JP2023125114A - Information processing system, server, information processing method, and program - Google Patents

Abstract

To provide an information processing system, a server, an information processing method, and a program for providing frail index information for showing especially a frail state on the basis of measurement data via a frail index information generation part.SOLUTION: In an information management system, a management server 100 includes a frail index information generation part for generating at least one of responsiveness index information for showing how biological data respond to occurrence of predetermined or more exercise intensity on the basis of change information of biological data within a determination period with a time when the predetermined or more exercise intensity occurs as reference time information, and recoverability index information for showing how biological data recover from a response due to occurrence of the predetermined or more exercise intensity on the basis of change information of biological data from a response state or an operation state to a rest state, and for generating frail index information for showing a user's frail state on the basis of at least one of the responsiveness index information and the recoverability index information and previously stored correspondence relation information.SELECTED DRAWING: Figure 1

Description

The present disclosure relates to an information processing system, a server, an information processing method, and a program that generate frailty index information based on measurement data obtained from a measurement device.

It is said that increasing muscle strength is helpful in preventing diseases associated with aging such as dementia, and it is important to prevent frailty by actively exercising on a daily basis.

Determination of frailty is also important; for example, Patent Document 1 discloses a determination method in which questions are asked to the subject and the total score of the answers is calculated, and Patent Document 2 discloses a determination method in which questions are asked to the subject and the total score of the answers is calculated, A frailty health checkup system is disclosed that outputs the results of a frailty health checkup based on the results and the results of a balance measurement test in a standing posture.

JP2021-177282A Patent No. 6943395

However, as wearable device technology has developed in recent years, a method for determining frailty based on daily activities is desired.

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

An information processing system according to an 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, and generates biometric data and frailty index information from the measurement data. A response indicating how the biological data responds to the occurrence of the exercise intensity above the predetermined value, based on change information in the biological data within a determination period whose reference time information is the time when the exercise intensity above the predetermined level occurred. at least recovery index information that indicates how the patient has recovered from the response caused by the occurrence of an exercise intensity of the predetermined level or more, based on sex index information or change information of biological data from a response state or a motion state to a resting state. frailty index information generation that generates frailty index information for indicating the frailty state of the user based on correspondence information stored in advance with at least either the responsiveness index information or the resilience index information. It is equipped with a section and a section.

A server in one aspect of the present disclosure is a server that receives measurement data from a measurement device worn by a user via a network, generates biological data and frailty index information from the measurement data, and is a server that receives measurement data from a measurement device worn by a user via a network, and generates biological data and frailty index information from the measurement data, Responsiveness index information indicating how the biological data responds to the occurrence of exercise intensity equal to or higher than the predetermined level, based on change information of the biological data within a determination period whose reference time information is the time when the exercise occurred; Alternatively, based on information on changes in biological data from a response state or a motion state to a resting state, at least one of recovery index information is generated that indicates how the body has recovered from the response caused by the occurrence of exercise intensity exceeding the predetermined level. and a frailty index information generation unit that generates frailty index information for indicating the frailty state of the user based on correspondence information stored in advance with at least either the responsiveness index information or the resilience index information. Be prepared.

An information processing method according to an aspect of the present disclosure is an information processing method that receives measurement data from a measurement device worn by a user via a network, generates biological data and frailty index information from the measurement data, and comprises: The index information generation unit determines how the biometric data changes with respect to the occurrence of the exercise intensity exceeding the predetermined level, based on change information of the biometric data within the determination period whose reference time information is the time when the exercise intensity exceeds the predetermined level. Based on responsiveness index information indicating whether the patient is responding or information on changes in biological data from a responsive state or a working state to a resting state, how the patient is recovering from the response caused by the occurrence of exercise intensity exceeding the predetermined level is determined. frailty index information for indicating a frailty state of the user based on correspondence information stored in advance with at least either the responsiveness index information or the resilience index information; generating.

A program according to an aspect of the present disclosure causes an information processing system to execute an information processing method for receiving measurement data from a measurement device worn by a user via a network, and generating biological data and frailty index information from the measurement data. In the program, the information processing method is configured to cause a frailty index information generation unit to perform an exercise that exceeds the predetermined level based on change information of biological data within a determination period in which the time when the exercise intensity of the predetermined level or higher occurs as reference time information. Based on responsiveness index information indicating how biological data responds to the occurrence of intensity, or information on changes in biological data from a response state or a motion state to a resting state, the exercise intensity of the predetermined or higher Generating at least one of resilience index information indicating how the response has been recovered from the response caused by the occurrence, based on at least one of the responsiveness index information or the resilience index information and correspondence information stored in advance, The method includes the step of generating frailty index information for indicating a frailty state of the user.

According to the present disclosure, the information processing system according to the present embodiment generates frailty index information particularly related to the frailty state of the user via the frailty index information generation unit 133 based on measurement data or biometric data. This makes it possible to understand, for example, the frailty state of the subject.

FIG. 1 is a block configuration diagram showing an information processing system according to an embodiment of the present disclosure. 2 is a diagram showing the hardware configuration of the management server 100 in FIG. 1. FIG. 3 is a block diagram illustrating functions of a storage unit 120 and a control unit 130 in FIG. 2. FIG. 4 is a schematic diagram showing an example of tag information associated with the measurement data of FIG. 3. FIG. 2 is a diagram for explaining an example of an electrocardiogram waveform and a pulse waveform measured by the measuring device 300 of FIG. 1. FIG. 2 is a flowchart illustrating an example of processing of the information processing system 1 of FIG. 1. FIG.

The contents of the embodiments of the present invention will be 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, and generates biological data and frailty index information from the measurement data, the system comprising:
Indicates how the biological data responds to the occurrence of exercise intensity greater than the predetermined value based on change information of the biological data within a determination period whose reference time information is the time when the exercise intensity greater than the predetermined value occurs. Responsiveness index information, or recovery index information indicating how the response is recovered from the response caused by the occurrence of exercise intensity exceeding the predetermined level, based on information on changes in biological data from a responsive state or a working state to a resting state. frailty index information that generates at least one of the responsiveness index information and the resilience index information, and generates frailty index information for indicating the frailty state of the user based on correspondence information stored in advance with at least one of the responsiveness index information or the resilience index information. A generation unit;
An information processing system characterized by:
[Item 2]
The frailty index information generation unit uses cardiovascular related information including at least one of heart rate information and blood pressure information as biological data within the determination period.
The information processing system according to item 1, characterized in that:
[Item 3]
The frailty index information generation unit performs clustering based on exercise intensity information indicating an exercise intensity associated with at least one of the responsiveness index information and the recovery index information, and generates correspondence for each clustered exercise intensity information. generating the frailty index information based on related information;
The information processing system according to item 1 or 2, characterized in that:
[Item 4]
The exercise intensity is an index based on data obtained from an inertial sensor of the measuring device,
The information processing system according to any one of items 1 to 3, characterized in that:
[Item 5]
A server that receives measurement data from a measurement device worn by a user via a network, and generates biological data and frailty index information from the measurement data,
Indicates how the biological data responds to the occurrence of exercise intensity greater than the predetermined value based on change information of the biological data within a determination period whose reference time information is the time when the exercise intensity greater than the predetermined value occurs. Responsiveness index information, or recovery index information indicating how the response is recovered from the response caused by the occurrence of exercise intensity exceeding the predetermined level, based on information on changes in biological data from a responsive state or a working state to a resting state. frailty index information that generates at least one of the responsiveness index information and the resilience index information, and generates frailty index information for indicating the frailty state of the user based on correspondence information stored in advance with at least one of the responsiveness index information or the resilience index information. A generation unit;
A server characterized by:
[Item 6]
An information processing method that receives measurement data from a measurement device worn by a user via a network, and generates biological data and frailty index information from the measurement data, the method comprising:
The frailty index information generation unit determines how the biometric data changes with respect to the occurrence of exercise intensity exceeding the predetermined level, based on information on changes in biometric data within a determination period whose reference time information is the time when the exercise intensity exceeds the predetermined level. How the body recovers from the response caused by the occurrence of exercise intensity above the predetermined level, based on responsiveness index information indicating whether the body is responding to or information on changes in biological data from the response state or motion state to the resting state. a frailty index for indicating a frailty state of the user based on correspondence information stored in advance with at least either the responsiveness index information or the resilience index information; generating information;
An information processing method characterized by:
[Item 7]
A program that causes an information processing system to execute an information processing method for receiving measurement data from a measurement device worn by a user via a network and generating biological data and frailty index information from the measurement data, the program comprising:
The information processing method includes:
The frailty index information generation unit determines how the biometric data changes with respect to the occurrence of exercise intensity exceeding the predetermined level, based on information on changes in biometric data within a determination period whose reference time information is the time when the exercise intensity exceeds the predetermined level. How the body recovers from the response caused by the occurrence of exercise intensity above the predetermined level, based on responsiveness index information indicating whether the body is responding to or information on changes in biological data from the response state or motion state to the resting state. a frailty index for indicating a frailty state of the user based on correspondence information stored in advance with at least either the responsiveness index information or the resilience index information; generating information;
A program characterized by:

Embodiments of the present disclosure will be described below with reference to the drawings. Note that the embodiments described below do not unduly limit the content of the present disclosure described in the claims. Furthermore, not all components shown in the embodiments are essential components of the present disclosure. Furthermore, features shown in each embodiment can be applied to other embodiments as long as they do not contradict each other.

(Embodiment 1)
<Configuration>
FIG. 1 is a block configuration diagram showing an information processing system 1 according to Embodiment 1 of the present disclosure. For example, the information processing system 1 receives user measurement data from the measurement device 300 via the network NW at the management server 100, generates biometric data by performing predetermined calculations on the measurement data, This is a system that generates frailty index information based on the biometric data.

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

The management server 100 is, for example, a device that receives user measurement data from the measurement device 300 via the network via the user terminal device 200, and performs calculations from the measurement data to biometric data. It is composed of server devices provided.

The user terminal device 200 is an information processing device owned 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, etc., or to display information generated based on biological data (details will be described later). User information such as the user's identification number, date of birth, gender, height, and weight is registered in the user terminal device 200 in advance, and the user information including the age calculated from the date of birth is associated with the measurement data. and transmits it to the management server 100 via the network NW.

Further, the user terminal device 200 may allow the user to input 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, such as "running" when the user is running, "mealing" when the user is eating, and the like. In this case, the user terminal device 200 transmits the measurement data received from the measurement 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 user's measurement data, and this measuring device 300 measures, for example, the user's electrocardiogram, pulse wave, skin temperature (body temperature), acceleration, and angular velocity data using a known method. This is a device for making measurements at periodic timing. The predetermined period may be set in advance, or may be set arbitrarily by the user. More specifically, for example, a temporal period in units of seconds may be set, or a frequency may be similarly set. Note that the user of the user terminal device 200 and the user (person to be measured) of the measuring device 300 may be the same or different.

As a specific example of the configuration of the measurement device 300, it may be configured with a device that brings two electrodes into contact with the skin and acquires the electrocardiogram as electrocardiogram waveform data based on the time change of the difference in detected potential. The radio waveform may be data obtained by galvanic skin reaction. In addition, LEDs that emit green, red, and infrared light irradiate the skin, and the time changes in the intensity of the light received by the photodiode detect pulse waves due to changes in blood vessel volume caused by the user's heartbeat. 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 volumetric pulse waveform. Alternatively, the device may be configured to acquire the user's skin temperature as data using a temperature sensor that is brought into contact with the user's skin. Alternatively, it may be configured with a three-axis acceleration sensor that detects the variation state of each of the orthogonal XYZ axes, and the measurement device 300 is attached to the user's wrist or arm. In this case, the measurement device 300 acquires acceleration data as an acceleration that is a combination of the swing of the wrist, arm, etc., and the movement of the whole body. Furthermore, it may be configured with a gyro sensor (angular velocity sensor) that detects the rotational angular velocity in each of the orthogonal XYZ axes, and acquires the user's motion as angular velocity data. If so, the measuring device 300 acquires angular velocity data as an angular velocity that is a combination of the rotation of the wrist, arm, etc., and the movement of the whole body.

Bluetooth (registered trademark), Near Field radio Communication (NFC), Afero (registered trademark), Zigbee (registered trademark), Z-Wave (registered trademark) are used between the user terminal device 200 and the measurement device 300. ) or wireless LAN. Note that a wired connection may be used instead of such wireless connection. Further, the user terminal device 200 and the measuring device 300 may be an integrated device; for example, the measuring device 300 may be equipped with a SIM to provide a communication function so as to be able to directly communicate with the management server 100. Good too.

There is one or more user terminal devices 200, which are connected to the network NW for the number of users using the measurement device 300. There is one or more measuring devices 300, and the measuring devices 300 are connected to the same number of user terminal devices 200 used by one user. When one user uses a plurality of measurement devices 300, the plurality of measurement 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. FIG. 3 is a block diagram illustrating the functions of the storage section 120 and the control section 130. Note that the illustrated configuration is an example, and other configurations may be used.

The management server 100 includes a communication section 110, a storage section 120, a control section 130, and an input/output section 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 communicates according to a communication protocol such as TCP/IP (Transmission Control Protocol/Internet Protocol).

The storage unit 120 stores programs, input data, etc. for executing various control processes and functions within the control unit 130, and is composed of RAM (Random Access Memory), ROM (Read Only Memory), etc. Ru. Further, as shown in FIG. 3, the storage unit 120 includes a measurement data DB 121 that stores measurement data obtained by the measurement device 300 in association with user information, and a measurement data DB 121 that associates biometric data calculated and generated from the measurement data with the user information. a frailty index information DB 123 that stores frailty index information generated based on measurement data or biometric data in association with user information; and a user information DB 124 that stores user information including user identification information. remember. Further, the user identification information may be account information generated by the data management unit 131, application identification information of an application downloaded to the user terminal device 200, or user terminal identification information of the user terminal device. good. 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 a part of the above-mentioned DB 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 biological data generation unit 132, a frailty index information generation unit 133, and a data output unit 134.

The data management unit 131 sets user identification information for each user who uses the measuring device 300. For example, if the user identification information is account information, the account information is generated when the user who uses the measurement device 300 registers the account information with the user terminal device 200. Therefore, the data management unit 131 controls whether the user terminal device 200 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, biological data, and frailty index information in a corresponding DB in association with user information including user identification information. Further, at this time, the data management unit 131 may associate the measurement data with predetermined tag information and store the associated measurement data.

FIG. 4 is a schematic diagram showing an example of tag information associated with the measurement data of FIG. 3. 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, and for example, the time information when the measuring device 300 measured the data D1, or the time information when the data D1 was transmitted from the measuring device 300 to the user terminal device 200 is chronologically 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 seconds and 144 milliseconds. Such time information can be obtained from communication logs. This makes it possible to understand which time period the measurement data is from.

Note that such association of measurement data using tag information is not limited to time information, and may also be performed by having the user freely write in physical information or behavioral information indicating the user's physical condition or behavior and storing it as tag information. Okay, let the user select from predetermined options (for example, in response to the question "How is your current physical condition?", let the user select one of "1: good, 2: average, 3: bad") etc.), and the selected answer may be stored. Furthermore, based on other measurement data and biological data (e.g., walking speed information, stride length information, movement information of the wearing part, posture information, center of gravity position information, heart rate information, etc.), predetermined actions (e.g., sitting, standing, etc.) , lying down, walking, running, sleeping, waking up, going to bed, eating, driving, resting, etc.) may be estimated by a known method and associated with biological data etc. as tag information (behavior type information). At this time, for example, the learning model may be estimated using a learning model learned based on the teacher measurement data, or the learning model may be personalized by performing additional learning based on the results recorded by the user described above. As a result, when the control unit 150 generates biometric data such as blood pressure information, by associating the tag information with the biometric data, it is possible to generate more accurate biometric data, and the biometric data is generated based on the associated tag information. The frailty indicator information provided can also be more personalized data.

The biometric data generation unit 132 performs predetermined calculations on the measurement data stored in the measurement data DB 121 to generate biometric data. In the generation of biometric data, new biometric data may be generated based on one or more previously generated biometric data that is not limited to data that is directly generated by calculating measurement data. The biological data includes, for example, the user's blood pressure information, heart rate information, blood oxygen amount information, maximum oxygen uptake information, electrocardiogram information, breathing rate, body temperature information, blood sugar level information, step count information, stride length information, center of gravity position information, Data such as posture information, activity type information, stress information, exercise amount information, exercise load information, movement distance information, walking speed information, activity amount information, movement information of hands or legs, etc., and is obtained from measurement data using known methods. However, it is not limited to these. The biometric data generated by the calculation is stored in the biometric data DB 122.

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

Here, a method for calculating systolic blood pressure and diastolic blood pressure, which are biological data, from measurement data will be exemplified. FIG. 5 is a diagram for explaining an example of an electrocardiogram waveform and a pulse wave measured by the measuring device 300 in FIG. The app shows a photoelectric volume pulse waveform, a velocity pulse waveform obtained by the first differentiation of the photoelectric volume pulse waveform with respect to time, and an acceleration pulse waveform obtained by the second order differentiation of the photoelectric volume pulse waveform with respect to time. FIG. 5 shows, from top to bottom, an electrocardiographic waveform, a photoelectric volume pulse waveform, a velocity pulse waveform, and an acceleration pulse waveform. The vertical axis indicates the intensity of each waveform, and the electrocardiographic waveform and photoelectric plethysmographic waveform are expressed in mV, which indicates the electric potential. The horizontal axis shows the passage of time, and the passage of time is shown from left to right.

An electrocardiogram waveform is a waveform that shows periodic changes in the electrical signals that cause a person's heart to beat. The electrocardiographic waveform is assigned names such as 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, the R wave, and the S wave represent the state of ventricular contraction, and the T wave represents the start of ventricular expansion.

The photoelectric volume pulse waveform is a waveform that shows changes in blood pressure and volume within the peripheral vascular system as the human heart beats. In the photoelectric volume pulse waveform, names of A wave, P wave, V wave, and D wave are assigned to the inflection points of the shape, respectively, indicating one cycle of a heartbeat. Using the A wave as the reference point at the time when the arterial pulse wave occurs, the P wave is a percussion wave (shock wave) generated by left ventricular ejection, the V wave is a Valley wave (wave due to overlapping bulge) generated when the aortic valve closes, and the D wave. indicates a dicrotic wave (overlapping wave) which is a reflected vibration wave.

The velocity pulse waveform is obtained by first-order differentiation of the photoelectric volume pulse waveform with respect to time. The acceleration pulse waveform is the first-order differential of the velocity pulse waveform with respect to time, that is, the second-order differential of the photoelectric volume pulse waveform. As shown in Figure 5, the accelerated pulse waveform includes wave a (positive wave in early systole), wave b (negative wave in early systole), wave c (re-rising wave in mid-systole), and d wave (systolic wave) at each peak of the waveform. The names are assigned as follows: late re-downward wave), e wave (diastolic early positive wave), and f wave (diastolic 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. The main components of blood vessels are vascular endothelium, elastic fibers, proteins, and smooth muscles. Each of these components has different properties, and Collagen and Elastin have a strong influence on the elasticity of blood vessels during systolic blood pressure and systolic blood pressure, respectively. Therefore, the elasticity that varies depending on the blood pressure value can be expressed by the parameters of (b/a), which is the ratio of the intensity of the b wave to the intensity of the a wave, and (f/e), which is the ratio of the intensity of the f wave to the intensity of the e wave. These values also vary depending on age, gender, and environmental variables. Therefore, the values of (b/a) and (f/e) can be calculated as characteristic information of the accelerated pulse waveform.

As shown in FIG. 5, the time equal to the difference between the time Tr when the R wave occurs and the time Tp when the P wave occurs becomes the ventricular systolic pulse wave propagation time PTT_SYS. The difference between the time Tt when the T wave occurs and the time Td when the D wave occurs becomes the ventricular diastolic pulse wave propagation time PTT_DIA. That is, from the R-wave time Tr and T-wave time Tt of the electrocardiogram waveform, the T-wave time Tp and the D-wave time Td of the photoelectric pulse waveform, as shown in equations (1) and (2), Then, the ventricular systolic pulse wave propagation time PTT_SYS and the ventricular diastolic pulse wave propagation time PTT_DIA can be calculated.

PTT_SYS=Tp-Tr...(1)

PTT_DIA=Td-Tt...(2)

The first electrode and the second electrode that measure the electrocardiogram waveform and the optical sensor module that measures the photoelectric volume pulse waveform are placed opposite to each other across the wrist, and by separating the placement distance, the electrocardiogram waveform detection site and the photoelectric sensor module are placed opposite each other across the wrist. This means that the measurement site of the volume pulse waveform is separated. Therefore, by creating a time lag between the respective characteristic waveforms, it is possible to lengthen the absolute calculation time of the ventricular systolic pulse wave propagation time PTT_SYS and the ventricular diastolic pulse wave propagation time PTT_DIA. Therefore, when obtaining change information on the ventricular systolic pulse wave propagation time PTT_SYS and the ventricular diastolic pulse wave propagation time PTT_DIA, the accuracy of the change information can be improved.

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

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

L/T_PTT=√(E・h/(2・r・ρ)) ...(3)

The parameters in equation (3) are: L: distance between measurements, T_PTT: pulse wave propagation time, r: blood vessel inner diameter, E: longitudinal elastic modulus of blood vessel, h: blood vessel thickness, and ρ: blood density.

It is known that there is a correlation between longitudinal elastic modulus and blood pressure values.

E=E ₀・exp(α・P)...(4)

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

From equations (3) and (4),

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

can be derived. ln indicates natural logarithm. At this time, since "r·ρ" is proportional to the blood volume at the measurement site, it can be indicated by the high value (Vp, Vd) shown by the photoelectric plethysmogram. Moreover, since "E ₀ · h" is a value proportional to the elasticity of the blood vessel, it can be replaced using (b/a) and (f/e), which are parameters indicating 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 equations (6) and (7).

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

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

A1 to A8 are constants determined by conditions. The systolic blood pressure BP_SYS, which can be calculated using equation (6), is the natural logarithm of the ventricular systolic pulse wave propagation time PTT_SYS multiplied by a constant A1, and the natural logarithm of the P wave intensity Vp multiplied by a constant A2. It can be determined by the sum of the natural logarithm of (b/a) multiplied by the constant A3 and the constant A4. The diastolic blood pressure BP_SYS, which can be calculated using equation (7), is the natural logarithm of the ventricular systolic pulse wave propagation time PTT_DIA multiplied by a constant A5, and the natural logarithm of the D wave intensity Vd multiplied by a constant A6. It can be determined by the sum of the natural logarithm of (f/e) multiplied by the constant A7 and the constant A8. Systolic blood pressure BP_SYS and diastolic blood pressure BP_DIA can be determined by determining each constant based on the characteristics of the device, the person to be measured, and the like. However, when checking the change status of systolic blood pressure BP_SYS and diastolic blood pressure BP_DIA, it is not necessary to finalize all the constants, and while using provisional values instead, information on systolic blood pressure BP_SYS and diastolic blood pressure BP_DIA can be used as information. It is possible to obtain the value. 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 influence of blood density. Further, the natural logarithm of (b/a) and the natural logarithm of (f/e) are terms that take into consideration the influence of the longitudinal elastic modulus of the artery wall. Therefore, depending on the measurement conditions, information regarding the systolic blood pressure BP_SYS and information regarding the diastolic blood pressure BP_DIA may be calculated by selecting one of the terms and making the other terms constant.

Furthermore, the biometric data generation unit 132 can obtain temperature information as biometric data from skin temperature information of the subject measured by a temperature sensor (thermistor, etc.) of the measuring device 300 worn by the subject, for example. .

Furthermore, a method for calculating walking speed information, which is biological data, from measurement data will be exemplified. For example, known calculation methods may be used alone or in combination (for example, averaging, weighting, etc.) from the waveform data of acceleration data measured by the measurement device 300 worn on the user's wrist. Walking speed information can be obtained, for example, by integrating acceleration data at predetermined time intervals. In addition, the user terminal device 200 or the measuring device 300 is equipped with a GPS function, and more accurate walking speed information can be obtained by referring to walking speed information calculated from GPS location information and time information related to the location information. You may also obtain However, especially when the location is indoors or underground, or when the travel distance is relatively short (for example, within a few meters), GPS accuracy generally decreases. It may be possible to select whether to use the information in combination (or to use it in place of the calculation by the acceleration sensor), or the user terminal device 200 may use the GPS reception sensitivity information or the distance calculated by the GPS or the acceleration sensor. Based on the information, it may be possible to select whether to use the information from the GPS. By calculating walking speed information in this way, it is possible to easily (especially on a daily basis) measure ``walking speed'', which is one of the known indicators for measuring human muscle mass, which is necessary to understand for frailty prevention etc. Is possible. Additionally, smartphones and mobile phones have acceleration sensors, but since these devices are difficult to identify where the body is moving, measurement devices such as wearable devices that are worn on the user's wrist, etc. 300, it is possible to easily track behavioral information.

Furthermore, a method for calculating step length information, which is biological data, from measurement data will be exemplified. For example, known calculation methods may be used alone or in combination (for example, averaging, weighting, etc.) from the waveform data of acceleration data measured by the measurement device 300 worn on the user's wrist. For example, since we wave our hands like a pendulum when we walk, we can obtain step length information using Since the interval between steps can be determined based on the timing at which the acceleration component in the direction perpendicular to the direction is the smallest or the timing at which the vertical direction changes, etc., step length information can be obtained by using time information. Alternatively, for example, when kicking off the ground, acceleration components in the kicking direction are combined, so it is also possible to determine the interval between one step based on the generation timing of the acceleration component in the direction. By calculating stride length information in this way, it is possible to accurately grasp the timing at which stride length begins to shorten and the degree of variation in stride length, which is useful for estimating the degree of progression of dementia. Furthermore, the decline in the sense of balance may be calculated by calculating the amount of left and right sway (for example, sway width, sway speed, etc.) during walking from the above-mentioned acceleration data.

Furthermore, the amount of exercise information calculated based on the distance information calculated by the above-mentioned GPS and acceleration sensor, the above-mentioned stride length information and step count information (for example, based on the information from the acceleration sensor, which can be acquired by a known method), etc. It may be calculated as data.

In addition, the biological data generation unit 132 determines the location where the measuring device 300 is worn (for example, the wrist, ankle, etc.) from the acceleration data and angular velocity data measured by the measuring device 300 worn by the subject, for example. Motion information such as how fast and at what angle the animal is moving can be obtained as biometric data.

In addition, the biological data generation unit 132 performs frequency analysis on the acceleration data measured by the measuring device 300 worn by the person being measured, and for example, the height of the frequency is associated with the height of the activity frequency, and the Activity amount information can be obtained as biological data by calculating based on predetermined conditions such as what percentage of the day the activity occupies.

Furthermore, the biological data generation unit 132 can identify acceleration data during exercise, including walking, using a known calculation method, for example, from acceleration data measured by the measuring device 300 worn by the subject. The amount of exercise information can be obtained as biological data by calculating it under predetermined conditions using, for example, frequency analysis. Further, by further using additional information such as angular velocity information, it is possible to obtain more accurate momentum information.

The biological data generation unit 132 also generates, for example, heart rate information that increases with exercise load, for example, with respect to the activity amount information and the exercise amount information derived from acceleration data measured by the measuring device 300 worn by the subject. By weighting the information, exercise load amount information can be obtained as biological data. In addition, for example, if vector information of acceleration data is taken into consideration, state information such as walking environment (slope, stairs, etc.) and posture (standing position, sitting position, etc.) can also be specified, and thus the state information may be further used. Further, by further using additional information such as angular velocity information, it is possible to obtain more accurate exercise load amount information.

Furthermore, the biological data generation unit 132 uses the heart rate information to generate maximum oxygen uptake information using a known formula such as VO ₂ max = 15 x (220 - age) ÷ heart rate (particularly resting heart rate). It can be obtained as biological data.

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

The frailty index information generation unit 133 generates information on changes in biological data (especially circulatory system related information such as heart rate information and/or blood pressure information) within a determination period whose reference time information is the time when an exercise intensity of a predetermined level or higher occurs. Based on this, responsiveness index information indicating how biological data responds to the occurrence of exercise intensity exceeding a predetermined level is generated, and/or biological data from a response state or movement state to a resting state (especially , heart rate information, and/or blood pressure information), recovery index information is generated that indicates how the exercise body has recovered from a response caused by an exercise intensity exceeding a predetermined level. Then, the frailty index information generation unit 133 performs clustering based on exercise intensity information indicating exercise intensity associated with at least either responsiveness index information or recovery index information. Then, the frailty index information generating unit 133 generates at least one of responsiveness index information or recovery index information and reference frailty index information (for example, there can be at least two or more classifications, For example, the responsiveness index information or recovery of the subject is determined by referring to the correspondence information stored in advance (including information obtained by machine learning), which may include at least the healthy group and the frail group. Based on at least one of the gender index information, frailty index information for indicating the frailty state of the user is determined. The frailty index information may, for example, indicate a probability value such as which category of the standard frailty index information has a higher probability, or indicate one classification finally determined from the probability value etc. It may be information.

The frailty index information generation unit 133 evaluates the frailty state of the user according to the determined frailty index information and generates evaluation information (for example, frailty symptom evaluation information (healthy or frail, etc.) or determines frailty symptoms). As supporting reference information, frailty symptom reference evaluation information such as whether it is positive or negative (which may correspond to the above-mentioned standard frailty index information) may be generated. The frailty state of the user may be evaluated for each exercise corresponding to each exercise intensity using the frailty index information as is, or alternatively, or in addition to this, the frailty index determined for each exercise within a predetermined period of time may be used. The information can be used as is (especially if it is a probability value), or it can be scored (e.g. 0 to indicate a healthy group, 1 to indicate a frail group, etc.), such as the average value or total value within a predetermined period of time. The standard value that corresponds to the aggregate value such as value (for example, if it is an average value, it is a half value such as 0.5, if it is a total value, it is half the value of the total value when all values are 1, or the value collected before the comparison) Evaluation information may be generated by comparing the median value, mode value, etc. of reference data, and evaluating the user's frailty state in a predetermined period according to the comparison result. At this time, the aggregated value of the scoring data of all frailty index information within a predetermined period may be used for comparison with the reference value, or the value less than the first quartile or the third quartile may be used for comparison with the standard value. You may use the aggregated value of the scoring data, excluding at least one of the large values or excluding outliers.

The exercise intensity may be clustered (classified) based on change information (particularly transition information) of acceleration data and angular velocity data obtained from an inertial sensor (acceleration sensor, gyro sensor) of the measuring device 300, for example. Clustering (classification) based on at least one of walking speed information, stride length information, exercise amount information, movement information of the wearing part, activity amount information, and exercise load information generated using acceleration data and angular velocity data. Good too. Alternatively, after clustering, an index expressed by a numerical value, classification, rank, etc. depending on the degree of exercise may be set. For example, the behavior information estimated from the above-mentioned behavior information and the index of exercise intensity may be associated. The index may be set based on the index, or it may be converted into a known index (for example, METS).

As a more specific example of a clustering method of exercise intensity, for example, at least one of the amplitude, frequency component, and stationarity of measurement data (particularly data obtained from an inertial sensor) or biological data signal for each predetermined time is used. A method of clustering using feature information may also be used. Alternatively, for example, correlation values between signal information of measurement data (particularly data obtained from an inertial sensor) or biological data or power spectral densities in a certain time window frame width (for example, the above-mentioned determination period may be used) A method may also be used in which items having a correlation value greater than or equal to a predetermined value are clustered. Alternatively, it may be calculated using a learning model in which machine learning is performed using the entire signal waveform of measurement data (particularly data obtained from an inertial sensor) or biological data, or each predetermined time window frame as a feature.

The determination period is, for example, a period set at least before or after the reference time, and may be arbitrarily set in advance, such as several minutes, tens of seconds, or several seconds before or after the reference time, or may be set before or after the reference time. The period set may be set differently, or may be set to several minutes, tens of seconds, several seconds, etc. after the reference time.

A response state is a state in which an exercise intensity exceeding a predetermined value is occurring, or a state in which biological data (particularly circulatory system-related information such as heart rate information and/or blood pressure information) is a predetermined value or more, or a state in which fluctuation information is greater than a predetermined value. This is a state in which the fluctuation shown is greater than or equal to a predetermined value.

The action state is a state in which action type information indicates some kind of action.

A resting state is a state in which the exercise intensity is below a predetermined value, or a state in which biological data (especially circulatory system-related information such as heart rate information and/or blood pressure information) is less than a predetermined value, or a state in which fluctuation information is less than a predetermined value. This is a state in which the fluctuation indicated by is less than a predetermined value.

As correspondence information, for example, a correspondence between at least one of responsiveness index information or recovery index information and frailty index information is stored for each clustered exercise intensity information according to the results of a prior experiment. Good too. As a more specific example, subjects are classified based on a known standard frailty index such as the J-CHS standard or the Rockwood frailty index, and measurement data of the measurement device 300 worn by the subject or biological data calculated from the measurement data is classified. The storage unit 120 stores the correspondence relationship between the exercise intensity information based on the above and at least one of the responsiveness index information or the recovery index information and the subject's reference frailty index (in particular, the correspondence relationship for each clustered exercise intensity information). In particular, it may be trained as a learning model by machine learning.

When storing the correspondence information as a learning model, a model capable of binary classification may be created using a known learning method such as SVM, decision tree, gradient boosting, or logistic regression. When using this model, whether the determined frailty index information (frailty assessment information) is positive or negative as frailty symptom information (healthy or frail) or reference information to support the judgment of frailty symptoms. It may be two estimated pieces of information such as. Furthermore, when storing the correspondence information as a learning model, a Transformer model using Self-Attention may be created. This learning model improves the architecture of the RNN system and uses the same Tensor for both input (query) and memory (key, value) in the attention structure, thereby improving the efficiency of learning on discontinuous sample information and improving the overall This improves learning performance. Using Self-Attention, it is now possible to acquire sequence structures and anaphora relationships, making it possible to efficiently learn non-continuous sample information. This Transformer structure has a function of extracting summary information of input information in an encoder section and replacing the summary information with sequence information in a decoder section. By constructing a model architecture that uses only the encoder part, such as BERT for language understanding and BEiT for image understanding, students learn to understand sequence information. When using a learning model (encoder model) created by this method, sequence information of measurement data (particularly data obtained from an inertial sensor) is processed, and vector information derived from the leading limb token is used as summary information of the sequence information. By making it possible to handle this, frailty index information (frailty evaluation information) representing the progress of frailty symptoms is determined from the vector information derived here.

The data output unit 134 outputs measurement data, biological data, frailty index information, etc. to the user terminal device 200 (here, the user may be the person being measured, or other different people (for example, close relatives, doctors, caregivers). It may be a researcher, researcher, etc.)). 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 the output data.

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

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

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

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

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 in step S104, the frailty index information generation unit 133 reads measurement data or biometric data, and generates frailty index information by referring to the correspondence information. The generated frailty index information is stored in the frailty index information DB 123 by the data management unit 131.

As the process in step S<b>105 , the data output unit 134 reads at least one of measurement data, biological data, and frailty index information, and outputs it to the user terminal device 200 .

<Effect>
As described above, the information processing system according to the present embodiment generates frailty index information particularly related to the frailty state of the user via the frailty index information generation unit 133 based on measurement data or biological data. This makes it possible to understand, for example, the frailty state of the subject.

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 changes. These embodiments and modifications, as well as 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 (7)

An information processing system that receives measurement data from a measurement device worn by a user via a network, and generates biological data and frailty index information from the measurement data, the system comprising:
Indicates how the biological data responds to the occurrence of exercise intensity greater than the predetermined value based on change information of the biological data within a determination period whose reference time information is the time when the exercise intensity greater than the predetermined value occurs. Responsiveness index information, or recovery index information indicating how the response is recovered from the response caused by the occurrence of exercise intensity exceeding the predetermined level, based on information on changes in biological data from a responsive state or a working state to a resting state. frailty index information that generates at least one of the responsiveness index information and the resilience index information, and generates frailty index information for indicating the frailty state of the user based on correspondence information stored in advance with at least one of the responsiveness index information or the resilience index information. A generation unit;
An information processing system characterized by: The frailty index information generation unit uses cardiovascular related information including at least one of heart rate information and blood pressure information as biological data within the determination period.
The information processing system according to claim 1, characterized in that: The frailty index information generation unit performs clustering based on exercise intensity information indicating an exercise intensity associated with at least one of the responsiveness index information and the recovery index information, and generates correspondence for each clustered exercise intensity information. generating the frailty index information based on related information;
The information processing system according to claim 1 or 2, characterized in that: The exercise intensity is an index based on data obtained from an inertial sensor of the measuring device,
The information processing system according to any one of claims 1 to 3, characterized in that: A server that receives measurement data from a measurement device worn by a user via a network, and generates biological data and frailty index information from the measurement data,
Indicates how the biological data responds to the occurrence of exercise intensity greater than the predetermined value based on change information of the biological data within a determination period whose reference time information is the time when the exercise intensity greater than the predetermined value occurs. Responsiveness index information, or recovery index information indicating how the response is recovered from the response caused by the occurrence of exercise intensity exceeding the predetermined level, based on information on changes in biological data from a responsive state or a working state to a resting state. frailty index information that generates at least one of the responsiveness index information and the resilience index information, and generates frailty index information for indicating the frailty state of the user based on correspondence information stored in advance with at least one of the responsiveness index information or the resilience index information. A generation unit;
A server characterized by: An information processing method that receives measurement data from a measurement device worn by a user via a network, and generates biological data and frailty index information from the measurement data, the method comprising:
The frailty index information generation unit determines how the biometric data changes with respect to the occurrence of exercise intensity exceeding the predetermined level, based on information on changes in biometric data within a determination period whose reference time information is the time when the exercise intensity exceeds the predetermined level. How the body recovers from the response caused by the occurrence of exercise intensity above the predetermined level, based on responsiveness index information indicating whether the body is responding to or information on changes in biological data from the response state or motion state to the resting state. a frailty index for indicating a frailty state of the user based on correspondence information stored in advance with at least either the responsiveness index information or the resilience index information; generating information;
An information processing method characterized by: A program that causes an information processing system to execute an information processing method for receiving measurement data from a measurement device worn by a user via a network and generating biological data and frailty index information from the measurement data, the program comprising:
The information processing method includes:
The frailty index information generation unit determines how the biometric data changes with respect to the occurrence of exercise intensity exceeding the predetermined level, based on information on changes in biometric data within a determination period whose reference time information is the time when the exercise intensity exceeds the predetermined level. How the body recovers from the response caused by the occurrence of exercise intensity above the predetermined level, based on responsiveness index information indicating whether the body is responding to or information on changes in biological data from the response state or motion state to the resting state. a frailty index for indicating a frailty state of the user based on correspondence information stored in advance with at least either the responsiveness index information or the resilience index information; generating information;
A program characterized by:

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