JP2021186681A - Information processing system, server, information processing method and program - Google Patents

Abstract

To provide an information processing system, etc. providing, via a user support data generation section, user support data including an advice of particularly improving a blood pressure state, an alert, transition data, and the like based on measurement data.SOLUTION: An information processing system 1 has a management server 100. The information processing system receives measurement data via a network from a measuring device worn by a user at prescribed periods and generates biometric data and user support data from the measurement data. The information processing system includes a biometric data generation section of executing a prescribed calculation of the measurement data and generating the biometric data, and a user support data generation section for generating transition data generated based on the biometric data. The biometric data is a result of a prescribed thinning correction of the biometric data. The biometric data is at least one of blood pressure information, heartbeat information, blood oxygen level information, maximum oxygen intake information, respiration frequency and body temperature information.SELECTED DRAWING: Figure 1

Description

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

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

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

Japanese Patent No. 6202510 Japanese Patent No. 6257015

By the way, in Japan, the number of stroke patients is about 300,000 a year, and the recurrence rate is said to be 50% in 10 years. Health management for recurrence prevention centering on daily blood pressure control is especially necessary. High in sex. In addition, there is a need in the world to reduce the risk of developing other diseases associated with hypertension.

However, for the prevention and treatment of hypertension, it is not enough to simply control the blood pressure level, and it is necessary to improve the quality of exercise and sleep, which is appropriate for general users. It was difficult to manage. In addition, the frequency of consultation with a doctor or the like is limited, and there is a demand for a system that can obtain guidance information for preventing hypertension on a daily basis.

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

The information processing system according to one aspect of the present disclosure is an information processing system that receives measurement data from a measuring device worn by a user via a network at a predetermined cycle and generates biometric data and user support data from the measured data. Therefore, it is provided with a biometric data generation unit that executes a predetermined calculation on the measurement data and generates the biometric data, and a user support data generation unit that generates transition data generated based on the biometric data. , The biometric data is biometric data as a result of performing a predetermined thinning correction on the biometric data, and the biometric data includes blood pressure information, heartbeat information, blood oxygen amount information, maximum oxygen intake information, and respiration. At least one of the number and body temperature information.

The server in one aspect of the present disclosure is 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 user support data from the measured data. The biometric data is provided with a biometric data generation unit that executes a predetermined operation on the measurement data and generates the biometric data, and a user support data generation unit that generates transition data generated based on the biometric data. Is biometric data as a result of performing predetermined thinning correction on the biometric data, and the biometric data includes blood pressure information, heartbeat information, blood oxygen level information, maximum oxygen intake information, respiratory rate, and body temperature information. At least one of.

The information processing method according to one aspect of the present disclosure is an information processing method in which measurement data is received from a measuring device worn by a user via a network at a predetermined cycle, and biometric data and user support data are generated from the measured data. Therefore, the biometric data generation unit executes a predetermined operation on the measurement data to generate the biometric data, and the user support data generation unit generates transitional data generated based on the biometric data. The biometric data is biometric data as a result of performing a predetermined thinning correction on the biometric data, and the biometric data includes blood pressure information, heartbeat information, blood oxygen level information, and maximum. At least one of oxygen intake information, respiratory rate, and body temperature information.

Further, the program according to one aspect of the present disclosure is an information processing method in which measurement data is received from a measuring device worn by a user via a network at a predetermined cycle, and biometric data and user support data are generated from the measured data. The information processing method is a program to be executed by a computer, in which the biometric data generation unit executes a predetermined operation on the measurement data to generate the biometric data, and the user support data generation unit. The biometric data includes a step of generating transitional data generated based on the biometric data, and the biometric data is biometric data as a result of performing a predetermined thinning correction on the biometric data. , Blood pressure information, heartbeat information, blood oxygen level information, maximum oxygen intake information, respiratory rate, body temperature information at least one of them.

According to the present disclosure, the information processing system according to the present embodiment is based on measurement data via a user support data generation unit, and user support data including advice, alerts, transition data, etc. for improving a blood pressure state in particular. To generate. As a result, for example, the user can easily acquire an improvement process for achieving the target step-down value, and the convenience of the user is improved.

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

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

[Item 1]
An information processing system that receives measurement data from a measurement device worn by a user via a network at a predetermined cycle and generates biometric data and user support data from the measurement data.
A biometric data generation unit that executes a predetermined operation on the measurement data and generates the biometric data.
It is equipped with a user support data generation unit that generates transition data generated based on the biometric data.
The biometric data is biometric data as a result of performing a predetermined thinning correction on the biometric data.
The biological data is at least one of blood pressure information, heart rate information, blood oxygen amount information, maximum oxygen uptake information, respiratory rate, and body temperature information.
An information processing system characterized by this.
[Item 2]
Predetermined decimation correction is performed according to the result of comparing the motion information and the reference motion information.
The information processing system according to item 1, wherein the information processing system is characterized by the above.
[Item 3]
Predetermined decimation correction is performed according to the rest period information,
The information processing system according to any one of items 1 and 2, wherein the information processing system is characterized by the above.
[Item 4]
The biometric data includes at least blood pressure information.
The user support data generation unit notifies information related to the user's exercise information according to the result of comparing the blood pressure reduction target value and the blood pressure transition data.
The information processing system according to any one of items 1 to 3, wherein the information processing system is characterized by the above.
[Item 5]
The biometric data includes at least blood pressure information.
The user support data generation unit notifies information related to the lower limit of blood pressure according to the result of comparing the lower limit of blood pressure and blood pressure information.
The information processing system according to any one of items 1 to 4, wherein the information processing system is characterized by the above.
[Item 6]
When the user support data generation unit detects an abnormality in the biometric data, the user support data generation unit sends a notification indicating the abnormality and the transition data to the contact linked to the user information.
The information processing system according to item 1 to item 5, wherein the information processing system is characterized by the above.
[Item 7]
A server that receives measurement data from a measurement device worn by a user via a network at a predetermined cycle and generates biometric data and user support data from the measurement data.
A biometric data generation unit that executes a predetermined operation on the measurement data and generates the biometric data.
It is equipped with a user support data generation unit that generates transition data generated based on the biometric data.
The biometric data is biometric data as a result of performing a predetermined thinning correction on the biometric data.
The biological data is at least one of blood pressure information, heart rate information, blood oxygen amount information, maximum oxygen uptake information, respiratory rate, and body temperature information.
A server that features that.
[Item 8]
It is an information processing method that receives measurement data from a measurement device worn by a user via a network at a predetermined cycle and generates biometric data and user support data from the measurement data.
A step of executing a predetermined operation on the measurement data by the biometric data generation unit to generate the biometric data, and a step of generating the biometric data.
Including a step of generating transition data generated based on the biometric data by the user support data generation unit.
The biometric data is biometric data as a result of performing a predetermined thinning correction on the biometric data.
The biological data is at least one of blood pressure information, heart rate information, blood oxygen amount information, maximum oxygen uptake information, respiratory rate, and body temperature information.
An information processing method characterized by that.
[Item 9]
It is a program for executing an information processing method on a computer that receives measurement data from a measurement device worn by a user via a network at a predetermined cycle and generates biometric data and user support data from the measurement data.
The information processing method is
A step of executing a predetermined operation on the measurement data by the biometric data generation unit to generate the biometric data, and a step of generating the biometric data.
Including a step of generating transition data generated based on the biometric data by the user support data generation unit.
The biometric data is biometric data as a result of performing a predetermined thinning correction on the biometric data.
The biological data is at least one of blood pressure information, heart rate information, blood oxygen amount information, maximum oxygen uptake information, respiratory rate, and body temperature information.
A program characterized by that.

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

(Embodiment 1)
<Structure>
FIG. 1 is a block configuration diagram showing an information processing system 1 according to the first embodiment of the present disclosure. The information information system 1 receives, for example, 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. It is a system that generates biometric data and generates user support data based on the biometric data.

The information processing system 1 has a management server 100, a user terminal device 200, a measuring device 300, and a network NW. The management server 100 and the user terminal device 200 are connected via the network NW. The network NW is composed of the Internet, an intranet, a blockchain network, a wireless LAN (Local Area Network), a WAN (Wide Area Network), 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 the user, such as a personal computer, a tablet terminal, a smartphone, a smart watch, a mobile phone, a PHS, or a PDA. It is used to display a waveform graph or the like, or to display user support data (details will be described later) generated based on biometric data. User information such as a user's identification number, date of birth, gender, height, weight, stride, etc. is registered in the user terminal device 200 in advance, and user information including age calculated from the date of birth is measured data. It is transmitted to the management server 100 via the network NW in association with.

Further, the user terminal device 200 can input a state in which the user acquires data by the measuring device 300 by using a touch panel or the like. The user terminal device 200 can input the "state for acquiring data" as tag information such as "running" when running and "mealing" when eating. 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, for example, a wearable device that the user wears on the body such as his / her wrist or arm. The measuring device 300 is, for example, a plurality of types of devices for measuring data of a user's electrocardiogram, pulse wave, temperature (body temperature), acceleration, and angular velocity at a predetermined periodic timing. The predetermined cycle may be preset or may be arbitrarily set by the user. More specifically, for example, a time 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 a pulse wave is generated by the change in the volume of the blood vessel caused by the heartbeat of the user's heart due to the time change of the intensity of the light received by the photodiode. 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 positive voltage 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. Other examples include those that measure pulse waves from fingers using a camera lens such as a smartphone, those that measure heart rate, respiratory rate, pulse waves, heat, etc. in a non-contact manner using a Doppler sensor or camera, and metals. The heart rate or the like may be measured by a weak current flowing by grasping the hand grip made of the product with both hands.

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 by 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 user support data DB 123 to store the user support data generated based on the biometric data in association with the user information, and the user information DB 124 to store the user information including the user identification number. .. Further, the user information includes the account information generated by the data management unit 131, and the user information DB 124 may store the account information in association with other user information. Further, the storage unit 120 temporarily stores the data that has communicated with the user terminal device 200. The data structure of the DB is not limited to this, and a part of the above-mentioned DB may be stored in the user terminal device 200 or the measuring device 300.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Further, exemplifying a method of calculating heart rate information which is biological data from measurement data, particularly a resting heart rate, for example, electrocardiographic waveform data measured by a measuring device 300 worn (for example, electrocardiographic waveform in FIG. 5). Heart rate information can be obtained from the intervals of QRS waves in (data, etc.), photoelectric volume pulse waveforms, and the like. When the heart rate information during exercise is an acquisition method that is affected by body movement (for example, movement of the wearing site), for example, frequency analysis of acceleration data or the like measured by the measuring device 300 is performed, if necessary. Then, the frequency component generated by the body movement may be specified, and the frequency component may be subtracted from the frequency component of the heartbeat information at the time of acquisition.

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

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

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

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

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

The user support data generation unit 133 executes predetermined calculation processing and statistical processing based on biological data, for example, transition of user's blood pressure transition data, activity status data (exercise amount information, exercise load amount information, activity amount information, etc.). Generates user statistical information including distribution), fatigue level data, sleep quality data, etc., and gives advice and alerts on the results of comparing the user statistical information with standard information (for example, average value information and target value information). Generate user support data including. The user support data is associated with the user information and stored in the user support data DB 123.

The user support data generation unit 133 generates, for example, the user's blood pressure transition data based on the blood pressure information generated from the measurement data received at a predetermined periodic timing by the measuring device 300 that is worn on a daily basis. Then, for example, it is estimated based on the antihypertensive target value set by the family doctor or the like, the antihypertensive target value set by the Japanese Society of Hypertension, the current medical history, age, BMI value, lifestyle, etc. included in the user information. Compare with the user's blood pressure transition data using the blood pressure reduction target value calculated from the improvement tendency of other users with the same attributes as the user as reference information.

Here, in this system, as compared with the conventional blood pressure measuring device, the measuring device 300 is attached on a daily basis to acquire the biological data. For example, the blood pressure transition data is easily affected by the user's exercise. .. More specifically, especially after exercise, the blood pressure tends to rise, and it may be difficult to judge the fluctuation state of the blood pressure in normal times from the blood pressure transition data. Therefore, for example, when exercise information such as exercise amount information and exercise intensity information is compared with reference exercise information and excessive exercise exceeding the reference exercise information is determined, blood pressure information measured at that time is deleted and thinned out. The correction may be performed. Then, an approximate curve of the time transition may be calculated from the blood pressure information after the thinning correction within an arbitrary predetermined time. If necessary, the approximate curve may be shown by a graph, and both graphs may be generated so that the blood pressure transition information before and after the thinning correction can be compared. , May be superimposed and displayed for comparison. In addition, not only the transition data of blood pressure information over time (blood pressure transition data), but also the living body that is easily affected by the user's exercise such as heartbeat information, blood oxygen level information, maximum oxygen intake information, respiratory rate, and body temperature information. By performing thinning correction in the same manner as above, it is possible to estimate the transition information of normal biometric data that is not affected by exercise, and in particular, the biometric data of normal time after each thinning correction. By acquiring and comparing the reference biometric data information (including past biometric data such as before a predetermined day), it becomes easy to confirm the state change that is not affected by exercise. In addition to or in addition to the above-mentioned thinning correction, comparison of the location information stored in advance with GPS information and input information from the user terminal device 200 or the measuring device 300 (for example, information indicating whether or not the user has gone out) Based on the information on whether or not the user is in a resting place that can be in a resting state), it is determined from the resting place information and the time information that the user is in a resting place with many resting states, and only the data in that period is acquired as the resting period information. The thinning correction may be performed.

Further, the user support data generation unit 133 is, for example, at least the activity amount information, the exercise amount information, and the exercise load information generated from the measurement data received at a predetermined periodic timing by the measuring device 300 which is worn on a daily basis. Based on any of the exercise information, activity status data related to this information is generated. Then, in the user support data generation unit 133, for example, the above-mentioned blood pressure lowering target value is compared with the user's blood pressure transition data, and the blood pressure reduction transition does not occur to the predetermined blood pressure value even after a predetermined number of days have passed. If it is determined that the situation is not appropriate, further refer to the above activity status data, and if it is determined that the amount of exercise is not appropriate, a notification to increase or decrease the amount of exercise (for example, walking time to be increased or decreased). Notification of increasing or decreasing the exercise load when it is judged that the exercise load is not appropriate (for example, change amount such as stride length, walking speed, frequency of use of slopes and stairs, frequency of playing sports, etc.) Generate user assistance data, including advice to increase or decrease activity (eg, suggestion of walking time to increase or decrease) if activity is deemed inappropriate. .. As a result, the user can easily acquire the improvement process for achieving the target step-down value, and the convenience of the user is improved.

On the other hand, the lower limit of blood pressure may be set so that excessive blood pressure due to the antihypertensive drug can be determined. ), For example, the cumulative period of the lower limit of blood pressure reduction within a predetermined period is equal to or longer than the predetermined position, and the value indicated by the blood pressure information in the comparison at the time of blood pressure information generation is lower than the lower limit of blood pressure reduction. Etc. may be determined, and user support data for notifying the user of the state (information related to the lower limit of blood pressure) may be generated.

The user support data generation unit 133 generates, for example, the blood pressure transition data of the user based on the blood pressure information generated from the measurement data by the measuring device 300 which is worn on a daily basis. Then, for example, an abnormal value such as a sudden rise or a sudden drop in blood pressure is monitored, and when the abnormal value occurs, user support data including an alert is generated. The user support data generation unit 133 may generate blood pressure transition data before and after an abnormal value as user support data in addition to the alert. Further, when the abnormality is detected, the control unit 130 links the user information such as a medical institution or a close relative directly from the management server 100 or indirectly via the user terminal device 200 or the measuring device 300. It may be controlled to send a notification indicating an abnormality (for example, a notification via a predetermined application stored in a device such as a PC or a smartphone, a notification using an e-mail address, etc.) to the attached contact. In particular, when the notification is sent to the medical institution, the notification may be displayed on the device as a request signal for transporting an ambulance to the device owned by the medical institution. This makes it possible to detect blood pressure-related diseases with a high recurrence rate at an early stage, and even if the user is unable to contact due to a sudden onset, it is possible to automatically notify others of blood pressure abnormalities. Furthermore, in general, in order to minimize the risk of death and sequelae at the onset of stroke, it is necessary to take prescribed measures within 4 hours and 30 minutes immediately after the onset, but ambulance transportation can be arranged immediately after the onset. The system can increase safety in the unlikely event.

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

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

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

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

When the user uses the measuring device 300 as the process of step S102, the measurement data is transmitted from the measuring device 300 to the management server 100 via the user terminal device 200 and 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 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 biometric data is read by the user support data generation unit 133, and the user support data is generated by a predetermined calculation or the like. The generated user support data is stored in the user support data DB 123 by the data management unit 131.

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

<Effect>
As described above, the information processing system according to the present embodiment generates user support data including advice and alerts for improving the blood pressure state based on the measurement data via the user support data generation unit 133. .. As a result, for example, the user can easily acquire an improvement process for achieving the target step-down value, 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 (9)

An information processing system that receives measurement data from a measurement device worn by a user via a network at a predetermined cycle and generates biometric data and user support data from the measurement data.
A biometric data generation unit that executes a predetermined operation on the measurement data and generates the biometric data.
It is equipped with a user support data generation unit that generates transition data generated based on the biometric data.
The biometric data is biometric data as a result of performing a predetermined thinning correction on the biometric data.
The biological data is at least one of blood pressure information, heart rate information, blood oxygen amount information, maximum oxygen uptake information, respiratory rate, and body temperature information.
An information processing system characterized by this. Predetermined decimation correction is performed according to the result of comparing the motion information and the reference motion information.
The information processing system according to claim 1. Predetermined decimation correction is performed according to the rest period information,
The information processing system according to any one of claims 1 or 2, wherein the information processing system is characterized by the above. The biometric data includes at least blood pressure information.
The user support data generation unit notifies information related to the user's exercise information according to the result of comparing the blood pressure reduction target value and the blood pressure transition data.
The information processing system according to any one of claims 1 to 3, wherein the information processing system is characterized by the above. The biometric data includes at least blood pressure information.
The user support data generation unit notifies information related to the lower limit of blood pressure according to the result of comparing the lower limit of blood pressure and blood pressure information.
The information processing system according to any one of claims 1 to 4, wherein the information processing system is characterized by the above. When the user support data generation unit detects an abnormality in the biometric data, the user support data generation unit sends a notification indicating the abnormality and the transition data to the contact linked to the user information.
The information processing system according to claim 1 to claim 5, wherein the information processing system is characterized by the above. A server that receives measurement data from a measurement device worn by a user via a network at a predetermined cycle and generates biometric data and user support data from the measurement data.
A biometric data generation unit that executes a predetermined operation on the measurement data and generates the biometric data.
It is equipped with a user support data generation unit that generates transition data generated based on the biometric data.
The biometric data is biometric data as a result of performing a predetermined thinning correction on the biometric data.
The biological data is at least one of blood pressure information, heart rate information, blood oxygen amount information, maximum oxygen uptake information, respiratory rate, and body temperature information.
A server that features that. It is an information processing method that receives measurement data from a measurement device worn by a user via a network at a predetermined cycle and generates biometric data and user support data from the measurement data.
A step of executing a predetermined operation on the measurement data by the biometric data generation unit to generate the biometric data, and a step of generating the biometric data.
Including a step of generating transition data generated based on the biometric data by the user support data generation unit.
The biometric data is biometric data as a result of performing a predetermined thinning correction on the biometric data.
The biological data is at least one of blood pressure information, heart rate information, blood oxygen amount information, maximum oxygen uptake information, respiratory rate, and body temperature information.
An information processing method characterized by that. It is a program for executing an information processing method on a computer that receives measurement data from a measurement device worn by a user via a network at a predetermined cycle and generates biometric data and user support data from the measurement data.
The information processing method is
A step of executing a predetermined operation on the measurement data by the biometric data generation unit to generate the biometric data, and a step of generating the biometric data.
Including a step of generating transition data generated based on the biometric data by the user support data generation unit.
The biometric data is biometric data as a result of performing a predetermined thinning correction on the biometric data.
The biological data is at least one of blood pressure information, heart rate information, blood oxygen amount information, maximum oxygen uptake information, respiratory rate, and body temperature information.
A program characterized by that.

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