JP2023077368A - Information processing system, server, information processing method and program - Google Patents

Abstract

To provide an information processing system, etc. generating biological data including at least action information based on measurement data, comparing the action information with blood sugar spike condition information and generating notification for reducing actions leading to blood sugar spikes falling under the condition depending on a comparison result.SOLUTION: An information processing system for receiving measurement data from a measuring device worn by a user at prescribed intervals via a network and generating biological data and prescribed notification from the measurement data. The information processing system includes a biological data generation part for generating the biological data including at least action information based on the measurement data, and a notification generation part comparing the action information with blood sugar spike condition information and generating notification for reducing actions leading to blood sugar spikes falling under the condition depending on the comparison result.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 a notification for reducing behavior leading to blood sugar level spikes of a user to be measured based on blood sugar level information acquired from a measuring device.

Conventionally, blood sugar levels have been measured by an invasive measurement method in which blood is collected from the user. However, since blood sampling is involved, it is a burden on the user to measure the blood sugar level on a daily basis and grasp the health condition. It was big.

Therefore, methods for measuring blood sugar levels in a non-invasive manner are being investigated. For example, Patent Literature 1 and Patent Literature 2 disclose a method of calculating a blood sugar level based on pulse wave information measured from a user.

Japanese Patent No. 6851664 Japanese Patent No. 6544751

However, simply measuring the blood sugar level is not sufficient for the benefit that the user can obtain. Further study is required on how to use it.

Therefore, in the present disclosure, the biometric data including at least behavioral information is generated based on the measurement data acquired from the measuring device, the behavioral information is compared with the blood sugar level spike condition information, and the conditions are met according to the result of the comparison. An information processing system, a server, an information processing method, and a program that generate notifications for reducing behaviors leading to relevant blood sugar spikes are described.

An information processing system according to one aspect of the present disclosure is an information processing system that receives measurement data from a measurement device worn by a user via a network at predetermined intervals, and generates biological data and predetermined notifications from the measurement data. a biometric data generation unit that generates the biometric data including at least behavior information based on the measurement data; a notification generator for generating notifications that reduce behaviors that lead to blood sugar spikes.

The present disclosure enables the generation of notifications that reduce behaviors that lead to blood sugar spikes. As a result, the user can appropriately grasp his/her own behavior, thereby improving convenience for the user.

1 is a block configuration diagram showing an information processing system according to an embodiment of the present disclosure; FIG. 2 is a diagram showing a hardware configuration of a management server 100 in FIG. 1; FIG. 3 is a block diagram illustrating functions of a storage unit 120 and a control unit 130 of FIG. 2; FIG. 4 is a schematic diagram showing an example of tag information associated with the measurement data of FIG. 3; FIG. FIG. 4 is a diagram for explaining an example of an electrocardiogram waveform and a pulse wave according to the embodiment; 2 is a flowchart showing an example of processing of the information processing system 1 of FIG. 1;

The contents of the embodiments of the present invention are listed and explained. A system according to an embodiment of the present invention has the following configuration.

[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 biological data and a predetermined notification from the measurement data,
a biometric data generation unit that generates the biometric data including at least behavior information based on the measurement data;
a notification generation unit that compares the behavior information with blood glucose level spike condition information and generates a notification for reducing behavior leading to a blood glucose level spike that meets the condition according to the result of the comparison;
An information processing system characterized by:
[Item 2]
further comprising a data output unit that transmits a notification to the user terminal device of the user to reduce behavior leading to the blood sugar spike;
The information processing system according to item 1, characterized by:
[Item 3]
further comprising a data output unit that transmits a notification to a user terminal device of a person associated with the user to reduce behavior leading to the blood glucose spike;
The information processing system according to any one of items 1 and 2, characterized by:
[Item 4]
A server that receives measured data from a measuring device worn by a user via a network at a predetermined cycle and generates biological data and a predetermined notification from the measured data,
a biometric data generation unit that generates the biometric data including at least behavior information based on the measurement data;
a notification generation unit that compares the behavior information with blood glucose level spike condition information and generates a notification for reducing behavior leading to a blood glucose level spike that meets the condition according to the result of the comparison;
A server characterized by:
[Item 5]
An information processing method for receiving measurement data from a measurement device worn by a user via a network at a predetermined cycle, and generating biological data and a predetermined notification from the measurement data,
a step of generating the biometric data including at least behavior information based on the measurement data by a biometric data generator;
comparing the behavior information with the blood glucose level spike condition information by a notification generation unit, and generating a notification for reducing the behavior leading to the blood glucose level spike corresponding to the condition according to the result of the comparison;
An information processing method characterized by:
[Item 6]
A program for executing, on a computer, an information processing method for receiving measurement data from a measurement device worn by a user via a network at predetermined intervals and generating biometric data and predetermined notifications from the measurement data,
The information processing method includes:
a step of generating the biometric data including at least behavior information based on the measurement data by a biometric data generator;
comparing the behavior information with the blood glucose level spike condition information by a notification generation unit, and generating a notification for reducing the behavior leading to the blood glucose level spike corresponding to the condition according to the result of the comparison;
A program characterized by

Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. It should be noted that the embodiments described below do not unduly limit the content of the present disclosure described in the claims. Also, not all the components shown in the embodiments are essential components of the present disclosure. Also, the features shown in each embodiment can be applied to other embodiments as long as they are not mutually contradictory.

<Configuration>
FIG. 1 is a block configuration diagram showing an information processing system 1 according to an embodiment of the present disclosure. In this information processing system 1, for example, the management server 100 receives user measurement data from the measurement device 300 via the network NW at predetermined periodic timings, and performs predetermined calculations on the measurement data. 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 a network NW. The network NW includes the Internet, an intranet, a blockchain network, a wireless LAN (Local Area Network), a WAN (Wide Area Network), BLE (Bluetooth Low Energy), and the like.

The management server 100 is, for example, a device that receives user measurement data from the measurement device 300 via a network at predetermined periodic timing via the user terminal device 200 and performs calculation from the measurement data to biometric data. There is, for example, a server device that provides various Web services.

The user terminal device 200 is an information processing device such as a personal computer, a tablet terminal, a smart phone, a smart watch, a mobile phone, a PHS, a PDA, etc. owned by a predetermined user. It is used to display data as 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 user identification number, date of birth, gender, height, weight, stride length, etc. is registered in advance in the user terminal device 200, and user information including age calculated from the date of birth is measured data. to the management server 100 via the network NW.

Also, the user terminal device 200 may be configured such that the user inputs the state of data acquisition by the measuring device 300 using a touch panel or the like. The user terminal device 200 can input the 'data acquisition state' as tag information, for example, 'running' when running or 'eating' when eating. In this case, the user terminal device 200 associates the measurement data received from the measurement device 300 with predetermined periodic timing with the tag information and transmits the measurement data to the management server 100 via the network NW.

The measuring device 300 is a device that measures a user's biological data, and is a so-called wearable device that the user wears on his or her body such as a wrist or arm. This measurement device 300 is a device for measuring, for example, the user's electrocardiogram, pulse wave, skin temperature (body temperature), acceleration, and angular velocity data at predetermined periodic timings by a known technique. The predetermined cycle may be set in advance or may be arbitrarily set by the user. More specifically, for example, a time period in seconds may be set, or it may be similarly set depending on the frequency.

As a specific example of the configuration of the measurement device 300, two electrodes may be brought into contact with the skin, and an electrocardiogram may be obtained as electrocardiogram data from changes in the detected potential difference over time. The waveform may be data obtained by galvanic skin response. In addition, the skin is irradiated with light from LEDs that emit green, red, and infrared light, and the change in intensity of the light received by the photodiode over time changes the volume of blood vessels caused by the user's heartbeat, generating a pulse wave. It may be configured with a device that acquires pulse waveform data, and the pulse waveform that can be detected by this method is a photoelectric volume pulse waveform. Alternatively, the apparatus may be configured by a device that acquires the user's skin temperature as data from a temperature sensor that is brought into contact with the user's skin. Further, it may be composed of a three-axis acceleration sensor that detects the deformation state of each of the orthogonal XYZ axes, acquires the user's motion as acceleration data, and for example, the measuring device 300 is worn on the user's wrist, arm, or the like. In this case, the measurement device 300 acquires acceleration data as acceleration obtained by synthesizing the swing of the wrist or arm and the movement of the whole body. Further, it may be composed of a gyro sensor (angular velocity sensor) that detects rotational angular velocities in each of the orthogonal XYZ axes. If so, the measurement device 300 acquires angular velocity data as an angular velocity obtained by synthesizing the rotation of the wrist, arm, etc. and the movement of the whole body.

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 a wireless LAN or the like. A wired connection may be used instead of such a wireless connection. In addition, the user terminal device 200 and the measurement device 300 may be integrated equipment, for example, the measurement device 300 may be equipped with a SIM to have a communication function, or a management server using BLE (Bluetooth Low Energy). 100 may be configured to be directly communicable.

One or a plurality of user terminal devices 200 are connected to the network NW for the number of users using the measuring device 300 . One or a plurality of measuring devices 300 are connected to the same number of user terminal devices 200 used by one user. When one user uses multiple measuring devices 300 , multiple measuring devices 300 are connected to one user terminal device 200 .

<Management Server 100>
FIG. 2 is a diagram showing the hardware configuration of the management server 100. As shown in FIG. FIG. 3 is a block diagram illustrating functions of the storage unit 120 and the control unit 130. As shown in FIG. Note that the illustrated configuration is an example, and other configurations may be employed.

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. FIG.

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, input data, and the like for executing various control processes and functions in the control unit 130, and is composed of RAM (Random Access Memory), ROM (Read Only Memory), and the like. be. Further, as shown in FIG. 3, the storage unit 120 includes a measurement data DB 121 that stores data measured by the measurement device 300 in association with user information, and a biometric data that is calculated and generated from the measurement data in association with the user information. , a notification information DB 123 that stores various notifications sent to the user terminal device 200, and a user information DB 124 that stores user information including user identification numbers. The user information may include 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. Furthermore, the storage unit 120 temporarily stores data communicated with the user terminal device 200 . Note that the data structure of the DB is not limited to this, and part of the DB described above may be stored in the user terminal device 200 or the measurement 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 .

Data management unit 131 generates account information for each user who uses measuring device 300 . This account information generation is performed when the user using the measuring device 300 registers the account information with the user terminal device 200 . Therefore, the data management unit 131 controls access to various DBs in the storage unit 120 for each account of the user terminal device 200 and other terminal devices of the user. The data management unit 131 stores various data such as measurement data and biometric data in a corresponding DB in association with user information. Also, at this time, the data management unit 131 can store the measurement data in association with predetermined tag information.

FIG. 4 is a schematic diagram showing an example of tag information associated with the measurement data of FIG. Data D1 shown in FIG. 4 is measurement data of the measurement device 300 . The tag T1 is tag information associated with the data D1. For example, time information when the measuring device 300 measured the data D1 or time information when the data D1 was transmitted from the measuring device 300 to the user terminal device 200 is time-series. 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, which indicates June 20, 2018 12:07:46:144 milliseconds. Such time information can be obtained from communication logs. This makes it possible to ascertain which time period the measurement data belongs to.

It should be noted that the association between the measurement data and the biometric data using such tag information is not limited to the time information, and the physical information and behavior information indicating the user's physical condition and behavior can be entered freely and stored as tag information. (For example, in response to the question "How are you feeling now?", select one of "1: good, 2: normal, 3: bad", etc.) , the selected answer may be stored. Alternatively, when the user takes a predetermined action (for example, sitting, standing, walking, running, sleeping, eating, driving, resting, etc.), the user records measurement data or biometric data as tag information (for example, It may be a choice format or free description). Furthermore, instead of or in combination with the recording by the user, for example, measurement data and biological data (for example, walking speed information, stride length information, movement information of the wearing part, posture information, position information of the center of gravity, heartbeat information, etc. ), tag biometric data etc. by estimating predetermined behavior (for example, sitting, standing, lying down, walking, running, sleeping, waking up, going to bed, eating, driving, resting, etc.) by a known method You may associate as information (behavior information). At this time, for example, the learning model may be estimated based on the teacher measurement data, and the learning model may be personalized by performing additional learning based on the results recorded by the user. As a result, when biometric data is generated by the control unit 150, by associating the tag information with the biometric data, it becomes possible to generate biometric data with higher accuracy, and the biometric data itself is also used as action information. becomes possible.

Further, for example, the data management unit 131 can manage data based on tag information, such as rearranging (sorting) the data D1 in chronological order of the tags T1, and 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 biological data may be any information as long as it can be calculated from the measurement data. For example, the user's blood sugar level information, blood pressure information, heartbeat information, Electrocardiogram 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. , which is data such as motion information of the attachment site, and is calculated from measurement data by a known method. Biometric data generated by the calculation is stored in the biometric data DB 122 .

Further, by a known learning device, for example, measured data, biological data generated based on the measured data (such as heartbeat information and blood pressure information) and positive biological data (such as heartbeat information based on known medical equipment) , blood pressure information, etc.), and a learning model is created in advance based on the teacher data associated by the correspondence relationship (for example, information indicating the degree and range of error may be included), and the biometric data generation unit 132 Alternatively, determination using the learning model may be used as the above-described predetermined calculation (analysis) to generate biometric data.

Here, generation of blood pressure information will be described in detail. The biological data generator 132 includes a pulse wave transit time calculator 1321 . The biometric data generation unit 132 calculates second-order differential data from photoelectric volumetric pulse waveform data (for example, pulse waveform data in FIG. 5, etc.). The pulse wave transit time calculator 1321 detects R waves and T waves from the waveform profile in the electrocardiogram information described later, and detects P waves and D waves from the waveform profile in the photoelectric volume pulse waveform data, Based on the information, the ventricular systolic pulse wave transit time PTT_SYS and the ventricular diastolic pulse wave transit time PTT_DIA are calculated. In addition, the biological data generation unit 132 calculates the blood pressure information from the acceleration pulse wave characteristic information based on the second-order differential data to be calculated and the ventricular systolic pulse wave transit time PTT_SYS and ventricular diastolic pulse wave transit time PTT_DIA. perform calculations. The pulse wave transit time calculator 1321 may use first-order differential data or second-order differential data of the photoelectric volume pulse waveform data in detecting the R wave and the T wave.

In addition, the biometric data generator 132, for example, brings two electrodes of the measuring device 300 worn by the subject into contact with the skin, and electrocardiogram waveform data measured from changes in detected potential differences over time. Heartbeat information can be obtained as biological information (biologically generated information) from the intervals of QRS waves in (for example, the electrocardiographic waveform data in FIG. 5).

FIG. 5 shows an electrocardiographic waveform and a photoelectric volume pulse waveform of a non-measurer measured by the measuring device 300, and a velocity pulse waveform and an acceleration pulse waveform calculated by the management server 100. FIG. From the top of FIG. 5, the electrocardiographic waveform, the photoelectric volume pulse waveform, the velocity pulse waveform, and the acceleration pulse waveform. The vertical axis indicates the intensity of each waveform, and the electrocardiogram waveform and photoelectric volume pulse waveform are expressed in mV, which indicates potential. The horizontal axis indicates the passage of time, from left to right.

An electrocardiogram is a waveform that represents the periodic variations in electrical signals that cause the human heart to beat. An electrocardiographic waveform is assigned names of P wave, Q wave, R wave, S wave, and T wave to the inflection points of its shape, and indicates one cycle of heartbeat. The P-wave represents atrial contraction, the Q-wave, R-wave, S-wave represents the state of ventricular contraction, and the T-wave represents the onset of ventricular dilation.

A photoelectric volume pulse waveform is a waveform that indicates changes in blood pressure and volume in the peripheral vasculature accompanying the beating of the human heart. The photoelectric plethysmogram is assigned names of A wave, P wave, V wave, and D wave to the inflection points of its shape, and indicates one cycle of heartbeat. With the A wave as the reference point at which the arterial pulse wave occurs, the P wave is the percussion wave (shock wave) generated by left ventricular ejection, the V wave is the Valley wave (wave due to double uplift) generated when the aortic valve closes, and the D wave. indicates a Dicrotic wave (overlapping wave) which is a reflected oscillatory wave.

The velocity plethysmogram is obtained by first differentiating the photoelectric volume plethysmogram with respect to time. The acceleration pulse waveform is obtained by first-order differentiation of the velocity pulse waveform with respect to time, that is, second-order differentiation of the photoelectric volume pulse waveform. As shown in FIG. 5, the acceleration pulse waveform has a wave (positive wave at the beginning of systole), wave b (negative wave at the beginning of systole), wave c (re-rising wave in the middle of systole), wave d (shock wave) at each peak of the waveform. late re-descending wave), e-wave (extended early positive wave), and f-wave (extended early negative wave). 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 stretchability or elasticity of the blood vessel, respectively. The main components of blood vessels are vascular endothelium (Endothelium), elastic fiber (Elastin), protein (Collagen), and smooth muscle (Smooth Muscle). Each of these components has different properties, and Collagen and Elastin have a strong influence on the elasticity of blood vessels at maximum blood pressure and minimum blood pressure, respectively. Therefore, the elasticity that varies depending on the blood pressure value is indicated by the parameters of (b/a), which is the ratio of the b-wave intensity to the a-wave intensity, and (f/e), which is the ratio of the f-wave intensity to the e-wave intensity. These values also fluctuate due to the effects of age, gender, and environmental variables (temperature, etc.). Therefore, the values of (b/a) and (f/e) can be calculated as characteristic information of the acceleration pulse waveform.

As shown in FIG. 5, the difference between the time Tr at which the R wave occurs and the time Tp at which the P wave occurs is the ventricular systolic pulse wave propagation time PTT_SYS. The difference between the time Tt at which the T wave occurs and the time Td at which the D wave occurs is the ventricular diastolic pulse wave propagation time PTT_DIA. That is, 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, as shown in equations (1) and (2) Finally, the ventricular systolic pulse wave transit time PTT_SYS and the ventricular diastolic pulse wave transit time PTT_DIA can be calculated.

PTT_SYS=Tp−Tr (1)

PTT_DIA=Td-Tt (2)

The first electrode and the second electrode for measuring the electrocardiogram waveform and the optical sensor module for measuring the photoelectric volume pulse waveform are opposed to each other through the wrist, and the arrangement distance is separated, so that the detection site of the electrocardiogram waveform and the photoelectric This will separate the measurement sites of the plethysmogram. Therefore, the absolute calculation time of the ventricular systolic pulse wave transit time PTT_SYS and the ventricular diastolic pulse wave transit time PTT_DIA can be lengthened by creating a time lag between the respective characteristic waveforms. Therefore, when obtaining change information of the ventricular systolic pulse wave transit time PTT_SYS and the ventricular diastolic pulse wave transit time PTT_DIA, the accuracy of the change information can be improved.

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

The relationship between the pulse wave velocity and the longitudinal elastic modulus of the arterial wall is indicated by the equation (3) of the pulse wave velocity (Moens-Korteweg equation) shown below.

L/T_PTT=√(E·h/(2·r·ρ)) (3)

Each parameter in equation (3) is L: distance between measurements, T_PTT: pulse wave transit time, r: inner diameter of blood vessel, E: longitudinal elastic modulus of blood vessel, h: thickness of blood vessel, and ρ: blood density.

It is known that the longitudinal elastic modulus and blood pressure value are correlated,

E= _E0 ·exp(α·P) (4)

can be shown as Here, P: blood pressure value, α: constant, E ₀ : initial value.

From formulas (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 high values (Vp, Vd) indicated by the photoelectric volume pulse waveform. Also, since "E ₀ ·h" is a value proportional to the elasticity of the blood vessel, it can be replaced using the parameters (b/a) and (f/e) that indicate the elasticity.

Therefore, the systolic blood pressure BP_SYS (Blood Pressure_Systolic) and the diastolic blood pressure BP_DIA (Blood Pressure_Diastolic) can be expressed by the following formulas (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 the formula (6) is obtained by multiplying the natural logarithm of the ventricular systolic pulse wave transit time PTT_SYS by a constant A1 and by multiplying the natural logarithm of the P-wave intensity Vp by a constant A2. and the sum of the natural logarithm of (b/a) multiplied by the constant A3 and the constant A4. The diastolic blood pressure BP_SYS that can be calculated by the formula (7) is obtained by multiplying the natural logarithm of the ventricular systolic pulse wave transit time PTT_DIA by a constant A5 and by multiplying the natural logarithm of the D wave intensity Vd by a constant A6. and the sum of the natural logarithm of (f/e) multiplied by the constant A7 and 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 according to the characteristics of the device, the person to be measured, and the like. However, when confirming the change state of the systolic blood pressure BP_SYS and the diastolic blood pressure BP_DIA, it is not necessary to fix all the constants, and while substituting provisional values, the information on the systolic blood pressure BP_SYS and the diastolic blood pressure BP_DIA can be obtained. value can be obtained. The natural logarithm of the P-wave intensity Vp and the natural logarithm of the D-wave intensity Vd are terms that take into account the effect of blood density. Also, the natural logarithm of (b/a) and the natural logarithm of (f/e) are terms that take into account the effect of the longitudinal elastic modulus of the arterial wall. Therefore, depending on the measurement conditions, the information on the systolic blood pressure BP_SYS and the information on the diastolic blood pressure BP_DIA may be calculated by selecting one of the terms and making the other terms constant.

In addition, the biometric data generator 132 converts temperature information from skin temperature information of the subject measured by a temperature sensor (such as a thermistor) of the measuring device 300 worn by the subject to biological information (biologically generated information), for example. can be obtained as

In addition, the biometric data generation unit 132 uses, for example, a known calculation method or the like from the waveform data of the acceleration data measured by the measurement device 300 worn on the wrist of the subject, or uses them in combination (for example, By averaging, weighting, etc.), walking speed information can be obtained as biological information (biologically generated information). ) can be obtained as

In addition, the biometric data generation unit 132 uses, for example, a known calculation method or the like from the waveform data of the acceleration data measured by the measurement device 300 worn on the wrist of the subject, or uses them in combination (for example, By averaging, weighting, etc.), stride information can be obtained as biological information (biologically generated information). One step interval is determined based on the timing when the acceleration component in the direction of travel is the smallest or when it switches to the opposite direction, the timing when the acceleration component in the direction perpendicular to the direction of travel is the smallest or the timing when the vertical direction switches, etc.) Therefore, stride information can be obtained as biometric information (biometric generation information) by using time information. Alternatively, for example, when kicking off the ground, the acceleration component in the kicking direction is synthesized, so it is possible to determine the interval of one step at the timing of generation of the acceleration component in that direction.

In addition, the biometric data generation unit 132 determines, for example, from acceleration data and angular velocity data measured by the measurement device 300 worn by the subject, the part (eg, wrist, ankle, etc.) where the measurement device 300 is worn is It is possible to obtain motion information, such as how fast and at what angle the robot moves, as biological information (biologically generated information).

In addition, the biometric data generation unit 132 performs frequency analysis of acceleration data measured by the measuring device 300 that is worn on a daily basis, for example, and associates high and low frequencies with high and low activity frequencies. Activity amount information can be obtained as biometric information (biometric generation information) by calculating based on a predetermined condition such as what percentage of a day occupies.

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

In addition, the biometric data generation unit 132 generates 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, for example, based on the heart rate information that increases with the exercise load. By weighting, the exercise load amount information can be obtained as biological information (biologically generated information). In addition, for example, if vector information of acceleration data is added, state information such as walking environment (slope, stairs, etc.) and posture (standing position, sitting position, etc.) can be specified, so the state information may be further used. Further, by using additional information such as angular velocity information, it is possible to obtain more accurate exercise load amount information.

In addition, the biological data generation unit 132 uses the heart rate information, for example, to obtain the maximum oxygen uptake information by a known formula such as VO ₂ max = 15 x (220 - age) / heart rate (especially heart rate at rest). It can be obtained as biometric information (biometric generation information).

Further, by a known learning device, for example, biometric information, biometric information generated based on the biometric information (such as heart rate information and blood pressure information) and positive biometric information (for example, based on known medical equipment Heart rate information, blood pressure information, etc.), and a machine learning model is created in advance based on the teacher data that is associated with the corresponding relationship (for example, information indicating the degree and range of error may be included), and biometric data The generating unit 132 may generate the biometric information using the determination using the machine learning model as the predetermined calculation (analysis) described above.

Here, blood sugar level spikes, which are states in which the blood sugar level suddenly rises and falls after a meal, are said to increase the risk of arteriosclerosis if they occur frequently, although the blood sugar level is usually normal. However, it is possible that the user has unwittingly taken actions that lead to blood sugar spikes, and it is desirable for the user to be informed of these actions early.

Therefore, in the present embodiment, notification generation unit 133 sets behavior information generated based on measurement data received at predetermined periodic timing by measurement device 300 worn by the user on a daily basis as a blood glucose level spike condition. Information is compared with the information, and depending on the result of the comparison, notifications that reduce behaviors that lead to blood sugar level spikes that meet the conditions (notifications that notify that the behavior itself will be suppressed, and notifications that what kind of behavior should be taken) Advice notifications, etc.). The generation of notifications here includes determining which notifications are to be output data. including determining from Details will be described below.

The blood sugar level spike condition information includes, for example, whether or not the user sits for a predetermined period of time without getting up from the chair after eating (post-meal behavior condition), sleep for a predetermined period or less (for example, 6 hours or less) at a predetermined frequency (for example, 3 times a week). etc.) (sleep condition), whether at least one of systolic blood pressure and diastolic blood pressure is a predetermined value or higher (blood pressure condition), whether breakfast is taken (breakfast condition), without eating early Information indicating conditions such as eating slowly (meal period condition) or eating low GI food (GI condition), and the reference value for each condition is the object of comparison with the corresponding behavioral information and biometric data. is set as As described above, the behavior information includes, for example, measurement data and biometric data (for example, walking speed information, stride information, movement information of the wearing part, posture information, position information of the center of gravity, heart rate information, etc.), and in particular, the movement of the hand or leg. Predicted behavior (e.g. sitting, standing, lying down, walking, running, sleeping, waking up, going to bed, eating, driving, resting, etc.) by known methods based on data obtained from movement information of the wearing part) More specifically, it may be estimated by a learning model learned based on teacher measurement data or biometric data.

For example, in the post-meal action condition, the notification generation unit 133 generates meal period information indicating the period during which the user had a meal based on the period information in which the action information indicating "meal" was determined, and then It may be determined whether behavior information (for example, standing, walking, running, activity information, etc.) indicating some exercise state during the period starts within a predetermined period. Then, based on the determination result, notification information that encourages exercise may be generated as notification that reduces actions leading to blood sugar spikes.

For example, in the sleep condition, the notification generation unit 133 generates sleep period information indicating the period during which the user was sleeping based on the period information in which the action information indicating "sleep" was determined, and the sleep period information is specified. (whether or not you sleep for 6 hours or less three times a week). Then, based on the determination result, notification information that encourages sleep to a degree that satisfies the conditions may be generated as a notification that reduces actions leading to blood sugar spikes.

For example, in the breakfast condition, the notification generating unit 133 generates meal period information indicating a period during which the user has been eating based on the period information in which the action information indicating "meal" is determined, and the meal period information is It may be determined whether the confirmation is made in a predetermined time period indicating early morning. Then, based on the determination result, notification information that prompts the user to eat breakfast may be generated as a notification that reduces actions leading to blood sugar spikes.

For example, in the meal period condition, the notification generation unit 133 may generate meal period information indicating the period during which the user had a meal based on the period information in which the action information indicating "meal" was determined. Then, for example, by presenting one or more pieces of meal period information to the user on the application, it becomes possible to objectively grasp the tendency of one's own meal period. In comparison with the meal period, meal period trend information (for example, if the meal period is shorter than the first reference meal period, "fast eating"; if the meal period is longer than the first reference meal period, "exactly good", and "late eating" when the meal period is longer than the second reference meal period longer than the first reference meal period). It determines whether the period conditions are met. Then, based on the determination results, notification information on changes in the meal period corresponding to the meal period trend information (for example, alert information such as ``Be careful not to eat too quickly'' or advice such as ``Let's try to chew well'') information, etc.) may be generated as notifications to reduce behaviors that lead to blood sugar spikes.

For example, in GI conditions, it is known that the higher the GI (glycemic index) value, the greater the increase in the amount of increase in the blood sugar level. amount), the notification generation unit 133 can determine how much GI value is likely to be ingested in the blood sugar level information linked to the action information indicating "meal". It may be determined whether or not the GI value condition is met by estimating.

Notification generator 133 generates blood sugar level spike information from blood sugar level information according to a predetermined spike condition. The spike condition is, for example, when the blood sugar level spike is normally a normal blood sugar level, but the blood sugar level rises significantly after a meal, and the peak value that turns from an increase to a decrease occurs after a predetermined time after the meal (for example, about 1 to 2 after a certain period of time), and after a predetermined period of time (for example, about 1 to 2 hours later), focusing on the fact that the spike condition is that the blood glucose level information reaches a predetermined reference value (for example, 140 to 140). Any value in the range of 160 mg / dl, etc.), and the blood sugar 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 when the 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 predetermined period of time has elapsed. In addition, in the case of a person with high blood sugar level, such as a diabetic, there is a risk of erroneous determination based on only the reference value. and the magnitude of the relative difference value with respect to the average value information such as the average value of the sleep period calculated based on the behavior information and the average value of the predetermined period before meals exceeds a predetermined reference value (such as 50 mg / dl) or that the gradient from the minimum value to the maximum value of the blood sugar level information for a predetermined period (time information of each value is also considered) exceeds a predetermined reference value. Then, the number of blood sugar level spikes indicated by the blood sugar level spike information may be notified together with the above notification.

In this way, it is possible to issue a notification that reduces behaviors that lead to blood sugar spikes.

Note that the notification generation unit 133 may transmit the notification to a person associated with the user instead of or in addition to the user. The person associated with the user is, for example, associated with the user information and has identification information such as account information stored in the user information DB 124 or the like. It may be a relative such as a partner, caregiver, assistant, doctor, etc.

The data output unit 134 outputs measurement data, biological 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 a dedicated application, for example, so that the user can easily check it.

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.

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

As the 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 processing of step S101 may be performed as preprocessing 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.

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

As the process of step S103, the biometric data generation unit 132 reads the measurement data, and generates biometric data by a predetermined calculation 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 notification generation unit 133 reads the biometric data, and generates a predetermined notification (especially a notification for reducing behavior leading to a blood sugar level spike) by predetermined calculation or the like.

As the process of step S<b>105 , the data output unit 134 reads the biometric data and/or the predetermined notification and outputs them to the user terminal device 200 .

<effect>
As described above, the information processing system according to the present embodiment generates notifications for reducing behaviors that lead to blood sugar spikes. As a result, the user can appropriately grasp his/her own behavior, thereby improving convenience for the user.

Although the disclosed embodiments have been described above, they can be implemented in various other forms, and can be implemented with various omissions, substitutions, and modifications. These embodiments, modifications, omissions, substitutions and changes are included within the technical scope of the claims and their equivalents.

1 Information Processing System 100 Management Server 200 User Terminal Device 300 Measuring Device NW Network

Claims (10)

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 biological data and a predetermined notification from the measurement data,
a biometric data generation unit that generates the biometric data including at least behavior information based on the measurement data;
a notification generation unit that compares the behavior information with blood glucose level spike condition information and generates a notification for reducing behavior leading to a blood glucose level spike that meets the condition according to the result of the comparison;
An information processing system characterized by: further comprising a data output unit that transmits a notification to the user terminal device of the user to reduce behavior leading to the blood sugar spike;
The information processing system according to claim 1, characterized by: further comprising a data output unit that transmits a notification to a user terminal device of a person associated with the user to reduce behavior leading to the blood glucose spike;
3. The information processing system according to claim 1 or 2, characterized in that: The blood sugar level spike condition information includes post-meal behavioral conditions,
The notification generating unit generates meal period information indicating a period during which the user has been eating based on the period information in which the behavior information indicating eating is determined, and the behavior information indicating some exercise state in the subsequent period is predetermined. determine if it will start within the period, and generate a notification to encourage exercise based on the result of the determination;
4. The information processing system according to any one of claims 1 to 3, characterized by: The blood glucose level spike condition information includes sleep conditions,
The notification generation unit generates sleep period information indicating a period during which the user was sleeping based on the period information in which the behavior information indicating sleep was determined, and determines whether the sleep period information matches a predetermined condition. , based on the results of that determination, generate a sleep-promoting notification;
5. The information processing system according to any one of claims 1 to 4, characterized in that: The blood glucose level spike condition information includes a breakfast condition,
The notification generation unit generates meal period information indicating a period during which the user has been eating based on the period information in which the behavior information indicating meal is determined, and the meal period information indicates the early morning. determining if it is confirmed and, based on the results of that determination, generating a notification to remind you to have breakfast;
6. The information processing system according to any one of claims 1 to 5, characterized in that: The blood glucose level spike condition information includes a breakfast condition,
The notification generating unit generates meal period information indicating a period during which the user has been eating based on the period information in which the behavior information indicating eating has been determined, and compares the meal period information with a reference meal period. Determining meal period trend information indicating the trend of the meal period, and generating a notification prompting the user to change the trend of the meal period based on the result of the determination;
7. The information processing system according to any one of claims 1 to 6, characterized by: A server that receives measured data from a measuring device worn by a user via a network at a predetermined cycle and generates biological data and a predetermined notification from the measured data,
a biometric data generation unit that generates the biometric data including at least behavior information based on the measurement data;
a notification generation unit that compares the behavior information with blood glucose level spike condition information and generates a notification for reducing behavior leading to a blood glucose level spike that meets the condition according to the result of the comparison;
A server characterized by: An information processing method for receiving measurement data from a measurement device worn by a user via a network at a predetermined cycle, and generating biological data and a predetermined notification from the measurement data,
a step of generating the biometric data including at least behavior information based on the measurement data by a biometric data generator;
comparing the behavior information with the blood glucose level spike condition information by a notification generation unit, and generating a notification for reducing the behavior leading to the blood glucose level spike corresponding to the condition according to the result of the comparison;
An information processing method characterized by: A program for executing, on a computer, an information processing method for receiving measurement data from a measurement device worn by a user via a network at predetermined intervals and generating biometric data and predetermined notifications from the measurement data,
The information processing method includes:
a step of generating the biometric data including at least behavior information based on the measurement data by a biometric data generator;
comparing the behavior information with the blood glucose level spike condition information by a notification generation unit, and generating a notification for reducing the behavior leading to the blood glucose level spike corresponding to the condition according to the result of the comparison;
A program characterized by

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