JP7033363B1 - Information processing system, server, information processing method and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To generate said biological data including at least behavioral information based on measurement data, compare the behavioral information with blood glucose level spike condition information, and perform behavior leading to blood glucose level spike corresponding to the condition according to the result of the comparison. Provide an information processing system or the like that generates notifications that reduce the number of data.
An information processing system according to the present embodiment receives measurement data from a measurement device worn by a user via a network at a predetermined cycle, and generates biometric data and a predetermined notification from the measurement data. In the processing system, the biometric data generation unit that generates the biometric data including at least the behavioral information based on the measurement data and the behavioral information are compared with the blood glucose level spike condition information, and the conditions are determined according to the result of the comparison. It is provided with a notification generation unit that generates a notification that reduces the behavior leading to the blood glucose level spike corresponding to the above.
[Selection diagram] Fig. 1

Description

The present disclosure relates to an information processing system, a server, an information processing method and a program that generate a notification that reduces an action leading to a blood glucose spike of a measurement target user based on the blood glucose level information acquired from the measuring device.

Conventionally, blood glucose level is measured by an invasive measurement method that collects blood from the user, but since blood sampling is involved, it is a burden on the user to measure blood glucose level on a daily basis and grasp the health condition. It was big.

Therefore, a method for measuring the blood glucose level in a non-invasive manner is being studied. For example, Patent Document 1 and Patent Document 2 disclose a method of calculating a blood glucose level based on pulse wave information measured by a user.

Japanese Patent No. 6851664 Japanese Patent No. 6544751

However, simply measuring the blood glucose level is not sufficient for the benefit to be obtained by the user, and in particular, the enormous amount of time-dependent blood glucose level information acquired by a measuring device (for example, a so-called wearable device) that is worn on a daily basis is effective. Further studies are required on how to use it.

Therefore, in the present disclosure, based on the measurement data acquired from the measuring device, the biological data including at least the behavioral information is generated, the behavioral information is compared with the blood glucose level spike condition information, and the conditions are set according to the result of the comparison. Describe information processing systems, servers, information processing methods and programs that generate notifications that reduce the behavior that leads to the relevant blood glucose spike.

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 biometric data and a predetermined notification from the measured data. Therefore, based on the measurement data, the biometric data generation unit that generates the biometric data including at least the behavioral information and the behavioral information are compared with the blood glucose level spike condition information, and the conditions are met according to the result of the comparison. It includes a notification generator that generates notifications that reduce behaviors that lead to blood glucose spikes.

According to the present disclosure, it is possible to generate notifications that reduce behaviors that lead to blood glucose spikes. As a result, the user can appropriately grasp his / her behavior, 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 wave which concerns on this embodiment. 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 measuring device worn by a user via a network at a predetermined cycle and generates biometric data and a predetermined notification from the measured data.
A biometric data generation unit that generates the biometric data including at least behavioral information based on the measurement data.
It is provided with a notification generation unit that compares the behavior information with the blood glucose spike condition information and generates a notification that reduces the behavior leading to the blood glucose spike corresponding to the condition according to the result of the comparison.
An information processing system characterized by this.
[Item 2]
Further comprising a data output unit for transmitting a notification to the user terminal device of the user to reduce the behavior leading to the blood glucose spike.
The information processing system according to item 1, wherein the information processing system is characterized by the above.
[Item 3]
Further comprising a data output unit for transmitting a notification to reduce the behavior leading to the blood glucose spike to the user terminal device of the person associated with the user.
The information processing system according to any one of items 1 or 2, wherein the information processing system is characterized by the above.
[Item 4]
A server that receives measurement data from a measuring device worn by a user via a network at a predetermined cycle and generates biometric data and a predetermined notification from the measured data.
A biometric data generation unit that generates the biometric data including at least behavioral information based on the measurement data.
It is provided with a notification generation unit that compares the behavior information with the blood glucose spike condition information and generates a notification that reduces the behavior leading to the blood glucose spike corresponding to the condition according to the result of the comparison.
A server that features that.
[Item 5]
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 biometric data and a predetermined notification from the measured data.
A step of generating the biometric data including at least behavioral information based on the measurement data by the biometric data generation unit.
The notification generation unit includes a step of comparing the behavior information with the blood glucose spike condition information and generating a notification that reduces the behavior leading to the blood glucose spike corresponding to the condition according to the result of the comparison.
An information processing method characterized by that.
[Item 6]
It is a program for executing an information processing method on a computer that receives measurement data from a measuring device worn by a user via a network at a predetermined cycle and generates biometric data and a predetermined notification from the measured data.
The information processing method is
A step of generating the biometric data including at least behavioral information based on the measurement data by the biometric data generation unit.
The notification generation unit includes a step of comparing the behavior information with the blood glucose spike condition information and generating a notification that reduces the behavior leading to the blood glucose spike corresponding to the condition according to the result of the comparison.
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.

<Structure>
FIG. 1 is a block configuration diagram showing an information processing system 1 according to an embodiment of the present disclosure. The information information system 1 receives, for example, user's measurement data from a measurement device 300 via a network NW at a management server 100 at a predetermined periodic timing, and performs a predetermined calculation on the measurement data. Is a system that generates biometric data and generates a predetermined notification 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), a BLE (Bluetooth Low Energy), 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 a user terminal device 200 at a predetermined periodic timing and calculates the measurement data into biometric data. Yes, for example, it is configured by a server device that provides various Web services.

The user terminal device 200 is an information processing device possessed by a predetermined user, for example, a personal computer, a tablet terminal, a smartphone, a smart watch, a mobile phone, a PHS, a PDA, etc. It is used to display data by a waveform graph or the like, or to display a predetermined notification (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.

Further, the user terminal device 200 may use a touch panel or the like to input a state in which the user acquires data by the measuring device 300. The user terminal device 200 can input the "state for acquiring data" as tag information such as "running" when running and "mealing" when eating. In this case, the user terminal device 200 transmits the measurement data received from the measuring device 300 at a predetermined periodic timing to the management server 100 via the network NW in association with the tag information.

The measuring device 300 is a device that measures the biometric data of the user, and is a so-called wearable device that the user wears on the body such as his / her wrist or arm. The measuring device 300 is a device for measuring data of a user's electrocardiogram, pulse wave, skin temperature (body temperature), acceleration, and angular velocity at a predetermined periodic timing by a known method. The predetermined cycle may be preset or may be arbitrarily set by the user. More specifically, for example, a time cycle 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 integrated devices, for example, the measuring device 300 may be equipped with a SIM to have a communication function, or a management server may be provided with BLE (Bluetooth Low Energy) or the like. It may be configured to be able to communicate directly with 100.

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 notification information DB 123 to store various notifications transmitted to the user terminal device 200, and the user information DB 124 to store the user information including the user identification number are stored. 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 notification 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 and biometric 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 between the measurement data and the biological data by such tag information is not limited to the time information, but the physical information and the behavior information indicating the physical condition state and the behavior state of the user can be freely entered and stored as the tag information. It is also good to let them choose from the prescribed options (for example, in response to the question "How are you feeling now?", Choose one of "1: good, 2: normal, 3: bad", etc.). , You may try to remember the selected answer. Alternatively, when the user takes a predetermined action (for example, sitting, standing, walking, running, sleeping, eating, driving, resting, etc.), the user records it as tag information of measurement data or biometric data (for example,). It may be a choice format or a free description). Furthermore, instead of or in combination with the above-mentioned user recording, for example, measurement data and biometric data (for example, walking speed information, stride information, motion information of the wearing site, posture information, position information of the center of gravity, heartbeat information, etc. ), And tags biometric data etc. by estimating predetermined behavior (for example, sitting, standing, lying down, walking, running, sleeping, waking up, sleeping, eating, driving, resting, etc.) by a known method. It may be associated as information (behavioral information). At this time, for example, the learning model may be estimated by a learning model learned based on the measurement data for teachers, or the learning model may be personalized by performing additional learning based on the result of the above-mentioned recording by the user. 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 control unit 150 itself is used as behavioral information. It becomes possible.

Further, for example, the data management unit 131 can manage data based on the tag information, such as sorting the data D1 in the time order of the tag T1 or extracting only predetermined physical information and behavior information.

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 glucose level information, blood pressure information, heartbeat information, blood oxygen level information, maximum oxygen intake information, and the like. Electrocardiographic information, respiratory rate, body temperature information, step count information, stride information, center of gravity position information, posture 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 known medical equipment, etc.) A learning model is created in advance based on the teacher data associated with the correspondence (for example, information indicating the degree and range of the error may be included) with the blood pressure information, etc., and the biological data generation unit 132. May generate biometric data using the determination using the learning model as the above-mentioned predetermined calculation (analysis).

Here, the generation of blood pressure information will be described in detail. The biological data generation unit 132 includes a pulse wave propagation time calculation unit 1321. The biological data generation unit 132 calculates second-order differential data from photoelectric volumetric pulse waveform data (for example, pulse waveform data in FIG. 5). The pulse wave propagation time calculation unit 1321 detects R wave and T wave from the waveform profile in the electrocardiographic information described later, and also detects P wave and D wave from the waveform profile in the photoelectric type volumetric pulse waveform data. Based on the information, the ventricular systolic pulse wave propagation time PTT_SYS and the ventricular diastolic pulse wave propagation time PTT_DIA are calculated. Further, the biological data generation unit 132 obtains blood pressure information from the acceleration pulse wave characteristic information based on the second-order differential data to be calculated later, the ventricular systolic pulse wave propagation time PTT_SYS, and the ventricular diastolic pulse wave propagation time PTT_DIA. Perform the calculation. The pulse wave propagation time calculation unit 1321 may use the first-order differential data or the second-order differential data of the photoelectric positive volume pulse waveform data in detecting the R wave and the T wave.

Further, the biometric data generation unit 132 brings, for example, two electrodes provided in the measuring device 300 worn by the person to be measured in contact with the skin, and the electrocardiographic waveform data measured from the time change of the difference in the detected potential. Heartbeat information can be obtained as biometric information (biologically generated information) from the intervals of QRS waves in (for example, the electrocardiographic waveform data of FIG. 5).

FIG. 5 shows the electrocardiographic waveform and the photoelectric volume pulse waveform of the non-measurer measured by the measuring device 300, and the velocity pulse waveform and the acceleration pulse waveform calculated by the management server 100. From the top of FIG. 5, the electrocardiographic waveform, the photoelectric volumetric pulse waveform, the velocity pulse waveform, and the acceleration pulse waveform are arranged in this order. 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. With 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 Dictric 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, elastin, collagen, and smooth muscle. Each of these components has 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 due to the influence of age, gender, and environmental variables (temperature, etc.). 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, as shown by Eqs. (1) and (2), from the time Tr of the R wave and the time Tt of the T wave of the electrocardiographic waveform, and the time Tp of the T wave and the time Td of the D wave of the photoelectric volume pulse waveform. In addition, the ventricular systolic pulse wave propagation time PTT_SYS and the ventricular diastolic pulse wave propagation time PTT_DIA can be calculated.

PTT_SYS = Tp-Tr ... (1)

PTT_DIA = Td-Tt ... (2)

The first and second electrodes for measuring the electrocardiographic waveform and the optical sensor module for measuring the photoelectric volumetric pulse waveform are opposed to each other via the wrist, and the arrangement distance is separated from the detection site of the electrocardiographic waveform. The measurement site of the formula volume pulse waveform will be separated. Therefore, by causing a time lag in which each characteristic waveform is generated, the absolute calculation time of the ventricular systolic pulse wave propagation time PTT_SYS and the ventricular diastolic pulse wave propagation time PTT_DIA can be lengthened. Therefore, when the change information of the ventricular systolic pulse wave propagation time PTT_SYS and the ventricular diastolic pulse wave propagation time PTT_DIA is obtained, the accuracy of the change information can be improved.

Here, the formula for calculating blood pressure will be described.

From the following equation (3) pulse wave velocity equation (Moens-Korteweg equation), the relationship between the pulse wave velocity and the Young's modulus of the arterial wall is shown.

L / T_PTT = √ (E ・ h / (2 ・ r ・ ρ)) ・ ・ ・ (3)

Each parameter of the formula (3) is L: distance between measurements, T_PTT: pulse wave propagation time, r: blood vessel inner diameter, E: blood vessel Young's modulus, h: blood vessel thickness, ρ: blood density.

It is known that Young's modulus and blood pressure value are correlated.

E = E ₀・ exp (α ・ P) ・ ・ ・ (4)

Can be indicated by. Here, P: blood pressure value, α: constant, E ₀ : initial value.

From equations (3) and (4)

P = (-2 ・ ln (T_PTT) + ln (2 ・ r ・ ρ ・ L ² / (E ₀・ h))) / α ・ ・ ・ (5)

Can be derived. ln indicates the natural logarithm. At this time, since "r · ρ" is proportional to the blood volume at the measurement site, it can be indicated by a high value (Vp, Vd) indicated by the photoelectric volume pulse waveform. Further, since "E ₀ · h" is a value proportional to the elasticity of the blood vessel, it can be replaced by using the parameters (b / a) and (f / e) indicating the elasticity.

Therefore, the systolic blood pressure BP_SYS (Blood Pressure_Systic) and the diastolic blood pressure BP_DIA (Blood Pressure_Diastolic) can be represented by the following equations (6) and (7).

BP_SYS = A1 · ln (PTT_SYS) + A2 · ln (Vp) + A3 · ln (b / a) + A4 ... (6)

BP_DIA = A5 · ln (PTT_DIA) + A6 · ln (Vd) + A7 · ln (f / e) + A8 ... (7)

A1 to A8 are constants determined by conditions. The systolic blood pressure BP_SYS that can be calculated by Eq. (6) is the natural logarithm of the ventricular systolic pulse wave propagation time PTT_SYS multiplied by the constant A1 and the natural logarithm of the intensity Vp of the P wave multiplied by the constant A2. It can be obtained by multiplying the natural logarithm of (b / a) by the constant A3 and the sum of the constants A4. The diastolic blood pressure BP_SYS that can be calculated by Eq. (7) is the natural logarithm of the ventricular systolic pulse wave propagation time PTT_DIA multiplied by the constant A5 and the natural logarithm of the intensity Vd of the D wave multiplied by the constant A6. It can be obtained by multiplying the natural logarithm of (f / e) by the constant A7 and the sum of the constant A8. It is possible to obtain the systolic blood pressure BP_SYS and the diastolic blood pressure BP_DIA by obtaining each constant depending on the characteristics of the device and the person to be measured. However, when confirming the changes in the systolic blood pressure BP_SYS and the diastolic blood pressure BP_DIA, it is not necessary to determine all the constants, and while substituting the provisional numerical values, the information on the systolic blood pressure BP_SYS and the information on the diastolic blood pressure BP_DIA are used. It is possible to get a value. The natural logarithm of the intensity Vp of the P wave and the natural logarithm of the intensity Vd of the D wave are terms in consideration of the influence of blood density. Further, the natural logarithm of (b / a) and the natural logarithm of (f / e) are terms in consideration of the influence of the Young's modulus of the arterial wall. Therefore, depending on the measurement conditions, information on the systolic blood pressure BP_SYS and information on the diastolic blood pressure BP_DIA may be calculated by selecting one of the terms and making the other term constant.

Further, the biological data generation unit 132 obtains temperature information from the skin temperature information of the person to be measured measured by, for example, a temperature sensor (thermistor or the like) of the measuring device 300 worn by the person to be measured. Can be obtained as.

Further, the biological data generation unit 132 uses, for example, a calculation method known from the waveform data of the acceleration data measured by the measuring device 300 worn on the wrist of the person to be measured alone or in combination (for example). Walking speed information can be obtained as biometric information (biologically generated information) by averaging, weighting, etc., for example, walking speed information biometric information (biologically generated information) by integrating acceleration data at predetermined time intervals. ) Can be obtained.

Further, the biological data generation unit 132 uses, for example, a calculation method known from the waveform data of acceleration data measured by the measuring device 300 worn on the wrist of the person to be measured alone or in combination (for example). Stride information can be obtained as biometric information (biologically generated information) by averaging, weighting, etc., and for example, since the hand is waved like a pendulum when walking, the above-mentioned accelerometer information (for example,). The interval of one step is determined based on the timing when the acceleration component in the traveling direction is the smallest or the timing when the acceleration component is switched in the opposite direction, the timing when the acceleration component in the direction perpendicular to the traveling direction is the smallest, or the timing when the acceleration component is switched up and down, etc.) Therefore, if time information is further used, stride information can be obtained as biometric information (biologically generated information). 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, in the biological data generation unit 132, for example, from the acceleration data and the angular velocity data measured by the measuring device 300 worn by the person to be measured, a portion (for example, a wrist or ankle) on which the measuring device 300 is worn is determined. It is possible to obtain motion information as biometric information (biologically generated information), such as how fast and at what angle it is moving.

Further, the biological data generation unit 132 frequency-analyzes acceleration data measured by, for example, a measuring device 300 that is worn on a daily basis, and for example, high and low frequencies are associated with high and low activity frequencies, and activities of a predetermined frequency or higher are performed. The activity amount information can be obtained as biometric information (biologically generated information) by calculating according to a predetermined condition such as what percentage of the day is occupied.

Further, since the biological data generation unit 132 can specify the acceleration data during exercise including walking by a known calculation method or the like from the acceleration data measured by the measuring device 300 which is worn on a daily basis, for example. For example, momentum information can be obtained as biometric information (biologically generated information) by calculating under predetermined conditions using frequency analysis or the like. Further, by further using additional information such as angular velocity information, it is possible to obtain more accurate momentum information.

Further, the biological data generation unit 132 uses, for example, heartbeat information that increases with an exercise load with respect to the activity amount information and the exercise amount information derived from the acceleration data measured by the measuring device 300 that is worn on a daily basis. By weighting, exercise load information can be obtained as biometric information (biologically generated information). 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, it is possible to obtain more accurate exercise load amount information.

Further, the biological data generation unit 132 uses, for example, heart rate information to obtain maximum oxygen uptake information by a known mathematical formula such as VO ₂ max = 15 × (220-age) ÷ heart rate (particularly resting heart rate). It can be obtained as biometric information (biologically generated information).

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

Here, it is said that the blood glucose spike, which is a state in which the blood glucose level rises and falls sharply after a meal, is usually a normal blood glucose level, but if it occurs frequently, the risk of arteriosclerosis increases. However, it is possible that the user has unknowingly taken actions that lead to a blood glucose spike, and it is desirable that these actions be notified to the user at an early stage.

Therefore, in the present embodiment, the notification generation unit 133 uses the behavior information generated based on the measurement data received at a predetermined periodic timing by the measurement device 300 worn by the user on a daily basis as the blood glucose level spike condition. Compare with the information, and depending on the result of the comparison, notify that the behavior that leads to the blood glucose spike corresponding to the condition is reduced (notice that the behavior itself is suppressed and what kind of action should be taken) Generate advice notifications, etc.). The generation of the notification referred to here includes determining which notification is to be output data, and among various notifications of the notification information DB 123, for example, what kind of notification is to be output data according to the conditions described later. Including deciding from. The details will be described below.

The blood pressure spike condition information includes, for example, whether or not the patient sits for a predetermined time without getting up from the chair after eating (behavioral condition after eating), and sleeps for a predetermined time or less (for example, 6 hours or less) at a predetermined frequency (for example, three times a week). Whether or not (sleep condition), whether at least one of the maximum blood pressure and the minimum blood pressure is higher than the predetermined value (blood pressure condition), whether or not breakfast is taken (breakfast condition), without eating early Information indicating conditions such as whether you are eating slowly (meal period conditions) or eating low GI foods (GI conditions), and the standard value for each condition is a comparison target with the corresponding behavioral information and biometric data. Is set as. As described above, the behavior information is, for example, measurement data or biometric data (for example, walking speed information, stride information, motion information of the wearing site, posture information, position information of the center of gravity, heartbeat information, etc. Predetermined behavior (eg, sitting, standing, lying down, walking, running, sleeping, waking up, sleeping, eating, driving, resting, etc.) is estimated by known methods based on the motion information of the wearing site). This may be done, and more specifically, it may be estimated by a learning model learned based on the measurement data for teachers and the biological data.

For example, in the post-meal behavior condition, the notification generation unit 133 generates meal period information indicating the period during which the user was eating, based on the period information in which the behavior information indicating "meal" was determined, and thereafter. It may be determined whether behavioral information (for example, standing, walking, running, activity information, etc.) indicating some exercise state is started within a predetermined period during the period. Then, based on the determination result, notification information that encourages exercise may be generated as a notification that reduces the behavior that leads to the blood glucose level spike.

For example, in the sleep condition, the notification generation unit 133 generates sleep period information indicating the period during which the user has been sleeping based on the period information in which the behavior information indicating "sleep" is determined, and the sleep period information is predetermined. It may be determined whether or not the condition (whether or not there is sleep of 6 hours or less three times a week) is satisfied. Then, based on the determination result, the notification information that promotes sleep to the extent that the condition is satisfied may be generated as a notification that reduces the behavior leading to the blood glucose level spike.

For example, in the breakfast condition, the notification generation unit 133 generates meal period information indicating the period during which the user was eating based on the period information in which the action information indicating "meal" was determined, and the meal period information is generated. It may be determined whether it is confirmed at a predetermined time zone indicating the morning. Then, based on the determination result, notification information prompting to have breakfast may be generated as a notification for reducing the behavior leading to the blood glucose level spike.

For example, in the meal period condition, the notification generation unit 133 may generate meal period information indicating the period during which the user was eating, based on the period information in which the action information indicating "meal" was determined. Then, for example, by configuring the application to show one or more meal period information to the user, it is possible to objectively grasp the tendency regarding one's own meal time, and further, each meal period information and standard are obtained by the notification generation unit 133. Compared with the meal period, the meal period trend information (for example, "early meal" when the meal period is shorter than the first standard meal period, and "just" when the meal period is longer than the first standard meal period. "Good", longer than the first standard meal period, "late meal" if the meal period is longer than the second standard meal period, etc.), that is, the meal by generating the meal period trend information. It is judged whether or not the period condition is met. Then, based on the judgment result, notification information regarding the change of the meal period corresponding to the meal period trend information (for example, alert information such as "Be careful not to eat early" or advice such as "Let's chew well" Information, etc.) may be generated as notifications that reduce behaviors that lead to blood glucose spikes.

For example, under the GI condition, it is known that the higher the GI (glycemic index) value is, the larger the increase amount is with respect to the upward tendency of the blood glucose level. By memorizing the correspondence with the amount), the notification generation unit 133 can determine how much GI value may be ingested in the blood glucose level information associated with the behavioral information indicating "meal". By estimating, it may be determined whether or not the GI value condition is satisfied.

The notification generation unit 133 generates blood glucose spike information from the blood glucose information according to a predetermined spike condition. The spike condition is, for example, that the blood glucose spike is usually a normal blood glucose level, but the blood glucose level rises significantly after a meal, and the peak value that returns from rising to falling is a predetermined time after a meal (for example, about 1 to 2). Focusing on the fact that it appears after an hour () and then drops significantly after a predetermined time (for example, about 1 to 2 hours later), for example, in the spike condition, the blood glucose level information has a predetermined reference value (for example, 140 to 140). The blood glucose spike information may include information on the number of times it is detected that the condition is met, and further exceeds the reference value. It may also include time information at the time. In addition to this condition, in order to eliminate noise, the blood glucose level information may exceed a predetermined reference value and fall below the reference value again after a lapse of a predetermined period. In addition, in the case of a person with a high blood glucose level such as a diabetic patient, there is a risk of erroneous judgment based only on the reference value, so the average value for a predetermined period (for example, one day, several days, one week, etc.) and blood glucose level information. And the magnitude of the relative difference value to the average value information such as the average value of the sleeping period calculated based on the behavior information and the average value of the predetermined period before meals exceeds the predetermined reference value (50 mg / dl, etc.). Or may be a condition that the gradient from the minimum value to the maximum value of the blood glucose level information for a predetermined period (considering the time information of each value) exceeds a predetermined reference value. Then, the number of blood glucose spikes indicated by the blood glucose spike information may be notified together with the above notification.

In this way, it is possible to notify notifications that reduce behaviors that lead to blood glucose spikes.

Note that the notification generation unit 133 may send a notification on behalf of or in addition to the user to a person associated with the user. The person associated with the user is, for example, associated with the user information and stores identification information such as account information in the user information DB 124 or the like, and is not particularly limited as long as it is another user, but for example, a parent, a child, or a relative. It may be a relative such as, a partner, a caregiver, an assistant, a doctor, or the like.

The data output unit 134 outputs measurement data, biometric data, and predetermined notification information 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 information processing 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 a predetermined cycle, 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 notification generation unit 133, and a predetermined notification (particularly a notification that reduces the behavior leading to the blood glucose level spike) is generated by a predetermined calculation or the like.

As the process of step S105, the biometric data and / or a predetermined notification 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 notifications that reduce behaviors that lead to blood glucose spikes. As a result, the user can appropriately grasp his / her behavior, and the convenience of the user is improved.

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 (12)

An information processing system that receives measurement data from a measuring device worn by a user on a wrist or arm via a network at a predetermined cycle, and generates biometric data and a predetermined notification from the measured data.
The behavior information estimated based on at least one of the motion information of at least one of the wrist or the arm, which is the mounting site of the measuring device, or at least one of the posture information is compared with the blood glucose spike condition information, and the result of the comparison is obtained. It is equipped with a notification generator that generates notifications that reduce behaviors that lead to blood glucose spikes that meet the conditions accordingly.
The blood glucose spike condition information includes post-meal behavioral conditions.
The notification generation unit generates meal period information indicating the period during which the user was eating based on the period information in which the behavior information indicating the meal was determined, and the behavior information indicating some exercise state within the subsequent predetermined period. Is generated, and based on the result of the judgment, a notification prompting exercise is generated.
An information processing system characterized by this. An information processing system that receives measurement data from a measuring device worn by a user on a wrist or arm via a network at a predetermined cycle, and generates biometric data and a predetermined notification from the measured data.
The behavior information estimated based on at least one of the motion information of at least one of the wrist or the arm, which is the mounting site of the measuring device, or at least one of the posture information is compared with the blood glucose spike condition information, and the result of the comparison is obtained. It is equipped with a notification generator that generates notifications that reduce behaviors that lead to blood glucose spikes that meet the conditions accordingly.
The blood glucose spike condition information includes sleep conditions.
The notification generation unit generates sleep period information indicating the period during which the user was sleeping based on the period information in which the behavior information indicating sleep is determined, and determines whether the sleep period information matches a predetermined condition. And, based on the result of the judgment, generate a notification to encourage sleep,
An information processing system characterized by this. An information processing system that receives measurement data from a measuring device worn by a user on a wrist or arm via a network at a predetermined cycle, and generates biometric data and a predetermined notification from the measured data.
The behavior information estimated based on at least one of the motion information of at least one of the wrist or the arm, which is the mounting site of the measuring device, or at least one of the posture information is compared with the blood glucose spike condition information, and the result of the comparison is obtained. It is equipped with a notification generator that generates notifications that reduce behaviors that lead to blood glucose spikes that meet the conditions accordingly.
The blood glucose spike condition information includes breakfast conditions.
The notification generation unit generates meal period information indicating the period during which the user was eating based on the period information in which the behavior information indicating the meal was determined, and the meal period information is set to a predetermined time zone indicating the morning. Determine if it is confirmed and generate a notification prompting you to have breakfast based on the result of the determination.
An information processing system characterized by this. A server that receives measurement data from a measuring device worn by a user on a wrist or arm via a network at a predetermined cycle, and generates biometric data and a predetermined notification from the measured data.
The behavior information estimated based on at least one of the motion information of at least one of the wrist or the arm, which is the mounting site of the measuring device, or at least one of the posture information is compared with the blood glucose spike condition information, and the result of the comparison is obtained. It is equipped with a notification generator that generates notifications that reduce behaviors that lead to blood glucose spikes that meet the conditions accordingly.
The blood glucose spike condition information includes post-meal behavioral conditions.
The notification generation unit generates meal period information indicating the period during which the user was eating based on the period information in which the behavior information indicating the meal was determined, and the behavior information indicating some exercise state within the subsequent predetermined period. Is generated, and based on the result of the judgment, a notification prompting exercise is generated.
A server that features that. A server that receives measurement data from a measuring device worn by a user on a wrist or arm via a network at a predetermined cycle, and generates biometric data and a predetermined notification from the measured data.
The behavior information estimated based on at least one of the motion information of at least one of the wrist or the arm, which is the mounting site of the measuring device, or at least one of the posture information is compared with the blood glucose spike condition information, and the result of the comparison is obtained. It is equipped with a notification generator that generates notifications that reduce behaviors that lead to blood glucose spikes that meet the conditions accordingly.
The blood glucose spike condition information includes sleep conditions.
The notification generation unit generates sleep period information indicating the period during which the user was sleeping based on the period information in which the behavior information indicating sleep is determined, and determines whether the sleep period information matches a predetermined condition. And, based on the result of the judgment, generate a notification to encourage sleep,
A server that features that. A server that receives measurement data from a measuring device worn by a user on a wrist or arm via a network at a predetermined cycle, and generates biometric data and a predetermined notification from the measured data.
The behavior information estimated based on at least one of the motion information of at least one of the wrist or the arm, which is the mounting site of the measuring device, or at least one of the posture information is compared with the blood glucose spike condition information, and the result of the comparison is obtained. It is equipped with a notification generator that generates notifications that reduce behaviors that lead to blood glucose spikes that meet the conditions accordingly.
The blood glucose spike condition information includes breakfast conditions.
The notification generation unit generates meal period information indicating the period during which the user was eating based on the period information in which the behavior information indicating the meal was determined, and the meal period information is set to a predetermined time zone indicating the morning. Determine if it is confirmed and generate a notification prompting you to have breakfast based on the result of the determination.
A server that features that. An information processing method in which measurement data is received from a measuring device worn by a user on a wrist or arm via a network at a predetermined cycle, and biometric data and a predetermined notification are generated from the measured data.
The notification generator compares at least the motion information of at least one of the wrists or arms, which is the mounting site of the measuring device, or the behavioral information estimated based on at least one of the posture information with the blood glucose spike condition information. Including , with the step of generating a notification that reduces the behavior leading to the blood glucose spike corresponding to the condition, depending on the result of the comparison.
The blood glucose spike condition information includes post-meal behavioral conditions.
The notification generation unit generates meal period information indicating the period during which the user was eating based on the period information in which the behavior information indicating the meal was determined, and the behavior information indicating some exercise state within the subsequent predetermined period. Is generated, and based on the result of the judgment, a notification prompting exercise is generated.
An information processing method characterized by that. An information processing method in which measurement data is received from a measuring device worn by a user on a wrist or arm via a network at a predetermined cycle, and biometric data and a predetermined notification are generated from the measured data.
The notification generator compares at least the motion information of at least one of the wrists or arms, which is the mounting site of the measuring device, or the behavioral information estimated based on at least one of the posture information with the blood glucose spike condition information. Including , with the step of generating a notification that reduces the behavior leading to the blood glucose spike corresponding to the condition, depending on the result of the comparison.
The blood glucose spike condition information includes sleep conditions.
The notification generation unit generates sleep period information indicating the period during which the user was sleeping based on the period information in which the behavior information indicating sleep is determined, and determines whether the sleep period information matches a predetermined condition. And, based on the result of the judgment, generate a notification to encourage sleep,
An information processing method characterized by that. An information processing method in which measurement data is received from a measuring device worn by a user on a wrist or arm via a network at a predetermined cycle, and biometric data and a predetermined notification are generated from the measured data.
The notification generator compares at least the motion information of at least one of the wrists or arms, which is the mounting site of the measuring device, or the behavioral information estimated based on at least one of the posture information with the blood glucose spike condition information. Including , with the step of generating a notification that reduces the behavior leading to the blood glucose spike corresponding to the condition, depending on the result of the comparison.
The blood glucose spike condition information includes breakfast conditions.
The notification generation unit generates meal period information indicating the period during which the user was eating based on the period information in which the behavior information indicating the meal was determined, and the meal period information is set to a predetermined time zone indicating the morning. Determine if it is confirmed and generate a notification prompting you to have breakfast based on the result of the determination.
An information processing method characterized by that. A program for executing an information processing method on a computer that receives measurement data from a measuring device worn by a user on a wrist or arm via a network at a predetermined cycle and generates biometric data and a predetermined notification from the measured data. And
The information processing method is
The notification generator compares at least the motion information of at least one of the wrists or arms, which is the mounting site of the measuring device, or the behavioral information estimated based on at least one of the posture information with the blood glucose spike condition information. Including , with the step of generating a notification that reduces the behavior leading to the blood glucose spike corresponding to the condition, depending on the result of the comparison.
The blood glucose spike condition information includes post-meal behavioral conditions.
The notification generation unit generates meal period information indicating the period during which the user was eating based on the period information in which the behavior information indicating the meal was determined, and the behavior information indicating some exercise state within the subsequent predetermined period. Is generated, and based on the result of the judgment, a notification prompting exercise is generated.
A program characterized by that. A program for executing an information processing method on a computer that receives measurement data from a measuring device worn by a user on a wrist or arm via a network at a predetermined cycle and generates biometric data and a predetermined notification from the measured data. And
The information processing method is
The notification generator compares at least the motion information of at least one of the wrists or arms, which is the mounting site of the measuring device, or the behavioral information estimated based on at least one of the posture information with the blood glucose spike condition information. Including , with the step of generating a notification that reduces the behavior leading to the blood glucose spike corresponding to the condition, depending on the result of the comparison.
The blood glucose spike condition information includes sleep conditions.
The notification generation unit generates sleep period information indicating the period during which the user was sleeping based on the period information in which the behavior information indicating sleep is determined, and determines whether the sleep period information matches a predetermined condition. And, based on the result of the judgment, generate a notification to encourage sleep,
A program characterized by that. A program for executing an information processing method on a computer that receives measurement data from a measuring device worn by a user on a wrist or arm via a network at a predetermined cycle and generates biometric data and a predetermined notification from the measured data. And
The information processing method is
The notification generator compares at least the motion information of at least one of the wrists or arms, which is the mounting site of the measuring device, or the behavioral information estimated based on at least one of the posture information with the blood glucose spike condition information. Including , with the step of generating a notification that reduces the behavior leading to the blood glucose spike corresponding to the condition, depending on the result of the comparison.
The blood glucose spike condition information includes breakfast conditions.
The notification generation unit generates meal period information indicating the period during which the user was eating based on the period information in which the behavior information indicating the meal was determined, and the meal period information is set to a predetermined time zone indicating the morning. Determine if it is confirmed and generate a notification prompting you to have breakfast based on the result of the determination.
A program characterized by that.

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