JP2022008126A - Information processing system, server, information processing method and program - Google Patents

Abstract

To provide an information processing system according to the present embodiment capable of generating renal function information particularly relating to renal function and user assistance data for alerting a decline in renal function of a user based on measurement data through a biological data generation unit and a user assistance data generation unit.SOLUTION: An information processing system 1 for receiving measurement data from a measurement device worn by a user via a network at predetermined intervals and generating renal function information based on the measurement data, includes a biological data generation unit that executes a prescribed operation on the measurement data to generate blood pressure information and exercise information, and generates renal function information of the user based on the blood pressure information and the exercise information.SELECTED DRAWING: Figure 1

Description

The present disclosure relates to an information processing system, a server, an information processing method and a program that generate user support data based on measurement data acquired from a measuring device.

It is effective for health management to continuously acquire user's biological data using a measuring device and to detect a disease at an early stage or detect a change in a medical condition from the change. For that purpose, it is necessary to grasp various health conditions from a plurality of acquired biometric data.

For example, there is known a blood pressure information measuring system that acquires a user's electrocardiographic waveform data and pulse waveform data from a measuring device and generates blood pressure information (see, for example, Patent Document 1). Further, for example, a development support server that generates a mathematical model that is a causal relationship for performing an operation from a user's measurement data to biometric information data and provides the mathematical model so that it can be used is also known (for example, Patent Document 2). reference.).

Japanese Patent No. 6202510 Japanese Patent No. 6257015

By the way, there is a deep relationship between kidney function and hypertension, and it is said that hypertension tends to lead to chronic kidney disease. It is also said that people with chronic kidney disease are more likely to have cardiovascular disease.

However, chronic kidney disease has few initial symptoms and is difficult to detect, and if left untreated until subjective symptoms begin to appear, it will worsen to the extent that dialysis etc. is necessary as a result, so it is an objective index. Is required to be found in.

Therefore, in the present disclosure, an information processing system, a server, an information processing method, and a program that generate user support data based on the measurement data acquired from the measuring device will be described.

The information processing system according to one aspect of the present disclosure is an information processing system that receives measurement data from a measuring device worn by a user via a network at a predetermined cycle and generates renal function information from the measured data. It includes a biological data generation unit that executes a predetermined calculation on the measurement data, generates blood pressure information and exercise information, and generates kidney function information of the user based on the blood pressure information and exercise information.

The server in one aspect of the present disclosure is a server that receives measurement data from a measurement device worn by a user via a network at a predetermined cycle and generates renal function information from the measurement data, and the measurement data is combined with the server. It is provided with a biological data generation unit that executes a predetermined calculation, generates blood pressure information and exercise information, and generates kidney function information of the user based on the blood pressure information and exercise information.

Further, the information processing method according to one aspect of the present disclosure is an information processing method in which measurement data is received from a measuring device worn by a user via a network at a predetermined cycle and renal function information is generated from the measured data. Then, the biological data generation unit executes a predetermined calculation on the measurement data, generates blood pressure information and exercise information, and generates the user's renal function information based on the blood pressure information and exercise information. include.

Further, the program according to one aspect of the present disclosure is a program for a computer that receives measurement data from a measurement device worn by a user via a network at a predetermined cycle and generates renal function information from the measurement data. In the computer, the program executes a predetermined calculation on the measurement data, generates blood pressure information and exercise information, and generates kidney function information of the user based on the blood pressure information and exercise information. Realize a functional part including a data generation part.

According to the present disclosure, in the information processing system according to the present embodiment, the renal function information regarding the renal function and the renal function of the user are deteriorated based on the measurement data via the biological data generation unit and the user support data generation unit. Generate user assistance data to alert you. As a result, for example, it becomes possible for the user to easily obtain information on the user's renal function and an alert that the renal function is deteriorated at a stage where there is no subjective symptom, and the convenience of the user is improved.

It is a block block diagram which shows the information processing system which concerns on one Embodiment of this disclosure. It is a figure which shows the hardware configuration of the management server 100 of FIG. It is a block diagram illustrating the function of the storage unit 120 and the control unit 130 of FIG. It is a schematic diagram which shows the example of the tag information associated with the measurement data of FIG. It is a figure for demonstrating the example of the electrocardiographic waveform and the pulse waveform measured by the measuring apparatus 300 of FIG. It is a flowchart which shows the example of the processing of the information processing system 1 of FIG.

The contents of the embodiments of the present invention will be described in a list. The system according to the embodiment of the present invention has the following configurations.

[Item 1]
An information processing system that receives measurement data from a measurement device worn by a user via a network at a predetermined cycle and generates renal function information from the measurement data.
A biological data generation unit that executes a predetermined calculation on the measurement data, generates blood pressure information and exercise information, and generates kidney function information of the user based on the blood pressure information and exercise information.
An information processing system characterized by this.
[Item 2]
Further, it is provided with a user support data generation unit that generates user support data related to exercise according to the result of comparing the exercise information and the reference exercise information.
The information processing system according to item 1, wherein the information processing system is characterized by the above.
[Item 3]
The biological data generation unit generates blood pressure transition information for a predetermined time from the blood pressure information, calculates blood pressure load information by obtaining an integrated value of a value exceeding the reference blood pressure information and time, and obtains blood pressure load information from the blood pressure load information. It generates the renal function information and generates the above-mentioned renal function information.
The blood pressure information is blood pressure information as a result of performing thinning correction on the blood pressure information according to the result of comparing the exercise information and the reference exercise information.
The information processing system according to any one of item 1 or item 2.
[Item 4]
The biological data generation unit stores the correspondence information between the integrated value and the state of the renal function, and generates the renal function information corresponding to the integrated value with reference to the correspondence information.
The information processing system according to item 3, characterized in that.
[Item 5]
The predetermined cycle is the same as or substantially the same as the predetermined time for generating the blood pressure transition information.
The biological data generation unit acquires the maximum value of blood pressure information within the predetermined time or the maximum value of change in blood pressure transition information, and makes a difference of a predetermined value or more as compared with a reference value or a past average value of a user. Judging the outlier information that appears,
The information processing system according to any one of item 3 or item 4, wherein the information processing system is characterized by the above.
[Item 6]
Further, it comprises a user support data generation unit that generates user support data including at least one of advice or alert based on the renal function information.
The information processing system according to item 1 to item 5, wherein the information processing system is characterized by the above.
[Item 7]
A server that receives measurement data from a measurement device worn by a user via a network at a predetermined cycle and generates renal function information from the measurement data.
A biological data generation unit that executes a predetermined calculation on the measurement data, generates blood pressure information and exercise information, and generates kidney function information of the user based on the blood pressure information and exercise information.
A server that features that.
[Item 8]
It is an information processing method that receives measurement data from a measuring device worn by a user via a network at a predetermined cycle and generates renal function information from the measured data.
The biological data generation unit includes a step of executing a predetermined calculation on the measurement data, generating blood pressure information and exercise information, and generating kidney function information of the user based on the blood pressure information and exercise information.
An information processing method characterized by that.
[Item 9]
It is a program for a computer that receives measurement data from a measuring device worn by a user via a network at a predetermined cycle and generates renal function information from the measured data.
The program is installed in the computer.
A functional unit including a biological data generation unit that executes a predetermined calculation on the measurement data, generates blood pressure information and exercise information, and generates renal function information of the user based on the blood pressure information and exercise information is realized. do,
A program characterized by that.

Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. The embodiments described below do not unreasonably limit the contents of the present disclosure described in the claims. Also, not all of the components shown in the embodiments are essential components of the present disclosure. In addition, the features shown in each embodiment can be applied to other embodiments as long as they do not contradict each other.

(Embodiment 1)
<Structure>
FIG. 1 is a block configuration diagram showing an information processing system 1 according to the first embodiment of the present disclosure. The information information system 1 receives, for example, the user's measurement data from the measurement device 300 via the network NW on the management server 100, and generates biometric data by performing a predetermined calculation on the measurement data. It is a system that generates user support data based on the biometric data.

The information processing system 1 has a management server 100, a user terminal device 200, a measuring device 300, and a network NW. The management server 100 and the user terminal device 200 are connected via the network NW. The network NW is composed of the Internet, an intranet, a blockchain network, a wireless LAN (Local Area Network), a WAN (Wide Area Network), and the like.

The management server 100 is, for example, a device that receives user's measurement data from the measurement device 300 via a network via the user terminal device 200 and calculates the measurement data into biometric data, for example, various Web services. It consists of the server equipment provided.

The user terminal device 200 is an information processing device possessed by the user, such as a personal computer, a tablet terminal, a smartphone, a smart watch, a mobile phone, a PHS, or a PDA. It is used to display a waveform graph or the like, or to display user support data (details will be described later) generated based on biometric data. User information such as a user's identification number, date of birth, gender, height, weight, stride, etc. is registered in the user terminal device 200 in advance, and user information including age calculated from the date of birth is measured data. It is transmitted to the management server 100 via the network NW in association with.

The measuring device 300 is a device that measures the biometric data of the user, and is, for example, a wearable device that is used by being worn on the body such as the wrist or arm of the user. The measuring device 300 is, for example, a plurality of types of devices for measuring data of a user's electrocardiogram, pulse wave, temperature (body temperature), acceleration, and angular velocity at a predetermined periodic timing. The predetermined cycle may be preset or may be arbitrarily set by the user. More specifically, for example, a time period in seconds may be set, or the same may be set depending on the frequency.

As an example of the specific configuration of the measuring device 300, a device may be configured in which two electrodes are brought into contact with the skin and the electrocardiogram is acquired as electrocardiographic waveform data from the time change of the difference in the detected potential. The radio wave type may be data acquired by a galvanic skin reaction. In addition, each light is radiated to the skin from LEDs that emit green, red, and infrared light, and the time change in the intensity of the light received by the photodiode causes a pulse wave due to the change in the volume of the blood vessel caused by the heartbeat of the user's heart. It may be configured by a device that acquires pulse waveform data, and the pulse waveform that can be detected by this method is a photoelectric volume pulse waveform. Further, the device may be configured to acquire the user's skin temperature as data by a temperature sensor in contact with the user's skin. Further, it may be configured by a 3-axis acceleration sensor that detects the variation state of each of the orthogonal XYZ axes, and the user's motion is acquired as acceleration data, and for example, the measuring device 300 is attached to the user's wrist, arm, or the like. In this case, the measuring device 300 acquires acceleration data as an acceleration in which the swing of the wrist, arm, or the like and the movement of the whole body are combined. Further, it may be configured by a gyro sensor (angular velocity sensor) that detects the rotational angular velocity in each of the orthogonal XYZ axes, and the user's motion is acquired as angular velocity data, for example, the measuring device 300 is attached to the user's wrist, arm, or the like. If so, the measuring device 300 acquires the angular velocity data as the angular velocity in which the rotation of the wrist, the arm, or the like and the movement of the whole body are combined.

Between the user terminal device 200 and the measuring device 300, Bluetooth (registered trademark), Near Field radio Communication (NFC), Afero (registered trademark), Zigbee (registered trademark), Z-Wave (registered trademark). ) Or is connected using a wireless LAN or the like. In addition, instead of such a wireless connection, a wired connection may be made. Further, the user terminal device 200 and the measuring device 300 may be an integrated device. For example, the measuring device 300 may be provided with a communication function so as to be able to directly communicate with the management server 100. Is also good.

There are one or a plurality of user terminal devices 200, and they are connected to the network NW for the number of users who use the measuring device 300. There are one or a plurality of measuring devices 300, and they are connected to the number of user terminal devices 200 used by one user. When one user uses a plurality of measuring devices 300, the plurality of measuring devices 300 are connected to one user terminal device 200.

<Management server 100>
FIG. 2 is a diagram showing a hardware configuration of the management server 100. FIG. 3 is a block diagram illustrating the functions of the storage unit 120 and the control unit 130. The configuration shown in the figure is an example, and may have other configurations.

The management server 100 includes a communication unit 110, a storage unit 120, a control unit 130, and an input / output unit 140. These functional units are realized by executing a predetermined program for the management server 100.

The communication unit 110 is a communication interface for communicating with the user terminal device 200, and communication is performed according to a communication protocol such as TCP / IP (Transmission Control Protocol / Internet Protocol).

The storage unit 120 stores programs for executing various control processes and functions in the control unit 130, input data, and the like, and is composed of a RAM (Random Access Memory), a ROM (Read Only Memory), and the like. To. Further, as shown in FIG. 3, the storage unit 120 associates the measurement data DB 121, which stores the measurement data by the measuring device 300 with the user information, and the biometric data calculated from the measurement data with the user information. The biometric data DB 122 to be stored, the user support data DB 123 to store the user support data generated based on the biometric data in association with the user information, and the user information DB 124 to store the user information including the user identification number. .. Further, the user information includes the account information generated by the data management unit 131, and the user information DB 124 may store the account information in association with other user information. Further, the storage unit 120 temporarily stores the data that has communicated with the user terminal device 200. The data structure of the DB is not limited to this, and a part of the above-mentioned DB may be stored in the user terminal device 200 or the measuring device 300.

The control unit 130 controls the overall operation of the management server 100, and is composed of a CPU (Central Processing Unit) and the like. Further, as shown in FIG. 3, the control unit 130 includes functional units such as a data management unit 131, a biometric data generation unit 132, a user support data generation unit 133, and a data output unit 134.

The data management unit 131 generates account information for each user who uses the measuring device 300. This account information generation is performed when the user who uses the measuring device 300 registers the account information on the user terminal device 200. Therefore, the data management unit 131 controls whether or not the user terminal device 200 and other terminal devices of the user can access various DBs in the storage unit 120 for each account. The data management unit 131 stores various data such as measurement data, biometric data, and user support data in a corresponding DB in association with user information. Further, at this time, the data management unit 131 can associate the measurement data with predetermined tag information and store it.

FIG. 4 is a schematic diagram showing an example of tag information associated with the measurement data of FIG. The data D1 shown in FIG. 4 is the measurement data of the measuring device 300. The tag T1 is tag information associated with the data D1, and for example, the time information in which the measuring device 300 measures the data D1 or the time information in which the data D1 is transmitted from the measuring device 300 to the user terminal device 200 is time-series. It is stored as data. Alternatively, both the measured time information and the transmitted time information may be associated. For example, in the first line of the tag T1 shown in FIG. 4, "20180620120746144" is stored, but it indicates 12:07:46:144 ms on June 20, 2018. Such time information can be obtained from the communication log. This makes it possible to grasp which time zone the measurement data belongs to.

It should be noted that the association of measurement data with such tag information is not limited to time information, and physical information or activity information indicating the user's physical condition or activity state may be freely entered and stored as tag information. Have them choose from a given option (for example, in response to the question "How are you feeling now?", Choose one of "1: good, 2: normal, 3: bad", etc.) and make that choice. You may try to remember the answer you gave. As a result, when the control unit 150 generates biometric data, by associating the tag information with the biometric data, it is possible to generate more accurate biometric data, and the user support generated based on the tag information can be generated. The data can also be more personalized.

Further, for example, the data management unit 131 can sort the data D1 in the time order of the tag T1 as shown in FIG. The reason for this configuration is that the measurement data is acquired in chronological order based on the biometric data of the user, and it is easier to process if the measurement data is arranged in chronological order. When receiving via the unit 110, the received data may be reversed (the transmitted data transmitted later is received before the transmitted data transmitted earlier) due to a change in the communication status or the like. This is to prevent inconsistency in the measurement data at that time. This makes it possible to prevent inconsistencies in the measured data.

The biometric data generation unit 132 performs a predetermined operation on the measurement data stored in the measurement data DB 121 to generate biometric data. This biometric data may be any information as long as it can be calculated from the measurement data, for example, the user's blood pressure information, heartbeat information, blood oxygen level information, maximum oxygen intake information, electrocardiographic information, etc. Breath rate, body temperature information, step count information, stride information, center of gravity position information, posture information, action type information, stress information, exercise amount information, exercise load information, movement distance information, movement speed information, activity amount information, hands or legs, etc. It is data such as operation information of the mounting part of the above, and is calculated from the measurement data by a known method. The biometric data generated by the calculation is stored in the biometric data DB 122.

In addition, for example, measurement data by a known learning device, biometric data generated based on the measurement data (for example, heartbeat information, blood pressure information, etc.) and positive biometric data (for example, heartbeat information based on a known medical device, etc.) A machine learning model is created in advance based on the teacher data associated with the correspondence with the blood pressure information (for example, information indicating the degree and range of the error may be included), and the biological data generation unit 132. May generate biometric data using the determination using the machine learning model as the above-mentioned predetermined calculation (analysis).

Here, a method of calculating the maximum blood pressure and the minimum blood pressure, which are biological data, from the measurement data will be exemplified. FIG. 5 is a diagram for explaining an example of an electrocardiographic waveform and a pulse wave measured by the measuring device 300 of FIG. 1, and is a diagram for explaining an example of the electrocardiographic waveform and the pulse wave of the user measured by the measuring device 300 and stored in the storage unit 120. And the photoelectric volume pulse waveform, the velocity pulse waveform in which the photoelectric volume pulse waveform is first-order differentiated by time, and the acceleration pulse waveform in which the photoelectric volume pulse waveform is second-order differentiated by time are shown. FIG. 5 shows an electrocardiographic waveform, a photoelectric volume pulse waveform, a velocity pulse waveform, and an acceleration pulse waveform in order from the top. The vertical axis shows the intensity of each waveform, and the electrocardiographic waveform and the photoelectric volume pulse waveform are represented by MV indicating the potential. The horizontal axis shows the passage of time, and shows the passage of time from left to right.

An electrocardiographic waveform is a waveform showing a periodic change in an electrical signal that causes a person's heart to beat. In the electrocardiographic waveform, the names of P wave, Q wave, R wave, S wave, and T wave are assigned to the inflection points of the shape, respectively, and indicate one cycle of heartbeat. The P wave represents the atrial contraction, the Q wave, the R wave, and the S wave represent the state of ventricular contraction, and the T wave represents the start of ventricular dilation.

The photoelectric volume pulse waveform is a waveform showing changes in blood pressure and volume in the peripheral vascular system accompanying the beating of the human heart. In the photoelectric volume pulse waveform, the names of A wave, P wave, V wave, and D wave are assigned to the inflection points of the shape, respectively, and indicate one cycle of the heartbeat. Using the A wave as the reference point at the time when the arterial pulse wave is generated, the P wave is the Percussion wave (shock wave) generated by the ejection of the left ventricle, the V wave is the Valley wave (wave due to the overlapping uplift) generated when the aortic valve is closed, and the D wave. Indicates a Dicrotic wave (overlapping wave) which is a reflected vibration wave.

The velocity pulse waveform is a first-order derivative of the photoelectric volume pulse waveform with respect to time. The acceleration pulse waveform is a first-order derivative of the velocity pulse waveform, that is, a second-order derivative of the photoelectric volume pulse waveform. As shown in FIG. 5, the acceleration pulse waveform has a wave (initial contraction positive wave), b wave (initial contraction negative wave), c wave (mid-contraction re-rise wave), and d wave (contraction) at each peak of the waveform. The names of the late re-descending wave), the e wave (extended early positive wave), and the f wave (extended early negative wave) are assigned.

The ratio of the intensity of the b wave to the intensity of the a wave and the ratio of the intensity of the f wave to the intensity of the e wave are parameters indicating the elasticity, that is, the elasticity of the blood vessel, respectively. The main vascular components are vascular endothelium (Endothelium), elastic fibers (Elastin), proteins (Collagen), and smooth muscle (Smooth Muscle). These components have different properties, and Collagen and Elastin have a strong influence on the elasticity of blood vessels at the maximum blood pressure and the minimum blood pressure, respectively. Therefore, the elasticity that differs depending on the blood pressure value is indicated by the parameters of the ratio of the intensity of the b wave to the intensity of the a wave (b / a) and the ratio of the intensity of the f wave to the intensity of the e wave (f / e). These values also fluctuate depending on the influence of age, gender, and environment variables. Therefore, the values of (b / a) and (f / e) can be calculated as the characteristic information of the acceleration pulse waveform.

As shown in FIG. 5, the time difference between the time Tr at which the R wave is generated and the time Tp at which the P wave is generated is the ventricular systolic pulse wave propagation time PTT_SYS. The time of the difference between the time Tt in which the T wave is generated and the time Td in which the D wave is generated is the ventricular diastolic pulse wave propagation time PTT_DIA. That is, from the R wave time Tr and T wave time Tt of the electrocardiographic waveform, and the T wave time Tp and D wave time Td of the photoelectric volume pulse waveform, the ventricular systolic pulse wave propagation time PTT_SYS and the ventricular diastole period. The pulse wave propagation time PTT_DIA can be calculated.

Further, it is known that the relationship between the pulse wave velocity and the Young's modulus of the arterial wall has a correlation expressed by a predetermined formula, and the relationship between the Young's modulus and the blood pressure value is also shown by a predetermined formula. It is known to be correlated. Therefore, the maximum blood pressure can be obtained by a predetermined formula of the ventricular systolic pulse wave velocity PTT_SYS, and the minimum blood pressure can be obtained by a predetermined formula of the ventricular diastolic pulse wave propagation time PTT_DIA. This makes it possible to calculate the maximum blood pressure and the minimum blood pressure.

In addition, a method of calculating walking speed information, which is biological data, from measurement data will be exemplified. For example, a known calculation method or the like from the waveform data of acceleration data measured by the measuring device 300 worn by the user on the wrist may be used alone or in combination (for example, averaging or weighting). The walking speed information can be obtained by, for example, the walking speed information is calculated by integrating the acceleration data at predetermined time intervals. In addition to this, the user terminal device 200 or the measuring device 300 is provided with a GPS function, and more accurate walking speed information is referred to by referring to the position information by GPS and the walking speed information calculated from the time information related to the position information. May be obtained. However, GPS accuracy generally deteriorates, especially when it is indoors or underground or when the travel distance is relatively short (for example, within a few meters). Therefore, for example, the user operates the user terminal device 200 by GPS. It may be possible to select whether to use the information in combination (or to use it instead of the calculation by the acceleration sensor), or to use the GPS reception sensitivity information in the user terminal device 200 or the distance calculated by the GPS or the acceleration sensor. You may choose whether to use the GPS information based on the information or the like.

In addition, a method of calculating stride information, which is biological data, from measurement data will be exemplified. For example, a known calculation method or the like from the waveform data of acceleration data measured by the measuring device 300 worn by the user on the wrist may be used alone or in combination (for example, averaging or weighting). For example, when walking, the hand is waved like a pendulum, so that the information of the above-mentioned acceleration sensor (for example, the timing at which the acceleration component in the traveling direction is the smallest or the timing at which the acceleration component is switched in the opposite direction, or the progress) can be obtained. Since the interval of one step (that is, pitch information) can be determined based on the timing when the acceleration component in the direction perpendicular to the direction is the smallest or the timing when the top and bottom are switched, the stride information can be obtained by further using the time information. Can be done. Alternatively, for example, when the ground is kicked out, the acceleration component in the kicking direction is synthesized, so that it is also possible to determine the interval of one step at the timing of occurrence of the acceleration component in the direction.

Further, exemplifying a method of calculating activity amount information which is biological data from measurement data, for example, frequency analysis of acceleration data measured by a measuring device 300 which is worn on a daily basis, for example, high and low frequencies are activity frequencies. It is possible to obtain activity amount information by calculating according to a predetermined condition such as what percentage of the day the activity is associated with high and low and the activity of a predetermined frequency or more occupies.

Further, exemplifying a method of calculating momentum information which is biological data from measurement data, for example, exercise including walking by a known calculation method from acceleration data measured by a measuring device 300 which is worn on a daily basis. Since the acceleration data at the time of being can be specified, the momentum information can be obtained by calculating under predetermined conditions using, for example, frequency analysis. Further, by further using additional information such as angular velocity information and tag information, it is possible to obtain more accurate momentum information.

Further, exemplifying the method of calculating the exercise load amount information which is the biological data from the measurement data, for example, the activity amount information and the exercise amount information derived from the acceleration data measured by the measuring device 300 which is worn on a daily basis. On the other hand, for example, exercise load amount information can be obtained by weighting with heartbeat information that increases with exercise load. Further, for example, if the vector information of the acceleration data is added, the state information such as the walking environment (slope, stairs, etc.) and the posture (standing position, sitting position, etc.) can be specified, so that the state information may be further used. Further, by further using additional information such as angular velocity information and tag information, it is possible to obtain more accurate exercise load amount information.

In addition, exercise amount information and exercise calculated based on the distance information calculated by the above-mentioned GPS and the acceleration sensor, the above-mentioned stride information and the step count information (for example, can be obtained by a known method based on the information from the acceleration sensor). Intensity information and exercise load information may also be calculated as biometric data.

Further, exemplifying a method of calculating renal function information (for example, the degree of deterioration of renal function) which is biological data from the measured data, for example, blood pressure transition information for a predetermined time (preferably 24 hours) from the above-mentioned blood pressure information. The reference blood pressure, which is the upper limit of normal blood pressure, is obtained by obtaining a value exceeding the reference blood pressure information, which is the upper limit of normal blood pressure set by a doctor or an external organization such as 130 mmHg or 140 mmHg, and an integrated value of time. A so-called Hyperbaric Index, which calculates "how much the information is exceeded" and "how long the excess occurs", may be used. Then, the daily blood pressure load information is calculated, the load on the kidney is estimated from the blood pressure load information, and the degree of deterioration of renal function is related to the relative amount. Renal function information can be obtained. More specifically, for example, by storing the correspondence information between the above integrated value and the state of renal function, the renal function information corresponding to the integrated value is determined with reference to the corresponding relation information (for example). It can be said that the larger the integrated value, the higher the degree of deterioration of renal function). The degree of deterioration of renal function may be expressed by an arbitrary parameter, may be a predetermined numerical value, or may have a rank (for example, A to F) defined for each integrated value in a predetermined range. .. Normally, it is carried out by the ABPM method using a cuff type sphygmomanometer to calculate Hyperbaric Index, but it is practically routine to measure for 24 hours with a cuff type sphygmomanometer because it is a compression type. Although it is difficult to measure, a measuring device having the above-mentioned blood pressure information acquisition method (for example, a wearable measuring device) can measure for 24 hours without compression. Then, it is possible to measure minute changes in the Hyperbaric Index value due to the availability of blood pressure per beat.

Here, as described above, for example, when Hyperbaric Index is used, the measuring device of this information processing system is compared with the case where an inspection period is provided and a cuff type (that is, compression type) is used as in the case of measurement by the conventional ABPM method. By using (especially non-compression type), the user can measure through daily life without being aware of the examination, but on the other hand, the user lives so as to maintain a resting state as at the time of the examination. You will need to get it. Therefore, for example, when the inspection mode is started by operating the user terminal device 200 or the measuring device 300, the exercise information such as the exercise amount information and the exercise intensity information is compared with the reference exercise information, and the excessive excess of the reference exercise information is exceeded. When the exercise is determined, the user support data generation unit 133 informs the user terminal device 200 and the measuring device 300 of the user support data related to the exercise (notification of excessive exercise or alert notification of not satisfying the inspection standard). (Notification data such as) may be generated.

In addition, instead of or in addition to the above, when excessive exercise is determined, the blood pressure information measured at that time is deleted or not linked to the test to perform thinning correction. May be good. Then, the approximate curve of the time transition is calculated from the blood pressure information after the thinning correction within an arbitrary predetermined time, and the integrated value for the portion of the approximate curve exceeding the reference blood pressure information is calculated to correct the blood pressure load. Information (for example, one day's worth) may be generated. If necessary, the approximate curve may be shown by a graph, and both graphs may be generated so that the blood pressure transition information before and after the thinning correction can be compared. As a result, the user can live a daily life without being aware of the inspection. The inspection mode is not limited to the explicit mode, and may be implicitly executed at any predetermined timing on the system.

The user support data generation unit 133 generates user support data including advice and alerts based on the above-mentioned renal function information (for example, the degree of deterioration of renal function). The reference blood pressure information may be stored in the biological data DB 122 in advance, and may be stored before the start of measurement. In addition, user support data may include alert notifications as a risk of chronic kidney disease, or advice notifications (for example, lifestyle-related changes), especially when it is determined that the degree of deterioration of renal function is not good. It includes advice notifications such as urging hospital visits), and is associated with user information and stored in the user support data DB 123. Furthermore, multiple types of content information such as advice notifications and alert notifications of user support data may be prepared based on the degree of deterioration of renal function, and the contents are set to be different for each of the above ranks, for example. You may.

The user support data generation unit 133 is, for example, based on the blood pressure information generated in a predetermined cycle (for example, a cycle shorter than the above-mentioned predetermined time) from the measurement data by the measuring device 300 which is always worn, and the user's blood pressure transition data. To generate. Then, for example, an abnormal value such as a sudden rise or a sudden drop in blood pressure is monitored, and when the abnormal value occurs, user support data including an alert notification is generated. The user support data generation unit 133 may generate blood pressure transition data before and after an abnormal value as user support data in addition to the alert. Further, when the abnormality is detected, the control unit 130 links the user information such as a medical institution or a close relative directly from the management server 100 or indirectly via the user terminal device 200 or the measuring device 300. It may be controlled to send a notification indicating an abnormality (for example, a notification via a predetermined application stored in a device such as a PC or a smartphone, a notification using an e-mail address, etc.) to the attached contact. .. This makes it possible to detect blood pressure abnormalities at an early stage, and even if the user is unable to contact due to a sudden onset, it is possible to automatically notify others of blood pressure abnormalities. The time of the above-mentioned predetermined cycle may be set to be the same as or substantially the same as the predetermined time for acquiring renal function information, and the blood pressure value and blood pressure within the predetermined time are set at the timing of estimating the degree of deterioration of the renal function. Abnormal information (for example, abnormal) that shows a difference of a predetermined value or more compared to the reference value or the user's past average value by acquiring the maximum value of the slope of the transition waveform (maximum value of the change in blood pressure transition). The determined blood pressure value and onset time) may be incidentally determined.

The data output unit 134 outputs biological data and user support data to the user terminal device 200. In the user terminal device 200, the output data may be displayed on the screen via, for example, a dedicated application so that the user can easily confirm the data.

The input / output unit 140 is an information input device such as a keyboard and a mouse, and an output device such as a display.

<Processing flow>
The processing flow of the data support method executed by the information processing system 1 will be described with reference to FIG. FIG. 6 is a flowchart showing an example of processing of the information processing system 1 of FIG.

As a process of step S101, the data management unit 131 generates account information for each user who uses the measuring device 300, and acquires predetermined user information from the user terminal device 200 or the like. The registered user information is stored in the user information DB 124 by the data management unit 131. The process of step S101 may be performed as a pre-process for the user to use the measuring device 300, or may be performed when the user uses the measuring device 300 for the first time.

When the user uses the measuring device 300 as the process of step S102, the measurement data is transmitted from the measuring device 300 to the management server 100 via the user terminal device 200 at predetermined intervals, and is received via the communication unit 110. .. The data management unit 131 stores the measurement data associated with the user information in the measurement data DB 121 of the storage unit 120.

As the process of step S103, the measurement data is read by the biometric data generation unit 132, and the biometric data is generated by a predetermined operation or the like. The generated biometric data is stored in the biometric data DB 122 by the data management unit 131.

As the process of step S104, the biometric data is read by the user support data generation unit 133, and the user support data is generated by a predetermined calculation or the like. The generated user support data is stored in the user support data DB 123 by the data management unit 131.

As the process of step S105, the biometric data and / or the user support data is read by the data output unit 134 and output to the user terminal device 200.

<Effect>
As described above, the information processing system according to the present embodiment generates user support data for indicating the state of the renal function of the user, in particular, based on the measurement data via the user support data generation unit 133. As a result, for example, the user can easily obtain an alert for early detection of chronic kidney disease with few initial symptoms, and the risk of chronic kidney disease can be reduced.

Although the embodiments related to the disclosure have been described above, these can be implemented in various other embodiments, and can be implemented by making various omissions, substitutions, and changes. These embodiments and variations as well as those with omissions, substitutions and modifications are included in the technical scope of the claims and the equivalent scope thereof.

1 Information processing system 100 Management server 200 User terminal device 300 Measuring device NW network

Claims (9)

An information processing system that receives measurement data from a measurement device worn by a user via a network at a predetermined cycle and generates renal function information from the measurement data.
A biological data generation unit that executes a predetermined calculation on the measurement data, generates blood pressure information and exercise information, and generates kidney function information of the user based on the blood pressure information and exercise information.
An information processing system characterized by this. Further, it is provided with a user support data generation unit that generates user support data related to exercise according to the result of comparing the exercise information and the reference exercise information.
The information processing system according to claim 1. The biological data generation unit generates blood pressure transition information for a predetermined time from the blood pressure information, calculates blood pressure load information by obtaining an integrated value of a value exceeding the reference blood pressure information and time, and obtains blood pressure load information from the blood pressure load information. It generates the renal function information and generates the above-mentioned renal function information.
The blood pressure information is blood pressure information as a result of performing thinning correction on the blood pressure information according to the result of comparing the exercise information and the reference exercise information.
The information processing system according to claim 1 or 2, wherein the information processing system is characterized by the above. The biological data generation unit stores the correspondence information between the integrated value and the state of the renal function, and generates the renal function information corresponding to the integrated value with reference to the correspondence information.
The information processing system according to claim 3, wherein the information processing system is characterized by the above. The predetermined cycle is the same as or substantially the same as the predetermined time for generating the blood pressure transition information.
The biological data generation unit acquires the maximum value of blood pressure information within the predetermined time or the maximum value of change in blood pressure transition information, and makes a difference of a predetermined value or more as compared with a reference value or a past average value of a user. Judging the outlier information that appears,
The information processing system according to claim 3 or 4. Further, it comprises a user support data generation unit that generates user support data including at least one of advice or alert based on the renal function information.
The information processing system according to claim 1 to claim 5, wherein the information processing system is characterized by the above. A server that receives measurement data from a measurement device worn by a user via a network at a predetermined cycle and generates renal function information from the measurement data.
A biological data generation unit that executes a predetermined calculation on the measurement data, generates blood pressure information and exercise information, and generates kidney function information of the user based on the blood pressure information and exercise information.
A server that features that. It is an information processing method that receives measurement data from a measuring device worn by a user via a network at a predetermined cycle and generates renal function information from the measured data.
The biological data generation unit includes a step of executing a predetermined calculation on the measurement data, generating blood pressure information and exercise information, and generating kidney function information of the user based on the blood pressure information and exercise information.
An information processing method characterized by that. It is a program for a computer that receives measurement data from a measuring device worn by a user via a network at a predetermined cycle and generates renal function information from the measured data.
The program is installed in the computer.
A functional unit including a biological data generation unit that executes a predetermined calculation on the measurement data, generates blood pressure information and exercise information, and generates renal function information of the user based on the blood pressure information and exercise information is realized. do,
A program characterized by that.

Images