, and the information terminal 40 (display unit 41) displays the determination result of the presence or absence of 3H risk in step S22 and the degree of vascular adjustment in step S23 based on the received presentation information. A display screen is displayed that shows the estimation result of , and the estimation result of the carbohydrate excess degree in step S26a. FIG. 11 is a diagram showing an example of such a display screen. As shown in FIG. 11, the degree of carbohydrate excess is displayed after being qualified in stages according to the numerical value of the degree of carbohydrate excess, for example. The degree of carbohydrate excess is determined, for example, by categorizing carbohydrate intake into three levels: excessive, slightly high, and normal. Thereby, the health management system 100a can provide an opportunity to prevent the user from unconsciously ingesting excessive amounts of carbohydrates. Note that the information terminal 40 may display a display screen that prompts the user to take specific improvement actions according to the estimation results. For example, the information terminal 40 may display a display screen that prompts the user to change the carbohydrates consumed at lunch to vegetables (such as broccoli).

As explained above, the health management system 100a can estimate the user's degree of carbohydrate excess.

(Embodiment 3)
[composition]
The configuration of the health management system according to Embodiment 3 will be described below. FIG. 12 is a diagram showing the configuration of a health management system according to the third embodiment. The health management system 100b includes a toilet seat device 10, a server device 30b, and an information terminal 40. The difference from the health management system 100a according to the second embodiment is the functional components of the server device 30b.

The server device 30b includes a metabolic abnormality degree estimating unit 39b in place of the carbohydrate excess degree estimating unit 39a included in the server device 30a. The metabolic abnormality degree estimation unit 39b is a functional It is a constituent element.

The metabolic abnormality degree estimating unit 39b estimates the metabolic abnormality degree based on the difference between the representative estimated value of the user's 3H risk and the estimated blood pressure contribution. Further, the metabolic abnormality degree estimating unit 39b outputs information for presenting the estimated metabolic abnormality degree.

[Operation example]
Next, an example of the operation of the health management system 100b will be described. FIG. 13 is a flowchart of an example of the operation of the health management system 100b.

The processes in step S11a, steps S12 to S23, and step S25 are similar to the operation example of the second embodiment (FIG. 8).

Next to step S25, the metabolic abnormality degree estimating unit 39b estimates the metabolic abnormality degree based on the difference between the representative estimated value of the user's 3H risk and the estimated blood pressure contribution (S26b). The degree of metabolic abnormality is a numerical value corresponding to another part of the representative estimated value of the user's 3H risk. While the blood pressure contribution degree is a parameter indicating the risk of vascular factors in the representative estimated value, the degree of metabolic abnormality is a parameter indicating the risk of blood factors in the representative estimated value.

The state in which sugar (glucose) in the blood is excessive as described in Embodiment 2 is considered to reflect a state in which there is an abnormality in metabolism (metabolic function is inferior to that of a healthy person). In other words, the degree of carbohydrate excess in Embodiment 2 can also be considered as the degree of metabolic abnormality. Therefore, the metabolic abnormality degree estimation unit 39b estimates the metabolic abnormality degree.

The method for estimating the degree of metabolic abnormality is the same as the method for estimating the degree of carbohydrate excess. Specifically, the metabolic abnormality degree estimation unit 39b estimates the difference between the representative estimated value of the user's 3H risk and the estimated blood pressure contribution degree as a numerical value of the metabolic abnormality degree.

Next, the determination unit 36 outputs information for presenting the determination result of the presence or absence of 3H risk in step S22, and the vascular adjustment degree estimating unit 37 presents the estimated vascular adjustment degree in step S23. Output information for. Further, the metabolic abnormality degree estimating unit 39b outputs information for presenting the metabolic abnormality degree estimated in step S26b. As a result, presentation information including these three pieces of information is output from the server device 30b (S27b).

The presentation information is transmitted from the server device 30b to the information terminal 40, and the information terminal 40 (display unit 41) displays the determination result of the presence or absence of 3H risk in step S22 and the degree of vascular adjustment in step S23 based on the received presentation information. , and a display screen showing the estimation results of the degree of metabolic abnormality in step S26b. FIG. 14 is a diagram showing an example of such a display screen. As shown in FIG. 14, the degree of metabolic abnormality is displayed after being qualified in stages according to the numerical value of the degree of metabolic abnormality, for example. The degree of metabolic abnormality is determined, for example, by categorizing the metabolic state into three levels: poor, somewhat poor, and normal. Thereby, the health management system 100b can provide an opportunity to improve the user's metabolism. Note that the information terminal 40 may display a display screen that prompts the user to take specific improvement actions according to the estimation results. For example, the information terminal 40 may display a display screen that encourages the user to increase the number of steps per day by 2,000, or may prompt the user to do a simple muscle training indoors (such as 7-second squats). A display screen may be displayed to prompt the user.

As explained above, the health management system 100b can estimate the degree of metabolic abnormality of the user.

(Effects, etc.)
As described above, the health management system 100 includes the acquisition unit 31 that acquires the user's electrocardiogram signal measured using the electrode by the toilet seat device 10 equipped with the electrode, and the acquisition unit 31 that acquires the electrocardiogram signal of the user measured using the electrode. The analysis unit 34 calculates the RR interval for a plurality of times by changing the interval, and the analysis unit 34 calculates the RR interval for a plurality of times by changing the interval. and an estimating unit that calculates a plurality of 3H risk estimated values that are estimated values of risks corresponding to at least one of hyperlipidemia and hyperlipidemia. The estimating unit estimates a degree of vascular adjustment that indicates the adjustment function of heart rate due to contraction of blood vessels based on the calculated multiple 3H risk estimates, and outputs information for presenting the estimated degree of vascular adjustment. do. The estimating section here corresponds to the 3H risk estimating section 35 and the vascular adjustment degree estimating section 37 of the above embodiment.

Such a health management system 100 can present information regarding health risks. Specifically, the health management system 100 can present information regarding the degree of vascular regulation.

Also, for example, the estimating unit estimates the user's degree of vascular adjustment based on the difference between the maximum value and the minimum value of the 3H risk estimation values for a plurality of times.

Such a health management system 100 can present information regarding the degree of blood vessel regulation.

Furthermore, for example, in the health management system 100a or the health management system 100b, the acquisition unit 31 further acquires the user's blood pressure value. The estimating unit takes the maximum value of the multiple 3H risk estimates as a representative estimate of the user's 3H risk, and based on the acquired blood pressure value, calculates a blood pressure that indicates the extent to which blood pressure contributes to the representative estimate. Estimate the contribution. The estimation section here corresponds to the blood pressure contribution estimation section 38 of the above embodiment.

Such a health management system 100a can estimate blood pressure contribution.

For example, in the health management system 100a, the estimating unit further estimates the degree of carbohydrate excess based on the difference between the representative estimate and the estimated blood pressure contribution, and relates to the estimated degree of carbohydrate excess. Output information for presentation. The estimating section here corresponds to the sugar excess degree estimating section 39a of the above embodiment.

Such a health management system 100a can present information regarding the degree of carbohydrate excess.

For example, in the health management system 100b, the estimating unit further estimates the degree of metabolic abnormality based on the difference between the representative estimate and the estimated blood pressure contribution, and provides a presentation regarding the estimated degree of metabolic abnormality. Output information to do so. The estimating section here corresponds to the metabolic abnormality degree estimating section 39b of the above embodiment.

Such a health management system 100b can present information regarding the degree of metabolic abnormality.

For example, the health management system 100 further includes a preprocessing unit 32 that performs preprocessing on the acquired electrocardiogram signal. The analysis unit 34 calculates the RR interval for a part of the acquired electrocardiogram signal that has undergone preprocessing. The preprocessing includes a process of limiting the frequency band and a process of excluding a period in which the amplitude is equal to or greater than a predetermined value.

Such a health management system 100 can improve the estimation accuracy of the 3H risk estimate by performing preprocessing on the electrocardiogram signal.

For example, the health management system 100 further determines that the user has a 3H risk when the maximum value of the calculated 3H risk estimates for the plurality of doses is larger than the threshold, and determines that the user has a 3H risk for the calculated multiple doses. A determination unit 36 is provided that determines that the user does not have a 3H risk when the maximum value of the estimated risk value is less than or equal to a threshold value. The determination unit 36 outputs information for presenting the determination result of the presence or absence of 3H risk.

Such a health management system 100 can present information regarding the determination result of the presence or absence of 3H risk.

For example, the acquisition unit 31 further acquires user attribute information. The estimation unit calculates 3H risk estimates for multiple times based on the calculated RR intervals for multiple times and the acquired attribute information. The estimating section here corresponds to the 3H risk estimating section 35 of the above embodiment.

Such a health management system 100 can calculate a 3H risk estimate in consideration of the user's attribute information.

For example, the health management system 100 further includes the toilet seat device 10 and a display unit 41 that displays an image based on the output information for presenting the degree of blood vessel adjustment.

Such a health management system 100 can present images related to the degree of blood vessel regulation.

In addition, the health management method executed by a computer such as the health management system 100 includes a first acquisition step S11 of acquiring an electrocardiogram signal of the user measured using the electrode by the toilet seat device 10 provided with the electrode, and the acquired electrocardiogram signal. An analysis step S15 for calculating an RR interval for a part of the signal section, an analysis step S15 for calculating an RR interval for a plurality of times by changing the section, and an analysis step S15 for calculating an RR interval for a plurality of times based on the calculated RR interval for a plurality of times. , a first estimation step S19 in which the user calculates a 3H risk estimate, which is an estimated value of the risk corresponding to at least one of hypertension, hyperglycemia, and hyperlipidemia, for multiple times, and for the calculated multiple times; A second estimation step S23 of estimating the degree of vascular adjustment indicating the adjustment function of heart rate due to contraction of the blood vessels based on the 3H risk estimation value of and an output step S24.

Such a health management method can present information regarding the degree of vascular regulation.

For example, the health management method further includes a second acquisition step S11a of acquiring the user's blood pressure value, and setting the maximum value of the plurality of 3H risk estimates as a representative estimate of the 3H risk of the user. A third estimation step S25 of estimating a blood pressure contribution indicating the extent to which blood pressure contributes to the representative estimated value based on the blood pressure value; It includes a fourth estimation step S26a for estimating the sugar excess degree, and a second output step S27a for outputting information for presenting the estimated sugar excess degree.

Such a health management method can present information regarding the degree of carbohydrate excess.

(Other embodiments)
Although the embodiments have been described above, the present invention is not limited to such embodiments.

For example, in the above embodiment, the electrocardiogram signal is measured by the toilet seat device, but it may be measured by another device such as an electrocardiograph. That is, the health management system may estimate the 3H risk etc. based on the electrocardiogram signal measured by a device other than the toilet seat device.

Furthermore, in the above embodiments, the health management system is realized by a plurality of devices. In this case, the functional components included in the health care system described in the above embodiments may be distributed to the plurality of devices in any manner. For example, the preprocessing section may be provided by the toilet seat device instead of the server device, and the acquisition section of the server device may acquire the preprocessed electrocardiogram signal from the toilet seat device.

Furthermore, in the above embodiments, the processing executed by a specific processing unit may be executed by another processing unit. Further, the order of the plurality of processes may be changed, or the plurality of processes may be executed in parallel. For example, estimation of the degree of vascular regulation and estimation of the degree of carbohydrate excess (or degree of metabolic abnormality) may be performed sequentially or in parallel.

Furthermore, the general or specific aspects of the present invention may be implemented in a system, device, method, integrated circuit, computer program, or computer-readable recording medium such as a CD-ROM. Further, the present invention may be realized by any combination of a system, an apparatus, a method, an integrated circuit, a computer program, and a recording medium.

For example, the present invention may be realized as a server device according to the above embodiment. The present invention may be realized as a health management method executed by a computer such as a health management system. Further, the present invention may be realized as a program for causing a computer to execute such a health management method, or may be realized as a computer-readable non-temporary recording medium in which such a program is recorded. Good too.

In addition, without departing from the spirit of the present invention, various modifications that can be thought of by those skilled in the art may be made to this embodiment, and embodiments constructed by combining components of different embodiments may also be incorporated into one or more embodiments. may be included within the range.

10 Toilet seat device 20 Toilet seat 21 First electrode 22 Second electrode 23

Body earth electrode

30, 30a, 30b Server device 31 Acquisition unit 32 Preprocessing unit 33 Storage unit 34 Analysis unit 35 3H risk estimation unit 36 Judgment unit 37 Estimation of vascular adjustment degree Section 38 Blood pressure contribution estimation section 39a Carbohydrate excess estimation section 39b Metabolic abnormality estimation section 40 Information terminal 41

Display section

100, 100a, 100b Health management system

Claims

an acquisition unit that acquires a user's electrocardiogram signal measured using the electrode by a toilet seat device including the electrode;
an analysis unit that calculates an RR interval for a partial section of the acquired electrocardiogram signal, the analysis unit that calculates a plurality of RR intervals by changing the section;
Based on the calculated RR interval for multiple times, calculate a 3H risk estimate value for multiple times, which is an estimated value of the risk that the user falls under at least one of hypertension, hyperglycemia, and hyperlipidemia. and an estimator to
The estimating unit is configured to estimate a degree of vascular adjustment indicating a function of adjusting heart rate due to contraction of blood vessels based on the calculated 3H risk estimate values for a plurality of times, and to provide a presentation regarding the estimated degree of vascular adjustment. A health management system that outputs information.
The health management system according to claim 1, wherein the estimator estimates the vascular adjustment degree of the user based on a difference between a maximum value and a minimum value of the 3H risk estimation values for a plurality of times.
The acquisition unit further acquires the blood pressure value of the user,
The estimation unit is
The maximum value of the 3H risk estimates for multiple times is set as a representative estimate of the 3H risk of the user,
The health management system according to claim 1 or 2, wherein a blood pressure contribution degree indicating a degree of contribution of blood pressure to the representative estimated value is estimated based on the acquired blood pressure value.
The estimation unit further includes:
estimating the degree of carbohydrate excess based on the difference between the representative estimate and the estimated blood pressure contribution;
The health management system according to claim 3, wherein information for presenting the estimated degree of carbohydrate excess is output.
The estimation unit further includes:
Estimating the degree of metabolic abnormality based on the difference between the representative estimated value and the estimated blood pressure contribution degree,
The health management system according to claim 3, wherein information for presenting the estimated degree of metabolic abnormality is output.
Furthermore, a preprocessing unit that performs preprocessing on the acquired electrocardiogram signal,
The analysis unit calculates the RR interval for a part of the acquired electrocardiogram signal that has undergone the preprocessing,
The health care system according to claim 1, wherein the preprocessing includes processing to limit a frequency band and processing to exclude a period in which the amplitude is equal to or greater than a predetermined value.
Furthermore, it is determined that the user has a 3H risk when the maximum value of the calculated 3H risk estimation value for the plurality of times is larger than a threshold value, and the maximum value of the calculated 3H risk estimation value for the plurality of times is greater than the threshold value. comprising a determination unit that determines that the user does not have a 3H risk when the value is below a threshold;
The health management system according to claim 1, wherein the determination unit outputs information for presenting the determination result of the presence or absence of 3H risk.
The acquisition unit further acquires attribute information of the user,
The health management system according to claim 1, wherein the estimation unit calculates the 3H risk estimate for multiple times based on the calculated RR interval for multiple times and the acquired attribute information.
moreover,
The toilet seat device;
The health management system according to claim 1, further comprising a display unit that displays an image based on the output information for presenting the degree of blood vessel adjustment.
A health management method performed by a computer, the method comprising:
a first acquisition step of acquiring a user's electrocardiogram signal measured using the electrode by a toilet seat device including the electrode;
an analysis step of calculating an RR interval for a partial section of the acquired electrocardiogram signal, the analysis step of calculating a plurality of RR intervals by changing the section;
Based on the calculated RR interval for multiple times, calculate a 3H risk estimate value for multiple times, which is an estimated value of the risk that the user falls under at least one of hypertension, hyperglycemia, and hyperlipidemia. a first estimation step of
a second estimation step of estimating a degree of vascular adjustment indicating a heart rate adjustment function due to vascular contraction, based on the calculated 3H risk estimate values for a plurality of times;
a first output step of outputting information for presenting the estimated degree of blood vessel regulation.
Furthermore, a second acquisition step of acquiring the blood pressure value of the user;
The maximum value of the 3H risk estimates for a plurality of times is taken as a representative estimate of the 3H risk of the user, and a blood pressure contribution degree indicating the extent to which blood pressure contributes to the representative estimate based on the acquired blood pressure value. a third estimation step of estimating
a fourth estimation step of estimating the degree of carbohydrate excess based on the difference between the representative estimated value and the estimated blood pressure contribution;
The health management method according to claim 10, further comprising: a second output step of outputting information for presenting the estimated degree of carbohydrate excess.
A program for causing a computer to execute the health management method according to claim 10 or 11.