JP2021196707A - System, method, and program for estimating injuries and diseases - Google Patents

Abstract

To provide a system for estimating injuries and diseases which estimates the possibility that a user will get injured or sick from information when the user is doing an exercise.SOLUTION: The system for estimating injuries and diseases includes: a storage unit for storing a learning model which has learned the relation between the blood sugar of a user when the user is doing an exercise and the injuries and diseases of the user; a reception unit for receiving an input of blood sugar information on the blood sugar of a target user when the target user is doing an exercise; an estimation unit for estimating the possibility that the target user will get injured or sick, by using the blood sugar information received by the reception unit and the learning model; and an output unit for outputting information on the possibility estimated by the estimation unit.SELECTED DRAWING: Figure 1

Description

The present invention relates to an injury / illness estimation system, an injury / illness estimation method, and an injury / illness estimation program for estimating injury / illness.

Traditionally, blood glucose control has been used to maintain human health. For example, Patent Document 1 discloses a blood glucose level predictor that predicts a user's blood glucose level.

Patent No. 6659049

By the way, the increase in blood glucose level is caused by ingestion of food and adrenaline secreted from the adrenal medulla acting on the liver to decompose glycogen. It is generally known that if the blood sugar level is too high or too low, it has an adverse effect on the human body. Although the blood glucose level can be predicted by using the technique of Patent Document 1, there is a problem that it is not possible to predict what kind of effect the predicted blood glucose level will have on the human body. In addition, the effect of adrenaline secretion associated with elevated blood glucose levels on the human body cannot be predicted.

Therefore, the present invention has been made in view of the above problems, and is an injury / illness estimation system, an injury / illness estimation method, and an injury / illness estimation that can estimate an injury / illness that a user may incur by using the blood glucose level of the user. The purpose is to provide a program.

The injury / illness estimation system according to one aspect of the present invention has a storage unit that stores a learning model that learns the relationship between the user's exercise blood glucose level and the user's injury / illness, and the blood glucose level related to the exercise blood glucose level of the target user. Regarding the estimation unit that estimates the possibility that the target user will suffer injury or illness, and the possibility that the estimation unit estimates, using the reception unit that accepts the input of information, the blood glucose level information received by the reception unit, and the learning model. It is equipped with an output unit that outputs information.

Further, in the above-mentioned injury / illness estimation system, the learning model is a learning model that learns the relationship between the transition of the blood glucose level during exercise of the user and the injury / illness of the user, and the reception unit is the reception unit of the blood glucose level during exercise of the target user. It may be possible to accept the input of blood glucose level information regarding the transition.

Further, in the above-mentioned injury / illness estimation system, the learning model is a model in which the relationship between the redox potential during exercise of the user and the injury / illness of the user is further learned, and the reception unit further oxidizes during exercise of the target user. Upon receiving the input of redox potential information regarding the reduction potential, the estimation unit estimates the possibility that the target user will be injured or ill by using the blood glucose level information, the redox potential information received by the reception unit, and the learning model. May be.

Further, the injury / illness estimation system according to one aspect of the present invention includes a storage unit that stores a learning model that learns the relationship between the amount of adrenaline secreted by the user during exercise and the user's injury / illness, and the adrenaline during exercise of the target user. Using the reception unit that accepts the input of adrenaline information regarding the amount of secretion, the adrenaline information received by the reception unit, and the learning model, the estimation unit estimates the possibility that the target user will suffer injury or illness, and the estimation unit estimates. It is provided with an output unit that outputs information about the possibility.

Further, in the above-mentioned injury / illness estimation system, the adrenaline secretion amount may indicate a value obtained as a result of converting the blood glucose level measured during the exercise of the user into adrenaline by a predetermined calculation.

Further, in the above-mentioned injury / illness estimation system, the learning model is a learning model that learns the relationship between the transition of the adrenaline secretion amount during exercise of the user and the injury / illness of the user, and the reception unit is the adrenaline secretion amount during exercise of the user. It may be possible to accept the input of adrenaline information regarding the transition of.

Further, in the above-mentioned injury / illness estimation system, the storage unit stores a second learning model in which the relationship between the blood glucose level and adrenaline is learned, and acquires the acquisition unit for acquiring the blood glucose level information obtained by measuring the blood glucose level of the user. A second estimation unit that estimates the user's adrenaline secretion amount based on the blood glucose level information acquired by the unit and a learning model is provided, and the reception unit receives the adrenaline secretion amount estimated by the second estimation unit. It may be that.

Further, in the above-mentioned injury / illness estimation system, the learning model is a model in which the relationship between the redox potential during exercise of the user and the injury / illness of the user is further learned, and the reception unit further oxidizes during exercise of the target user. Upon receiving the input of redox potential information regarding the reduction potential, the estimation unit estimates the possibility that the target user will be injured or ill by using the adrenaline information and the redox potential information received by the reception unit and the learning model. May be good.

Further, in the injury / illness estimation method according to one aspect of the present invention, the computer receives the input of the blood glucose level information regarding the blood glucose level during exercise of the target user, the blood glucose level information received by the reception unit, and the user's exercise. Using a learning model that learns the relationship between the blood glucose level at the time and the user's injury or illness, an estimation step that estimates the possibility that the target user will suffer injury or illness, and an output that outputs information about the possibility that the estimation step estimated. Step and execute.

Further, the injury / illness estimation program according to one aspect of the present invention has a reception function for receiving input of blood glucose level information regarding the blood glucose level during exercise of the target user, blood glucose level information received by the reception unit, and user's exercise. Using a learning model that learns the relationship between the blood glucose level at the time and the user's injury or illness, an estimation function that estimates the possibility that the target user will suffer injury or illness, and an output that outputs information about the possibility that the estimation function estimated. Realize the function.

Further, in the injury / illness estimation method according to one aspect of the present invention, the computer receives the input of adrenaline information regarding the amount of adrenaline secreted during exercise of the target user, the adrenaline information received by the reception step, and the user's exercise. Using a learning model that learns the relationship between the amount of adrenaline secreted at the time and the user's injury or illness, the target user outputs information about the estimation step for estimating the possibility of suffering injury and illness and the possibility estimated by the estimation step. Perform the output step and.

Further, the injury / illness estimation program according to one aspect of the present invention has a reception function that accepts input of adrenaline information regarding the amount of adrenaline secreted by the target user during exercise, adrenaline information received by the reception function, and user exercise. Using a learning model that learns the relationship between the amount of adrenaline secreted at the time and the user's injury or illness, the target user outputs information on the estimation function that estimates the possibility of suffering injury and illness and the possibility that the estimation function estimates. Realize the output function.

Further, the adrenaline estimation system according to one aspect of the present invention includes a storage unit that stores a learning model that learns the relationship between the blood glucose level and adrenaline, and an acquisition unit that acquires blood glucose level information obtained by measuring the blood glucose level of the user. It is provided with an estimation unit that estimates the adrenaline secretion amount of the user based on the blood glucose level information acquired by the acquisition unit and a learning model, and an output unit that outputs the adrenaline secretion amount estimated by the estimation unit.

Further, in the adrenaline estimation method according to one aspect of the present invention, an acquisition step in which a computer acquires blood glucose level information obtained by measuring a user's blood glucose level, blood glucose level information acquired by the acquisition step, blood glucose level, and adrenaline are used. Based on the learning model that learned the relationship between, an estimation step for estimating the adrenaline secretion amount of the user and an output step for outputting the estimated adrenaline secretion amount by the estimation step are executed.

Further, the adrenaline estimation program according to one aspect of the present invention has an acquisition function for acquiring blood glucose level information obtained by measuring a user's blood glucose level, blood glucose level information acquired by the acquisition function, and blood glucose level and adrenaline. Based on the learning model in which the relationship is learned, an estimation function for estimating the adrenaline secretion amount of the user and an output function for outputting the estimated adrenaline secretion amount by the estimation function are realized.

The injury / illness estimation system according to one aspect of the present invention obtains the blood glucose level during exercise of another user by using a learning model that learns the relationship between the blood glucose level during exercise of the user and the injury / illness of the user. , It is possible to estimate the possibility that the user will be injured or sick. As a result, for example, it is possible to obtain information for improving the user's life and exercise from the estimated contents.

It is a system diagram which shows the outline of an injury / disease estimation system. It is a block diagram which shows the configuration example of the injury / disease estimation server. It is a data conceptual diagram which shows an example of teacher data for generating an injury / disease estimation learning model. It is a block diagram which shows the configuration example of the adrenaline estimation server. It is a data conceptual diagram which shows an example of teacher data for generating an adrenaline estimation learning model. It is a block diagram which shows the configuration example of a sensor. It is a figure which shows an example of the specific structure of the detection part of a sensor. It is a sequence diagram which shows the example of the exchange between each apparatus which concerns on an injury / disease estimation system. It is a flowchart which shows the operation example of a sensor. It is a flowchart which shows the operation example of the adrenaline estimation server. It is a flowchart which shows the operation example of the injury / disease estimation server. It is a figure which shows the display example of a user terminal. It is a system diagram which shows the other configuration example of an injury / disease estimation system. It is a data conceptual diagram which shows an example of teacher data for generating an injury / disease estimation learning model. It is a flowchart which shows the operation example of the injury / disease estimation server. It is a figure which shows an example of another concrete structure of the detection part of a sensor. It is a figure which shows an example of another concrete structure of the detection part of a sensor. It is a data conceptual diagram which shows an example of teacher data for generating an injury / disease estimation learning model. It is a sequence diagram which shows the example of the exchange between each apparatus which concerns on an injury / disease estimation system.

<First Embodiment>
FIG. 1 is a system diagram showing an outline of the injury / illness estimation system according to the first embodiment. As shown in FIG. 1, the injury / illness estimation system includes an injury / illness estimation server 100, an adrenaline estimation server 200, and may include a sensor 300 and a user terminal 500. The injury / illness estimation server 100, the adrenaline estimation server 200, the sensor 300, and the user terminal 500 may be communicably connected to each other via the network 400. Although one device is shown for each device shown in FIG. 1, the number of terminals and servers is not limited to one, and a plurality of devices may be used.

The injury / illness estimation server 100 is a server device (information processing device) that estimates the possibility of the user 30 suffering an injury / illness from the amount of adrenaline secreted during exercise of the user 30. The adrenaline estimation server 200 is a server device (information processing device) that estimates the adrenaline secretion amount of the user 30 from the blood glucose level (glucose secretion amount) during exercise of the user 30. The sensor 300 is a sensor that detects and outputs the amount of glucose secreted from the body fluid of the user 30. The user terminal 500 is an information processing terminal held by the user 30, and may be realized by a so-called smartphone, tablet terminal, mobile phone, PC, or the like.

The injury / illness estimation system shown in FIG. 1 is an estimation system that estimates the possibility of the user 30 having an injury or illness or the possibility of suffering an injury or illness based on the amount of adrenaline secreted during exercise of the user 30. As shown in FIG. 1, the user 30 measures the blood glucose level during exercise by the sensor 300. The sensor 300 transmits the measured sensing data 31 to the adrenaline estimation server 200. The adrenaline estimation server 200 estimates the adrenaline secretion amount of the user 30 based on the sensing data 31 indicating the blood glucose level of the user 30 that has been received. The adrenaline estimation server 200 transmits the adrenaline information 21 indicating the estimated adrenaline secretion amount to the injury / disease estimation server 100. The injury / illness estimation server 100 estimates the possibility that the user 30 suffers (or bears) the injury / illness based on the received adrenaline information 21. Then, the injury / illness estimation server 100 transmits the injury / illness estimation information 11 indicating the possibility of suffering the estimated injury / illness to the user terminal 500 of the user 30. The user terminal 500 displays the received injury / illness estimation information 11. This allows the user 30 to recognize that self-confidence may be injured.

Hereinafter, it will be described in detail. The network 400 is a network for connecting various devices to each other, and is, for example, a wireless network or a wired network. Specifically, the network includes wireless LAN (WILES LAN: WLAN), wide area network (wide area network: WAN), ISDNs (integrated service digital networks), wireless LANs, LTE (long term evolution), and LTE. 4th generation (4G), 5th generation (5G), CDMA (code division network access), WCDMA (registered trademark), Ethernet (registered trademark) and the like. The network is not limited to these examples, and the network is not limited to these examples, for example, the public switched telephone network (Public Switched Telephone Network: PSTN), Bluetooth (Bluetooth (registered trademark)), Bluetooth Low Energy, optical line, ADSL (ADSL). It may be an Asymmetric Digital Subscriber Line) line, a satellite communication network, or any other network. A network is sometimes referred to as a home network when it is provided in a user's residence. Further, the network may be, for example, NB-IoT (Narrow Band IoT) or eMTC (enhanced Machine Type Communication). NB-IoT and eMTC are wireless communication systems for IoT, and are networks capable of long-distance communication at low cost and low power consumption. Further, the network may be a combination of these. The network may also include a plurality of different networks that combine these examples. For example, the network may include a wireless network by LTE and a wired network such as an intranet which is a closed network.

<Configuration example of injury / illness estimation server 100>
FIG. 2 is a block diagram showing a configuration example of the injury / illness estimation server 100. As described above, the injury / illness estimation server 100 is a server device (information processing device) that estimates the possibility of the user suffering from injury / illness based on the amount of adrenaline secreted during exercise of the user.

As shown in FIG. 2, the injury / illness estimation server 100 includes a communication unit 110, an input unit 120, a control unit 130, a storage unit 140, and an output unit 150. The communication unit 110, the input unit 120, the control unit 130, the storage unit 140, and the output unit 150 may be configured to be communicable with each other via the bus 160.

The communication unit 110 is a communication interface having a function for executing communication with another device. The communication unit 110 may perform communication by any communication protocol as long as it can communicate with other devices, and may be communication by wire or wireless. The communication unit 110 communicates with the adrenaline estimation server 200 and the user terminal 500 according to the instruction from the control unit 130. The communication unit 110 receives, for example, the adrenaline information 21 transmitted from the adrenaline estimation server 200 via the network 400. Further, the communication unit 110 transmits, for example, the injury / illness estimation information 11 indicating that the user 30 estimated by the injury / illness estimation server 100 may suffer an injury or illness to the user terminal 500 via the network 400.

The input unit 120 is an input interface having a function of receiving input from an operator or the like of the injury / illness estimation server 100 and transmitting the input to the control unit 130. The input unit 120 may be realized by a soft key such as a touch panel, or may be realized by a hard key. Alternatively, the input unit 120 may be a microphone for receiving voice input. Further, the input unit 120 may be a port that receives input from a storage medium such as a flash memory. The input unit 120 receives, for example, a direct input of adrenaline information (for example, a numerical value of the amount of adrenaline secreted every hour) and transmits it to the control unit 130.

The storage unit 140 has a function of storing various programs and data required for operation by the injury / illness estimation server 100. The storage unit 140 can be realized by, for example, an HDD (Hard Disc Drive), an SSD (Solid State Drive), a flash memory, or the like. The storage unit 140 stores the injury / illness estimation learning model 141. The injury / illness estimation learning model 141 may be a model generated by the injury / illness estimation server 100, or may store a model generated by another device.

The injury / disease estimation learning model 141 is a learning model that learns the relationship between the transition of the adrenaline secretion amount during exercise of the user and the injury / illness affected by the user, and acquires the adrenaline secretion amount during exercise of the user. It is a model for estimating the possibility that the user has an injury or illness (or the possibility that the user has an injury or illness). The injury / illness estimation learning model 141 may output an injury / illness name that the user may incur, may output an injury / illness name that the user may incur more than a predetermined value, or may output an injury / illness name. It may output a numerical value (for example, a percentage) or an evaluation (for example, an evaluation of large, medium, small, etc.) indicating the possibility that the user bears each of them.

FIG. 3 is a data conceptual diagram showing an example of teacher data for generating an injury / disease estimation learning model 141. As shown in FIG. 3, the teacher data 1410 is information in which the subject ID 1411, the gender 1412, the weight 1413, the adrenaline data 1414, and the disease information 1415 are associated with each other. The teacher data 1410 is a set of information in which the amount of adrenaline secreted during exercise of the user and the disease information indicating the injury or illness affected by the user are associated with each other.

The subject ID 1411 is information for the injury / illness estimation server 100 to identify each teacher data on the injury / illness estimation system, and is information for identifying a user (subject).
Gender 1412 is information indicating the gender of the user indicated by the corresponding subject ID 1411.
The body weight 1413 is information indicating the body weight of the user indicated by the corresponding subject ID 1411.

The adrenaline data 1414 is information indicating the amount of adrenaline secreted during exercise of the user indicated by the corresponding subject ID 1411. This adrenaline secretion amount is information estimated from the blood glucose level of the user by the adrenaline estimation server 200. In FIG. 3, for convenience, identification information indicating information for holding adrenaline data is shown.

The disease information 1415 is information indicating an injury or illness that the user indicated by the corresponding subject ID 1411 has or is affected. The disease information 1415 may include information on a plurality of injuries and illnesses. Further, when only one injury or illness is registered as the disease information 1415, other injury or illness of the same user may be registered as different data. The disease information 1415 may further include information on the date of the disease or injury or illness. If the disease information 1415 is associated with the injury or illness currently incurred by the corresponding user, the injury or illness estimation server 100 can estimate the possibility that the user is currently injured or ill. Further, when the information on the injury or illness inflicted several years (for example, 10 years) after the date when the blood glucose level information which is the source of the corresponding adrenaline data 1414 is measured is used as the disease information 1415, the injury / illness estimation server 100 Can estimate the injuries and illnesses that the user may incur in the future.

The teacher data 1410 may have at least adrenaline data 1414 and disease information 1415, and may not have any other information. On the contrary, the information about the subject corresponding to the adrenaline data 1414 may include information other than gender and weight, and for example, information such as height, physique, and BMI (Body Mass Index) of the user is associated with the information. May be.

Returning to FIG. 2, the output unit 150 has a function of outputting the designated information according to the instruction from the control unit 130. The output by the output unit 150 may be either an image signal or an audio signal. In the case of output by an image signal, it may be output to a monitor connected to the injury / illness estimation server 100 (or included in the injury / illness estimation server 100). Further, in the case of output by an audio signal, it may be output to a speaker connected to the injury / illness estimation server 100 (or included in the injury / illness estimation server 100). The output unit 150 outputs, for example, information indicating that the user may be injured or sick.

The control unit 130 is a processor having a function of controlling each unit of the injury / illness estimation server 100. The control unit 130 may be realized by a single core or may be realized by a multi-core. The control unit 130 realizes a function as an injury / illness estimation server 100 by executing various programs stored in the storage unit 140.

The control unit 130 includes a reception unit 131 and an estimation unit 132 as functions realized by the control unit 130. Further, the control unit 130 may include a learning unit 133.

The reception unit 131 receives the adrenaline information of the user to be estimated whether or not there is a possibility of suffering an injury or illness, and transmits the adrenaline information to the estimation unit 132. The reception unit 131 receives the user's adrenaline information by being transmitted from the communication unit 110 or transmitted from the input unit 120. The reception unit 131 transmits the received adrenaline information to the estimation unit 132.

The estimation unit 132 uses the adrenaline information transmitted from the reception unit 131 as an input, and uses the injury / illness estimation learning model 141 stored in the storage unit 140, so that the user corresponding to the input adrenaline information may suffer some injury or illness. Estimate if there is sex. The estimation unit 132 may estimate the injury / illness name that the user may be affected by, and the estimation unit may have one or more injury / illness names. If there is no possibility of suffering injury or illness, the estimation unit 132 may estimate that there is no possibility of suffering injury or illness. Further, the estimation unit 132 may estimate the injury or illness most likely to be incurred by the user, or may estimate the injury or illness that the user is most likely to incur (for example, 30% or more).

The learning unit 133 learns the relationship between the amount of adrenaline secreted during exercise by the user and the injury or illness that the user suffers from. Specifically, the input of teacher data as shown in FIG. 3 is accepted, learning is performed using the disease information 1415 as a so-called label, and the injury / disease estimation learning model 141 is generated. As the learning algorithm used by the learning unit 133, an existing algorithm may be used. The learning unit 133 stores the generated injury / illness estimation learning model 141 in the storage unit 140. The learning unit 133 may perform re-learning using the injury / illness estimation learning model 141 stored in the storage unit 140 and the newly input teacher data. In addition, information on the truth of the estimation based on the injury / illness estimation information provided to the user may be accepted as feedback information and re-learning may be performed.
The above is the description of the configuration example of the injury / illness estimation server 100.

<Adrenaline estimation server 200>
FIG. 4 is a block diagram showing a configuration example of the adrenaline estimation server 200. As described above, the adrenaline estimation server 200 is a server device (information processing device) that estimates the amount of adrenaline secretion based on the blood glucose level information of the user. It is generally known that humans have an increased blood glucose level when adrenaline is secreted. In other words, it can be said that there is a close relationship between blood glucose level and adrenaline. Therefore, conversely, it is possible to estimate the amount of adrenaline secreted from the blood glucose level.

As shown in FIG. 4, the adrenaline estimation server 200 includes a communication unit 210, an input unit 220, a control unit 230, a storage unit 240, and an output unit 250. The communication unit 210, the input unit 220, the control unit 230, the storage unit 240, and the output unit 250 may be configured to be communicable with each other via the bus 260.

The communication unit 210 is a communication interface having a function for executing communication with another device. The communication unit 210 may perform communication by any communication protocol as long as it can communicate with other devices, and may be communication by wire or wireless. The communication unit 210 communicates with the injury / illness estimation server 100 according to the instruction from the control unit 230. The communication unit 210 may communicate with the sensor 300.

The input unit 220 is an input interface having a function of receiving an input from the operator of the adrenaline estimation server 200 and transmitting the input to the control unit 230. The input unit 220 may be realized by a soft key such as a touch panel, or may be realized by a hard key. Alternatively, the input unit 220 may be a microphone for receiving voice input. The input unit 220 transmits the input content input from the operator to the control unit 230. The input unit 220 receives, for example, a direct input of blood glucose level information (for example, a numerical value of the blood glucose level for each hour) and transmits it to the control unit 230.

The storage unit 240 has a function of storing various programs and data required for operation by the adrenaline estimation server 200. The storage unit 240 can be realized by, for example, an HDD (Hard Disc Drive), an SSD (Solid State Drive), a flash memory, or the like. The storage unit 240 stores the adrenaline estimation learning model 241. The adrenaline estimation learning model 241 may be generated by the adrenaline estimation server 200, or may store the one generated by another device.

The adrenaline estimation learning model 241 is a learning model that learns the relationship between the user's blood glucose level during exercise and the amount of adrenaline secreted, and is secreted by the user by acquiring the blood glucose level during exercise of the user. This is a model for estimating the amount of adrenaline secreted. The adrenaline estimation learning model 241 estimates the qualitative amount of adrenaline secreted.

FIG. 5 is a data conceptual diagram showing an example of teacher data for generating an adrenaline estimation learning model 241. As shown in FIG. 5, the teacher data 2410 is information in which the subject ID 2411, the gender 2412, the body weight 2413, the blood glucose level data 2414, and the adrenaline data 2415 are associated with each other. The teacher data 2410 is a set of information in which the amount of adrenaline secreted during exercise of the user and the disease information indicating the injury or illness affected by the user are associated with each other.

The subject ID 2411 is information for the adrenaline estimation server 200 to identify each teacher data on the injury / illness estimation system, and is information for identifying a user (subject).

Gender 2412 is information indicating the gender of the user indicated by the corresponding subject ID 2411.
The body weight 2413 is information indicating the weight of the user indicated by the corresponding subject ID 2411.

The blood glucose level data 2414 is information indicating the blood glucose level during exercise of the user indicated by the corresponding subject ID 2411. This blood glucose level may be measured by the sensor 300 or may be measured by another sensor. FIG. 5 shows identification information indicating information for holding the blood glucose level for convenience.

The adrenaline data 2415 is information indicating the amount of adrenaline secreted during exercise of the user indicated by the corresponding subject ID 2411. This adrenaline secretion amount is information estimated from the blood glucose level of the user by the adrenaline estimation server 200. In FIG. 5, for convenience, identification information indicating information for holding adrenaline data is shown.

The teacher data 2410 may have at least blood glucose level data 2414 and adrenaline data 2415, and may not have any other information. On the contrary, the information about the subject corresponding to the blood glucose level data 2414 may include information other than gender and weight, and for example, information such as height, physique, and BMI (Body Mass Index) of the user is associated with the information. It may have been. At this time, the adrenaline data 2415 used as the teacher data is a state in which adrenaline can be presumed to be secreted from the change in the time series indicated by the corresponding blood glucose level data 2414, or at the timing when the emotion is expressed. The data may be a value according to the amount, and may be information set so that the amount of adrenaline secreted is low even if the blood glucose level is high at the timing when it can be estimated that adrenaline is not secreted.

Returning to FIG. 4, the output unit 250 has a function of outputting the designated information according to the instruction from the control unit 230. The output by the output unit 250 may be either an image signal or an audio signal. In the case of output by an image signal, it may be output to a monitor connected to the adrenaline estimation server 200 (or included in the adrenaline estimation server 200). Further, in the case of output by an audio signal, it may be output to a speaker connected to the adrenaline estimation server 200 (or included in the adrenaline estimation server 200). The output unit 250 outputs, for example, adrenaline information indicating the amount of adrenaline secreted estimated from the blood glucose level of the user.

The control unit 230 is a processor having a function of controlling each unit of the adrenaline estimation server 200. The control unit 230 may be realized by a single core or may be realized by a multi-core. The control unit 230 realizes the function as the adrenaline estimation server 200 by executing various programs stored in the storage unit 240.

The control unit 230 includes a reception unit 231 and an estimation unit 232 as functions realized by the control unit 230. Further, the control unit 230 may include a learning unit 233.

The reception unit 231 receives the blood glucose level information of the user to be estimated whether or not there is a possibility of suffering an injury or illness, and transmits it to the estimation unit 232. The reception unit 231 receives the user's blood glucose level information by being transmitted from the communication unit 210 or transmitted from the input unit 220. The reception unit 231 transmits the received adrenaline information to the estimation unit 232.

The estimation unit 232 uses the adrenaline estimation learning model 241 stored in the storage unit 240 as an input of the blood glucose level information transmitted from the reception unit 231 to obtain the adrenaline secretion amount of the user corresponding to the input blood glucose level information. presume.

The learning unit 233 learns the relationship between the amount of adrenaline secreted during exercise by the user and the injury or illness that the user suffers from. Specifically, it accepts the input of teacher data as shown in FIG. 3, performs learning using the adrenaline data 2415 as a so-called label, and generates an adrenaline estimation learning model 241. As the learning algorithm used by the learning unit 233, an existing algorithm may be used. The learning unit 233 stores the generated adrenaline estimation learning model 241 in the storage unit 240. The learning unit 233 may perform re-learning using the adrenaline estimation learning model 241 stored in the storage unit 240 and the newly input teacher data.
The above is the description of the configuration example of the adrenaline estimation server 200.

<Configuration example of sensor 300>
FIG. 6 is a block diagram showing a configuration example of the sensor 300. As shown in FIG. 6, the sensor 300 includes a communication unit 310, a detection unit 320, a storage unit 340, and an output unit 350.

The communication unit 310 is a communication interface having a function for executing communication with another device. The communication unit 310 may perform communication by any communication protocol as long as it can communicate with other devices, and may be wired or wireless communication. The communication unit 310 communicates with the adrenaline estimation server 200. The communication unit 310 may communicate with the user terminal 500.
The detection unit 320 detects the blood glucose level of the user by using the body fluid (blood) of the user wearing the sensor 300.
FIG. 7 is a diagram showing an example of a specific configuration of the detection unit 320.

FIG. 7 is a schematic diagram of the main part configuration of the detection unit 320 of the first application form. The detection unit 320 includes a detection element 321 having a predetermined container shape, and the detection element 321 is connected to the measurement element 700 by wiring. A body fluid collecting unit 330 is formed on the detection element 321. The body fluid collecting unit 330 has a known needle shape, and biological fluid 322 such as blood, sweat, and saliva is collected and guided into the detection element 321 by surface tension.

The detection element 321 includes at least an identifier of glucose as a substance to be detected and a receptor for detecting the glucose concentration in the body fluid collected from the body fluid collection unit 330. Receptors are formed of self-assembled monolayers (SAMs) containing discriminants and inhibitors. SAM usually refers to an organic thin film in which organic molecules spontaneously aggregate at an interface between a solid and a liquid or an interface between a solid and a gas to spontaneously form a monomolecular film.

The identifying substance has a function of binding to glucose contained in the sample. Phenylboronic acid can be used as the identifying substance, and a phenylboronic acid derivative is particularly preferable. This phenylboronic acid derivative has a halogen group such as F (fluorine) or Cl (chlorine) or NO ₂ (nitro group) in the aromatic ring portion of the phenylboronic acid. It is preferable to use a phenylboronic acid derivative having a nitro group in the aromatic ring portion, because the reactivity with glucose is remarkable, and it is preferable to use a halogen group.

The inhibitor suppresses that a protein such as albumin, which is a non-detection target substance, binds to a phenylboronic acid derivative or adheres to the surface of the redox potential measuring electrode 326. In the case of this embodiment, the inhibitor is formed of a polymer compound. As the polymer compound, oligoethylene glycol having a longer molecular chain than the identification substance can be used, and for example, polyethylene glycol and the like can also be used.

A redox potential measuring electrode 326 and a reference electrode 329 are mounted in the detection element 321. A glucose trapping molecule 327 (phenylboronic acid derivative) for capturing glucose 323 as a substance to be detected is immobilized on the redox potential measuring electrode 326. Since the biological fluid 322 is blood (may be sweat, saliva, etc.), in addition to glucose 323 as a substance to be detected, a redox potential fluctuation factor 324 and impurities 325 are also present. The redox potential fluctuation factor 324 and impurities 325 are proteins, dust and the like. The detection unit 320 may also detect substances other than glucose through the redox potential measurement electrode 326. For this purpose, an antibody that captures the substance to be detected, an aptamer, is used as the capture molecule 327.

One end of the glucose trapping molecule 327 is electrically connected to the electrode 328. The measuring element 700 is realized by an FET as an example. Glucose in the sample binds to the glucose trapping molecule 327 (phenylboronic acid derivative) as a discriminating substance. This causes the glucose trapping molecule 327 to generate a negative charge. This negative charge charges the electrode 328. In the case of an FET, the measuring element 700 measures a change in the gate voltage that changes with the charging of the electrode 328. As a result, the detection unit 320 detects the glucose concentration contained in the sample. Alternatively, the measuring element 700 may be configured to detect the glucose concentration based on the potential difference between the reference electrode 329 and the electrode 328. The configuration of the detection unit 320 shown in FIG. 7 is only an example, and other configurations may be adopted as long as the glucose concentration can be detected.

Returning to FIG. 6, the storage unit 340 stores the sensing data 341 which is the blood glucose level information detected by the detection unit 320.

The output unit 350 outputs the sensing data 341 stored in the storage unit 340 or the blood glucose level information detected by the detection unit. The output unit 350 can be realized as an output terminal such as a USB terminal, for example.

The sensor 300 may not be provided with the communication unit 310 as long as it can output the detected blood glucose level. For example, the output unit 350 may be realized as a USB port, and the detected blood glucose level information may be connected to the user terminal 500 or the like for output. In such a case, the blood glucose level information transmitted to the adrenaline estimation server 200 may be transmitted from the user terminal 500. Further, if the blood glucose level measured by the detection unit 320 is output as it is from the communication unit 310 or the output unit 350, the storage unit 340 may not be provided.
The above is the description of the configuration example of the sensor 300.

<Operation>
FIG. 8 is a flow of injury / illness estimation by the injury / illness estimation system, and is a sequence diagram showing an example of interaction between each device related to the injury / illness estimation system.

As shown in FIG. 8, the sensor 300 is attached to the user 30 and detects the user's blood glucose level (glucose concentration in the blood) from the user's blood (step S801). The sensor 300 transmits the detected blood glucose level as blood glucose level information to the adrenaline estimation server 200 (step S802).

The adrenaline estimation server 200 estimates the amount of adrenaline secreted by the user 30 from the received blood glucose level information (step S803). The adrenaline estimation server 200 transmits the adrenaline information indicating the estimated adrenaline secretion amount of the user 30 to the injury / disease estimation server 100 (step S804).

When the injury / illness estimation server 100 receives the adrenaline information, the injury / illness estimation server 100 estimates the possibility that the user 30 will suffer the injury / illness based on the received adrenaline information (step S805). The injury / illness estimation server 100 transmits the injury / illness estimation information indicating the possibility of suffering the estimated injury / illness to the user terminal 500 (step S806).

The user terminal 500 of the user 30 displays the received injury / illness estimation information (step S807). As a result, the user 30 can recognize the possibility of suffering an injury or illness or the possibility of suffering an injury or illness.
FIG. 9 is a flowchart showing an operation example of the sensor 300 for realizing the exchange shown in FIG.

As shown in FIG. 9, the detection unit 320 of the sensor 300 measures the glucose value of the user 30 (subject) (step S901). The detection unit 320 stores the measured glucose value in the storage unit 340.

The communication unit 310 transmits the sensing data stored in the storage unit 340 to the adrenaline estimation server (step S902). The sensing data includes identification information (sensor ID) indicating the sensor 300 or identification information (subject ID) indicating the user 30 using the sensor 300.
The sensor 300 continues to execute the process shown in FIG. 9 until it is removed from the user 30 or the start switch of the sensor 300 is turned off.
FIG. 10 is a flowchart showing an operation example of the adrenaline estimation server 200 for realizing the exchange shown in FIG.

As shown in FIG. 10, the communication unit 210 of the adrenaline estimation server 200 receives the blood glucose level information indicating the blood glucose level of the user 30 transmitted from the sensor 300 (step S1001). The communication unit 210 transmits the received blood glucose level information to the control unit 230. The reception unit 231 of the control unit 230 receives the blood glucose level information transmitted from the communication unit 210 as the information of the subject who estimates the possibility of suffering an injury or illness, and transmits the information to the estimation unit 232.

When the blood glucose level information is transmitted, the estimation unit 232 estimates the adrenaline secretion amount of the user 30 by using the transmitted blood glucose level information as an input and using the adrenaline estimation learning model 241 stored in the storage unit 240. (Step S1002).

Then, the estimation unit 232 transmits the adrenaline information indicating the estimated adrenaline secretion amount to the injury / illness estimation server 100 via the communication unit 210 (step S1003), and ends the process. The adrenaline information transmitted from the adrenaline estimation server 200 to the injury / illness estimation server 100 includes identification information for identifying which user's information is. The adrenaline estimation server 200 executes the process shown in FIG. 10 every time the blood glucose level information is received.
FIG. 11 is a flowchart showing an operation example of the injury / illness estimation server 100 for realizing the exchange shown in FIG.

As shown in FIG. 11, the communication unit 110 of the injury / illness estimation server 100 receives the adrenaline information indicating the amount of adrenaline secretion estimated based on the blood glucose level of the user 30 transmitted from the adrenaline estimation server 200 (step S1101). .. The communication unit 110 transmits the received adrenaline information to the control unit 130. The reception unit 131 of the control unit 130 receives the adrenaline information transmitted from the communication unit 110 as the information of the subject who estimates the possibility of suffering an injury or illness, and transmits the information to the estimation unit 132.

When the adrenaline information is transmitted, the estimation unit 132 uses the transmitted adrenaline information as an input and uses the injury / illness estimation learning model 141 stored in the storage unit 140 to cause the user 30 to be injured or ill. Estimate the possibility of suffering an injury or illness (step S1102).

Then, the estimation unit 132 transmits the injury / illness estimation information indicating that the estimated user 30 may suffer an injury / illness to the user terminal 500 via the communication unit 110 (step S1103), and ends the process. The injury / illness estimation information transmitted from the injury / illness estimation server 100 to the user terminal 500 may include identification information for identifying which user's information is. The injury / illness estimation server 100 executes the process shown in FIG. 10 every time it receives adrenaline information.
The above is the method of estimating the possibility of suffering an injury or illness based on the amount of adrenaline secreted during exercise by the user in the injury or illness estimation system.

FIG. 12 is a diagram showing a display example of injury / illness estimation information of the user 30 displayed on the user terminal 500 or the like. FIG. 12 shows a display example when the user terminal 500 is assumed to be a smartphone.

As shown in FIG. 12, the user terminal 500 displays the injury / illness information 1212 that the user 30 who is the holder of the user terminal 500 may bear as the injury / illness estimation information. FIG. 12 shows an example of displaying the name of the injury or illness and the probability of suffering the name of the injury or illness as the injury or illness information 1212. Here, the possibility of becoming obese is 80% and the possibility of becoming diabetes is 40%. Here is an example. Further, the user terminal 500 may display the adrenaline information 1211 indicating the amount of adrenaline secreted by the user, which is the source of the injury / illness information 1212, as the injury / illness estimation information. FIG. 12 shows an example in which the transition of the amount of adrenaline secreted over time is graphically represented as the adrenaline information 1211.

<Summary of the first embodiment>
As shown in the first embodiment, the injury / illness estimation server 100 estimates the possibility that the user 30 is injured or injured based on the amount of adrenaline secreted during exercise of the user 30. Can be done. It is presumed that a large amount of adrenaline is secreted when the user 30 is exercising, especially in a tense situation, and the blood glucose level is increased accordingly. In addition, since adrenaline is involved in the increase in blood glucose level of the user, it may affect the health condition of the user. Based on the amount of adrenaline secreted and the history of the injury or illness inflicted by the user, adrenaline during exercise of other users. The amount of adrenaline secreted can be used to estimate the likelihood of other users being injured or ill. Therefore, a user who is presumed to have some kind of injury or illness can take good measures to prevent the injury or illness.

<Second Embodiment>
In the first embodiment, the possibility that the user 30 will be injured or sick is estimated based on the amount of adrenaline secreted during exercise of the user, but this is not the case. The injury / illness estimation server 100 may estimate the injury / illness directly from the blood glucose level (glucose secretion amount) instead of the adrenaline secretion amount of the user 30.

FIG. 13 is a system diagram showing a configuration example of the injury / illness estimation system according to the second embodiment. As shown in FIG. 13, the injury / illness estimation system includes an injury / illness estimation server 100, and may include a sensor 300 and a user terminal 500. As shown in FIG. 13, unlike the first embodiment, the injury / disease estimation system does not include the adrenaline estimation server 200.

In the injury / illness estimation system shown in FIG. 13, the sensor 300 directly transmits the measured sensing data (blood glucose level information) to the injury / illness estimation server 100. Then, the injury / illness estimation server 100 estimates the possibility that the user 30 will be injured or ill from the received blood glucose level information. Then, the injury / illness estimation server 100 transmits the injury / illness estimation information estimated based on the blood glucose level to the user terminal 500 of the user 30. Thus, in the second embodiment, the injury / illness estimation system estimates the possibility that the user 30 will be injured or ill from the blood glucose level during exercise of the user 30 without converting it into the adrenaline secretion amount. Hereinafter, although it will be specifically described, in the second embodiment, only the difference from the first embodiment will be described.
The configuration of the injury / illness estimation server 100 is the same as the configuration shown in FIG. On the other hand, there are differences in the functions realized by each functional unit.
First, the reception unit 131 receives the blood glucose level information during exercise of the user 30 instead of the adrenaline information during exercise of the user 30, and transmits the information to the estimation unit 132.

Further, the estimation unit 132 estimates the possibility that the user 30 will suffer an injury or illness from the blood glucose level information and the injury / illness estimation learning model instead of the adrenaline information. Here, the injury / illness estimation learning model according to the second embodiment will be described.

The injury / illness estimation learning model according to the second embodiment is a model that learns the relationship between the user's blood glucose level information during exercise and the injury / illness inflicted by the user, and inputs the blood glucose level information during exercise of the user 30. It is a model used for accepting and estimating the possibility (or possibility of suffering) of injury or illness of the user 30.

FIG. 14 is a data conceptual diagram showing a data configuration example of the teacher data 3410 used to generate the injury / disease estimation learning model according to the second embodiment. The data configuration example of the teacher data 3410 shown in FIG. 14 is substantially the same as the teacher data 1410 shown in FIG. 3, whereas the teacher data 1410 shown in the first embodiment holds the adrenaline data 1414. The second embodiment is different in that the blood glucose level data 2414 is retained in place of the adrenaline data 1414. Therefore, the model generated when learning using the teacher data 3410 shown in FIG. 14 is a model that learns the relationship between the blood glucose level and the possibility of suffering injury or illness. In the teacher data 3410, at least the blood glucose level data 2414 and the disease information 1415 may be associated with each other, and if necessary, other information may be deleted, or other information regarding the corresponding user may be obtained. May be added.

FIG. 15 is a flowchart showing an operation example of the injury / illness estimation server 100 according to the second embodiment.

As shown in FIG. 15, the communication unit 110 of the injury / illness estimation server 100 receives the blood glucose level information of the user 30 (subject) from the sensor 300 (step S1501). The communication unit 110 transmits the received blood glucose level information to the control unit 130.

The reception unit 131 of the control unit 130 receives the transmitted blood glucose level information as information for estimating the possibility that the user 30 will be injured or sick. The reception unit 131 transmits the received blood glucose level information to the control unit 130. The estimation unit 132 of the control unit 130 estimates the possibility that the user 30 will suffer an injury or illness by using the injury / illness estimation learning model 141 with the received blood glucose level information as an input (step S1502).

The estimation unit 132 generates injury / illness estimation information indicating the possibility of suffering an estimated injury / illness, transmits the generated injury / illness estimation information to the user terminal 500 via the communication unit 110 (step S1503), and ends the process. ..

In this way, the injury / illness estimation server 100 can estimate the possibility that the user 30 will be injured or ill based on the blood glucose level information, not the amount of adrenaline secreted by the user.

The user terminal 500 is the same as a general smartphone or tablet terminal, and is the same as a conventional terminal as long as it has at least a communication function and a display function for displaying data. Is omitted.

<Summary of the second embodiment>
According to the injury / illness estimation system according to the second embodiment, it is possible to estimate the possibility of the user suffering an injury / illness directly from the blood glucose level during exercise of the user.

<Supplement>
The injury / illness estimation system according to the above embodiment is not limited to the above embodiment, and each configuration may be realized by another method. Hereinafter, various modification examples will be described.

(1) In the first embodiment, in order to estimate adrenaline from the blood glucose level, it is decided to estimate using a learning model in which the relationship between the blood glucose level and adrenaline is learned, but this is not the case. It may be calculated by a predetermined function that inputs the blood glucose level and outputs the adrenaline level. At this time, the adrenaline value is calculated by weighting the timing at which adrenaline is presumed to be secreted (or the timing at which it is presumed not to be secreted) with respect to the blood glucose level data which is the time-series data. It may be estimated.

(2) The configuration of the detection unit 320 shown in the above embodiment is not limited to the configuration of FIG. 7. It may be realized by other configurations.

FIG. 16 is a schematic diagram of the main part configuration of another detection unit 320 (other item biomolecule measuring instrument). The detection unit 320 includes a detection element 321 having a predetermined container shape, and the detection element 321 is connected to the measurement element 700 by wiring. A body fluid collecting unit 330 is formed on the detection element 321. The body fluid collecting unit 330 has a known needle shape, and biological fluid 322 such as sweat and saliva on the skin surface is collected and guided into the detection element 321 by surface tension.

A redox potential measuring electrode 326 and a reference electrode 329 are mounted in the detection element 321. A glucose trapping molecule 327 (phenylboronic acid derivative) for capturing glucose 323 as a substance to be detected is immobilized on the redox potential measuring electrode 326. Since the biological fluid 322 is blood, sweat, saliva, etc., in addition to glucose 323 as a substance to be detected, redox potential fluctuation factors 324 such as proteins and dust, and impurities 325 are also present. The detection unit 320 may also detect substances other than glucose through the redox potential measurement electrode 326. For this purpose, an antibody that captures the substance to be detected, an aptamer, is used as the capture molecule 327.

In the detection unit 320 (other item biomolecular measuring instrument), the measuring element 700 is configured to measure the potential difference between the redox potential measuring electrode 326 of glucose 323 and the reference electrode 329. Therefore, the amount of glucose is estimated from the change in voltage.
Further, the detection unit 320 may be realized in the embodiment shown in FIG.

FIG. 17 is a schematic diagram of the main part configuration of another detection unit 320 (other item biomolecule measuring instrument). The detection unit 320 includes a detection element 321 having a predetermined container shape, and the detection element 321 is connected to the measurement element 700 by wiring. A body fluid collecting unit 330 is formed on the detection element 321. The body fluid collecting unit 330 has a known needle shape, and biological fluid 322 such as sweat and saliva on the skin surface is collected and guided into the detection element 321 by surface tension.

A redox potential measuring electrode 326, a reference electrode 329, and a counter electrode 331 are mounted in the detection element 321. A glucose trapping molecule (phenylboronic acid derivative) for capturing glucose 323 as a substance to be detected is immobilized on the redox potential measuring electrode 326. Since the biological fluid 322 is sweat, saliva, etc., in addition to glucose 323 as a substance to be detected, redox potential fluctuation factors 324 such as proteins and dust, and impurities 325 are also present. The detection unit 320 may also detect substances other than glucose through the redox potential measurement electrode 326. For this purpose, an antibody or aptamer that captures the substance to be detected is used as a capture molecule.

In the detection unit 320 (other item biomolecular measuring instrument), the measuring element 700 has a configuration in which the change in the amount of current of the redox potential measuring electrode 326 of glucose 323 is measured through the reference electrode 329 and the counter electrode 331. be. Therefore, the amount of glucose is estimated from the change in current. The detection unit 320 may be configured in this way.

(3) In the above-described embodiment, the injury / illness estimation system for estimating the possibility of the user suffering an injury / illness based on the adrenaline secretion amount estimated from the blood glucose level during exercise of the user or the blood glucose level has been described. Further, as a sensor for measuring the blood glucose level, an example is shown in FIGS. 7, 16 and 17.

By the way, the above-mentioned sensor can measure not only the blood glucose level but also the redox potential. Therefore, in the above-mentioned injury / illness estimation system, injury / illness estimation may be further performed using the redox potential. In this supplement, an injury / illness estimation system that can estimate the possibility of injuries / illnesses by using the redox potential will be described.

FIG. 19 is a data conceptual diagram showing an example of data composition of teacher data in this supplement. As shown in FIG. 19, the teacher data 1410 in this supplement is information in which subject ID 1411, gender 1412, weight 1413, adrenaline data 1414, redox potential data 1801, and disease information 1415 are associated with each other. be. The teacher data 1410 is a set of information in which the adrenaline secretion amount during exercise of the user, the transition of the redox potential during exercise of the user, and the disease information indicating the injury or illness affected by the user are associated with each other. Of these information, information having the same content as the information shown in the above embodiment is designated by the same reference numerals, and detailed description thereof will be omitted.

The redox potential data 1801 in the teacher data 1410 shown in FIG. 19 is information indicating the transition of the redox potential during exercise of the user indicated by the corresponding subject ID 1411. The redox potential can measure whether the state of the body fluid is in the oxidized state or the reduced state. Due to extrinsic stress and injury or illness, excessive generation of free radicals centered on active oxygen causes the body fluid to become in an oxidized state. It is known that if a high fat body or hyperglycemia continues for a long period of time, free radicals are generated in the blood vessels, damaging the vascular endothelium and causing various lifestyle-related diseases such as renal disorder, retinopathy, and periodontal disease. (Wells-Knecht, KJ et al. Biochem. 34, 3702-3709 (1995), etc.). Furthermore, in recent years, there have been reports of measuring blood redox potentials in healthy subjects and patients with metabolic syndrome and type 2 diabetes (Spanidis, Y. et al. Exp. Therap. Med. 11, 895-903 (2016). )), Compared to the blood redox potential of healthy subjects, that of metabolic syndrome patients and type 2 diabetic patients has a high redox potential, and it has been clarified that the blood is in an oxidized state. The transition of the potential can be used as a reference for estimating the injury or illness. The redox potential data 1801 is information measured in the same time series as the corresponding adrenaline data 1414. It is desirable that the time stamps related to the transition of the changes in the redox potential data 1801 and the adrenaline data 1414 are synchronized with each other, but if both are measurement data during exercise, the data are measured at different timings. May be good.

By generating a learning model using the teacher data 1410 shown in FIG. 19, this learning model is based on the transition of the redox potential during exercise of the user and the transition of the adrenaline secretion amount during exercise of the user. It is a model that can estimate the possibility that the user will suffer injury or illness. In this model, it is possible to estimate the possibility that the user will be injured or sick by inputting only one of the transition of the redox potential and the transition of the adrenaline secretion amount, but it is desirable to have both inputs.

FIG. 20 is a sequence diagram showing a procedure for estimating the possibility of a user suffering an injury or illness by the injury or illness estimation system in this supplement, and is a diagram showing an example of interaction between each device related to the injury or illness estimation system. FIG. 20 shows an example of performing estimation with a system having a configuration based on the injury / illness estimation system shown in the first embodiment.

As shown in FIG. 20, the sensor 300 detects the blood glucose level during exercise of the user wearing the sensor 300 and the redox potential (step S1901). Of the measured data, the sensor 300 transmits the pedigree information indicating the transition of the blood glucose level to the adrenaline estimation server 200 (step S802), and the redox potential information indicating the transition of the redox potential is transmitted to the injury / disease estimation server 100. (Step S1902). The blood glucose level information and the redox potential information transmitted at this time both include user identification information that can uniquely identify which user's information is and date and time information indicating when the information was measured. It's fine. The user identification information and date / time information are information for identifying that the injury / disease estimation server 100 belongs to the same user with respect to the redox potential information and the adrenaline secretion amount estimated by the adrenaline estimation server 200. The addition of the identification information and the date and time information here can also be realized by adding the same identification information unique to the system to each of the redox potential information and the blood glucose level information.

As shown in the first embodiment, the adrenaline estimation server 200 estimates the amount of adrenaline secreted from the blood glucose level information, and transmits the adrenaline information indicating the transition to the injury / disease estimation server 100 (steps S803 and S804). This adrenaline information includes information indicating the transition of the estimated adrenaline secretion amount, as well as user identification information and date and time information included in the blood glucose level information.

The injury / disease estimation server 100 receives redox potential information from the sensor 300. Further, the injury / illness estimation server 100 receives adrenaline information from the adrenaline estimation server 200. The injury / disease estimation server 100 confirms whether the received redox potential information and the adrenaline information belong to the same user by collating the user's identification information and the date / time information included in each, and then confirms that the redox potential. Input information and adrenaline information into the learning model. Then, according to the input, the estimation unit 132 of the injury / illness estimation server 100 estimates the possibility that the user will be injured (step S1903). Then, the injury / illness estimation server 100 transmits the injury / illness estimation information indicating the estimated result to the user terminal 500 (step S806).

When the user terminal 500 receives the injury / illness estimation information, the user terminal 500 displays the content thereof (step S807). As a result, the user recognizes how likely he / she is likely to be injured and what kind of injury / illness he / she is likely to suffer from the transition of the redox potential and the transition of the adrenaline secretion amount during the user's exercise. can do.

In the injury / illness estimation system in this supplement, by learning information including the transition of the adrenaline secretion amount during the user's exercise and the transition of the redox potential as teacher data, the adrenaline secretion during the user's exercise is learned. Although it is possible to estimate the possibility of suffering injury or illness based on the transition of the amount and the transition of the redox potential, the estimation using the transition of the redox potential is not limited to this. Needless to say, examples other than those shown in this supplement may be taken.

For example, in the teacher data shown in FIG. 18, the transition of the blood glucose level may be used instead of the transition of the adrenaline secretion amount. In this case, it is possible to provide an injury / illness estimation system that accepts inputs of the transition of the blood glucose level during exercise of the user and the transition of the redox potential and outputs information indicating the possibility of the user suffering an injury or illness. ..

Further, in the first and second embodiments, the estimation of the possibility of injury or illness of the user based on the transition of the adrenaline secretion amount and the transition of the blood glucose level is different from the estimation of the possibility of injury or illness of the user, based on the transition of the redox potential during exercise of the user. It may be possible to estimate the possibility that the user will be injured or sick. That is, the injury / illness estimation system estimates the possibility that the user will suffer injury / illness based on the transition of the adrenaline secretion amount during exercise of the user, and the user causes injury / illness based on the transition of the redox potential during exercise of the user. It is also possible to estimate the possibility of incurring and output the estimation results of both. Further, instead of outputting the estimation results of both, it may be possible to output the information of the injury or illness estimated that both of them are likely to suffer the same kind of injury or illness in both estimation results. Further, among the injuries and illnesses estimated to have a high possibility of injuries and illnesses based on the estimation results of both, information on the injuries and illnesses in which the possibility of injuries and illnesses is equal to or higher than a predetermined threshold value may be output.

As shown in this supplement, the blood glucose level is estimated by using the transition of the oxidation-reduction potential during the user's exercise as information other than the blood glucose level or the amount of adrenaline secreted based on the blood glucose level. Alternatively, it is possible to estimate the possibility of suffering an injury or illness that cannot be estimated only from the amount of adrenaline secretion, or to increase the probability of suffering an injury or illness estimated from the blood glucose level during exercise of the user or the amount of adrenaline secretion. be able to.

(4) In the above embodiment, as a method of estimating the possibility of suffering an injury or illness in the injury or illness estimation system, the processor of the injury or illness estimation server executes an injury or illness estimation program or the like to estimate the possibility. It may be realized by a logic circuit (hardware) or a dedicated circuit formed in an integrated circuit (IC (Integrated Circuit) chip, LSI (Large Scale Integration)) or the like in the apparatus. Further, these circuits may be realized by one or a plurality of integrated circuits, and the functions of the plurality of functional units shown in the above embodiment may be realized by one integrated circuit. LSI may be referred to as VLSI, super LSI, ultra LSI, or the like depending on the degree of integration.

The injury / illness estimation program may be recorded on a recording medium that can be read by a processor, and the recording medium may be a "non-temporary tangible medium" such as a tape, a disk, a card, a semiconductor memory, or a programmable logic circuit. Can be used. Further, the injury / illness estimation program may be supplied to the processor via any transmission medium (communication network, broadcast wave, etc.) capable of transmitting the injury / illness estimation program. That is, for example, an information processing device such as a smartphone may be used to download and execute the injury / illness estimation program from the network. The present invention can also be realized in the form of a data signal embedded in a carrier wave, in which the injury / disease estimation program is embodied by electronic transmission.

The injury / illness estimation program can be implemented using, for example, a script language such as ActionScript or JavaScript (registered trademark), or an object-oriented programming language such as Objective-C, Java (registered trademark), C ++, Python, or R.

(4) The various configurations shown in the above embodiments and supplements may be combined as appropriate.

100 Injury and illness estimation server 110 Communication unit 120 Input unit 130 Control unit 131 Reception unit 132 Estimate unit 133 Learning unit 140 Storage unit 141 Injury and illness estimation learning model 150 Output unit 200 Adrenaline estimation server 210 Communication unit 220 Input unit 230 Control unit 231 Reception unit 232 Estimating unit 233 Learning unit 240 Storage unit 241 Adrenaline estimation learning model 250 Output unit 300 Sensor 310 Communication unit 320 Detection unit 340 Storage unit 341 Sensing data 350 Output unit

Claims (15)

A storage unit that stores a learning model that learns the relationship between the user's blood glucose level during exercise and the user's injury or illness.
A reception unit that accepts input of blood glucose level information related to the blood glucose level during exercise of the target user,
Using the blood glucose level information received by the reception unit and the learning model, an estimation unit that estimates the possibility that the target user will suffer an injury or illness,
An output unit that outputs information about the possibility estimated by the estimation unit, and an output unit.
Injury and illness estimation system equipped with. The learning model is a learning model that learns the relationship between the transition of the blood glucose level of the user during exercise and the injury or illness of the user.
The injury / illness estimation system according to claim 1, wherein the reception unit accepts an input of blood glucose level information regarding a transition of the blood glucose level during exercise of the target user. The learning model is a model in which the relationship between the redox potential during exercise of the user and the injury or illness of the user is further learned.
The reception unit further receives input of redox potential information regarding the redox potential of the target user during exercise.
The claim is characterized in that the estimation unit estimates the possibility that the target user will be injured or ill by using the blood glucose level information, the redox potential information, and the learning model received by the reception unit. The injury / illness estimation system according to 1 or 2. A storage unit that stores a learning model that learns the relationship between the amount of adrenaline secreted by the user during exercise and the injury or illness of the user.
A reception unit that accepts input of adrenaline information regarding the amount of adrenaline secreted by the target user during exercise,
Using the adrenaline information received by the reception unit and the learning model, an estimation unit that estimates the possibility that the target user will suffer an injury or illness,
An output unit that outputs information about the possibility estimated by the estimation unit, and an output unit.
Injury and illness estimation system equipped with. The injury / illness estimation system according to claim 4, wherein the adrenaline secretion amount indicates a value obtained as a result of converting the blood glucose level measured during exercise of the user into adrenaline by a predetermined calculation. The learning model is a learning model that learns the relationship between the transition of the adrenaline secretion amount during exercise of the user and the injury or illness of the user.
The injury / illness estimation system according to claim 4 or 5, wherein the reception unit receives an input of adrenaline information regarding a transition of the adrenaline secretion amount during exercise of the user. The storage unit stores a second learning model in which the relationship between the blood glucose level and adrenaline is learned.
An acquisition unit that acquires blood glucose level information that measures the user's blood glucose level,
A second estimation unit that estimates the adrenaline secretion amount of the user based on the blood glucose level information acquired by the acquisition unit and the learning model is provided.
The injury / illness estimation system according to any one of claims 4 to 6, wherein the reception unit receives an adrenaline secretion amount estimated by the second estimation unit. The learning model is a model in which the relationship between the redox potential during exercise of the user and the injury or illness of the user is further learned.
The reception unit further receives input of redox potential information regarding the redox potential of the target user during exercise.
4. The estimation unit is characterized in that it estimates the possibility that the target user will be injured or ill by using the adrenaline information, the redox potential information, and the learning model received by the reception unit. The injury / illness estimation system according to any one of 6 to 6. The computer
A reception step that accepts input of blood glucose level information related to the blood glucose level during exercise of the target user,
Estimating the possibility that the target user will suffer an injury or illness using the blood glucose level information received by the reception step and a learning model that learns the relationship between the blood glucose level during exercise of the user and the injury or illness of the user. Steps and
An output step that outputs information about the possibility estimated by the estimation step, and an output step.
How to estimate the injury or illness to perform. On the computer
A reception function that accepts input of blood glucose level information related to the blood glucose level during exercise of the target user,
Estimating the possibility that the target user will suffer an injury or illness using the blood glucose level information received by the reception function and a learning model that learns the relationship between the blood glucose level during exercise of the user and the injury or illness of the user. Functions and
An output function that outputs information about the possibility estimated by the estimation function, and
Injury and illness estimation program to realize. The computer
A reception step that accepts input of adrenaline information regarding the amount of adrenaline secreted by the target user during exercise, and
Estimating the possibility that the target user will suffer injury or illness using the adrenaline information received by the reception step and a learning model that learns the relationship between the amount of adrenaline secretion during exercise of the user and the injury or illness of the user. Steps and
An output step that outputs information about the possibility estimated by the estimation step, and an output step.
How to estimate the injury or illness to perform. On the computer
A reception function that accepts input of adrenaline information regarding the amount of adrenaline secreted by the target user during exercise, and
Estimating the possibility that the target user will suffer injury or illness using the adrenaline information received by the reception function and a learning model that learns the relationship between the amount of adrenaline secretion during exercise of the user and the injury or illness of the user. Functions and
An output function that outputs information about the possibility estimated by the estimation function, and
Injury and illness estimation program to realize. A memory unit that stores a learning model that learned the relationship between blood glucose levels and adrenaline,
An acquisition unit that acquires blood glucose level information that measures the user's blood glucose level,
An estimation unit that estimates the amount of adrenaline secreted by the user based on the blood glucose level information acquired by the acquisition unit and the learning model.
An output unit that outputs the amount of adrenaline secreted estimated by the estimation unit, and an output unit.
Equipped with an adrenaline estimation system. The computer
The acquisition step to acquire the blood glucose level information that measured the user's blood glucose level,
An estimation step for estimating the adrenaline secretion amount of the user based on the blood glucose level information acquired by the acquisition step, a learning model learned the relationship between the blood glucose level and adrenaline, and an estimation step.
An output step that outputs the amount of adrenaline secreted estimated by the estimation step, and an output step.
Adrenaline estimation method to perform. On the computer
The acquisition function to acquire the blood glucose level information that measured the user's blood glucose level,
An estimation function that estimates the amount of adrenaline secreted by the user based on the blood glucose level information acquired by the acquisition function and a learning model that learns the relationship between the blood glucose level and adrenaline.
An output function that outputs the amount of adrenaline secreted estimated by the estimation function, and
An adrenaline estimation program that realizes.

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