WO2022102717A1

WO2022102717A1 - Information processing device, support system, terminal device, information processing method, and program

Info

Publication number: WO2022102717A1
Application number: PCT/JP2021/041577
Authority: WO
Inventors: 梨世藪内; 亜有子宮坂; 朋哉日下部; 景太乾
Original assignee: パナソニックＩｐマネジメント株式会社
Priority date: 2020-11-12
Filing date: 2021-11-11
Publication date: 2022-05-19
Also published as: JPWO2022102717A1; CN116367897A; JP7442155B2

Abstract

The problem addressed by the present disclosure is to enable training information suited to a user to be provided. An information processing device (10) is provided with an attribute information acquisition unit (1311), an environment information acquisition unit (1315), a determination unit (132), and an output unit (137). The attribute information acquisition unit (1311) acquires attribute information pertaining to a user's attributes. The environment information acquisition unit (1315) acquires environment information pertaining to the user's training environment. The determination unit (132) determines, on the basis of at least the attribute information and the environment information, training information pertaining to training that the user should undertake. The output unit (137) outputs the training information determined by the determination unit (132).

Description

Information processing equipment, support systems, terminal equipment, information processing methods, and programs

The present disclosure generally relates to information processing devices, support systems, terminal devices, information processing methods, and programs, and more specifically, information processing devices, support systems, terminal devices, information processing methods, and information processing devices that process training information related to training. Regarding the program.

Patent Document 1 discloses an exercise support device. This exercise support device includes a pulse measuring unit, a moving pace measuring unit, and a guide unit. When the user selects an arbitrary exercise intensity level and is instructed to start a workout, the movement pace measuring unit measures the user's movement pace. In addition, the pulse measuring unit measures the pulse rate of the user. The guide unit determines whether or not the pulse rate measured by the pulse measuring unit is outside the range of the pulse rate corresponding to the exercise intensity level selected by the user. If the determination is YES, the guide unit corrects the measured movement pace according to the measured pulse rate.

In the exercise support device described in Patent Document 1, the guide unit determines the movement pace of the user based on the exercise intensity level selected by the user. Therefore, if the user does not accurately grasp his / her own exercise intensity level, it may not be possible to carry out training suitable for the user.

Japanese Unexamined Patent Publication No. 2016-220709

This disclosure is made in view of the above reasons, and an object of the present disclosure is to make it possible to provide training information suitable for the user.

The information processing device according to one aspect of the present disclosure includes an attribute information acquisition unit, an environment information acquisition unit, a determination unit, and an output unit. The attribute information acquisition unit acquires attribute information related to the user's attributes. The environmental information acquisition unit acquires environmental information regarding the training environment of the user. The determination unit determines training information regarding training to be performed by the user, based on at least the attribute information and the environment information. The output unit outputs the training information determined by the determination unit.

The support system according to one aspect of the present disclosure includes the information processing device and the terminal device. The terminal device presents information output from the output unit.

The terminal device according to one aspect of the present disclosure is used as the terminal device in the support system.

The support system according to one aspect of the present disclosure includes the information processing device and the measuring device. The measuring device measures the biological information of the user. The determination unit determines the training information at least based on the biometric information of the user, the attribute information, and the environmental information measured by the measuring device.

In the information processing method according to one aspect of the present disclosure, the user acquires attribute information regarding the user's attributes, acquires environmental information regarding the user's training environment, and at least based on the attribute information and the environmental information, the user The training information regarding the training to be performed is determined, and the determined training information is output.

The program according to one aspect of the present disclosure is a program for causing one or more processors to execute the information processing method.

FIG. 1 is an explanatory diagram of a support system according to an embodiment. FIG. 2 is a block diagram of an information processing device in the support system. FIG. 3 is a block diagram of a terminal device in the support system. FIG. 4 is a block diagram of the measuring device in the support system. FIG. 5 is an explanatory diagram showing an example of using the measuring device. FIG. 6 is a graph showing an example of the prediction result of the transition of biological information (deep body temperature) by the support system. FIG. 7 is a graph showing an example of the comparison result by the support system. FIG. 8 is a graph showing an example of the comparison result by the support system. FIG. 9 is a graph showing an example of the comparison result by the support system. FIG. 10 is a graph showing an example of comparison results by the support system. FIG. 11 is a graph showing an example of the comparison result by the support system. FIG. 12 is a graph showing an example of the comparison result by the support system. FIG. 13 is a graph showing an example of the comparison result by the support system. FIG. 14 is a graph showing an example of the comparison result by the support system. FIG. 15 is a graph showing an example of the comparison result by the support system. FIG. 16 is a graph showing an example of the comparison result by the support system. FIG. 17 is a graph showing an example of the comparison result by the support system. FIG. 18 is a graph showing an example of the comparison result by the support system. FIG. 19 is a graph showing an example of the comparison result by the support system. FIG. 20 is a diagram showing the time change of the load on the user when the training is performed according to the first to third examples of the training information by the support system. FIG. 21 is a diagram showing the time change of the user's biological information (core body temperature) when the training is performed according to the first to third examples of the training information by the support system. FIG. 22 is a flowchart showing an example of the operation by the support system. FIG. 23 is a flowchart showing an example of the operation by the support system. FIG. 24 is a flowchart showing an example of the operation by the support system. FIG. 25 is a block diagram of an information processing device in the support system of the first modification. FIG. 26 is a diagram showing an example of a question screen displayed on the terminal device in the support system. FIG. 27 is a diagram showing an example of a caution screen displayed on the terminal device in the support system. FIG. 28 is a diagram showing an example of a question screen displayed on the terminal device in the support system. FIG. 29 is a graph showing an example of the prediction result of the transition of biological information (deep body temperature) by the support system. FIG. 30 is a block diagram of an information processing device in the support system of the second modification. FIG. 31 is a diagram showing an example of a question screen displayed on the terminal device in the support system. FIG. 32 is a flowchart showing an example of the operation by the support system. FIG. 33 is a block diagram of the measuring device in the support system of the modified example 3. FIG. 34 is a flowchart showing an example of the operation by the support system. FIG. 35 is a flowchart showing an example of the operation by the support system of the modified example 4.

Hereinafter, the support system 100 according to the embodiment will be described with reference to the drawings. However, the following embodiments are only one of the various embodiments of the present disclosure. The following embodiments can be variously modified according to the design and the like as long as the object of the present disclosure can be achieved.

(1) Overview Many people engage in sports, various strength training, etc. for the purpose of maintaining and promoting health or relieving stress, but in general, the use of advanced facilities and equipment is a cost burden. The reality is that, with the exception of some top athletes, it is difficult to receive the guidance of a professional trainer or coach, and it is extremely difficult to practice effective training that leads to improved competitive performance and records. Training generally refers to the process of strengthening and developing sports abilities such as the form and function of the human body, including willpower, by utilizing the adaptability of the body to exercise stimuli. It may be light exercise, exercise, gymnastics, etc. performed for the purpose of dieting or suppressing or recovering muscle weakness by imposing some load on the body by one's own will.

The support system 100 of the present embodiment is, for example, a training suitable for an individual user 200 for a general user 200 who has difficulty in using advanced facilities and equipment, receiving guidance from a specialized trainer or coach, and the like. The purpose is to provide information. Of course, the user 200 of the support system 100 of the present embodiment is not limited to a general user, and may be an exercise expert such as an athlete or a coach thereof.

As shown in FIG. 1, the support system 100 includes an information processing device 10, a terminal device 20, and a measuring device 30.

As shown in FIG. 2, the information processing apparatus 10 includes an attribute information acquisition unit 1311 (“attribute acquisition unit” in FIG. 2) and an environment information acquisition unit 1315 (“environment acquisition unit” in FIG. 2). There is.

The attribute information acquisition unit 1311 acquires attribute information related to the attributes of the user 200. The attribute information is information on the characteristics / properties of the user 200, and may include information related to the physical function and / or the motor function of the user 200 in particular. Examples of attribute information include various so-called user 200 age, date of birth, race, place of residence, gender, height, weight, lean body mass, muscle mass, body fat mass, body fat percentage, training level, etc. The profile information of the user 200 is mentioned. The training level here refers to an index indicating the degree of physical function (motor function) of the user 200, for example, and is typically the maximum oxygen uptake (VO ₂ max) and anaerobic work. It may include information such as threshold (AT), maximal work (WRmax), resting heart rate, maximal heart rate and the like. Examples of the profile information of various users 200 may include information on exercise habits (exercise type, exercise execution time, exercise execution frequency, exercise implementation period, exercise load, etc.) in place of / in addition to the above-mentioned ones.

The environmental information acquisition unit 1315 acquires environmental information regarding the training environment of the user 200. The environmental information is information about the training environment in general when the user 200 carries out training. The environmental information may include, for example, environmental information of the training implementation time and location specified by the user 200. Examples of environmental information include weather, temperature, humidity, wind speed, wind direction, and amount of solar radiation.

The information processing device 10 further includes a determination unit 132 and an output unit 137.

The decision unit 132 determines the training information regarding the training to be performed by the user 200 based on at least the attribute information and the environment information. The output unit 137 outputs the training information determined by the determination unit 132.

As described above, according to the information processing apparatus 10 of the present embodiment and the support system 100 provided with the information processing apparatus 10, the training information is determined using the attribute information and the environmental information. That is, the training information reflects the attribute information of the user 200 itself and the environment information around the user 200. Therefore, the user 200 can perform training suitable for himself / herself by performing training based on the training information.

In short, the information processing device 10 and the support system 100 of the present embodiment have an advantage that it is possible to provide training information suitable for an individual user 200.

(2) Details Hereinafter, the support system 100 of the present embodiment will be described in more detail with reference to the drawings. The support system 100 of the present embodiment is used, for example, for the purpose of improving the endurance motor ability or performance of the user 200, but the purpose of the support system 100 is not limited to this.

The information processing device 10 can be connected to the terminal device 20 via the communication network 40. The communication network 40 may include the Internet, a telephone network, and the like. The communication network 40 may be composed of not only a network compliant with a single communication protocol but also a plurality of networks compliant with different communication protocols. The communication protocol can be selected from a variety of well-known wired and wireless communication standards. Although simplified in FIG. 1, a communication network may include data communication equipment such as repeater hubs, switching hubs, bridges, gateways, routers and the like.

(2.1) Terminal device The terminal device 20 is used to present the information output from the output unit 137.

The terminal device 20 is an information terminal. The terminal device 20 is, for example, a portable device possessed by the user 200. The terminal device 20 is, for example, a smartphone. The terminal device 20 is not limited to a smartphone, but may be a portable information terminal such as a tablet terminal, a personal computer (desktop computer, laptop computer, etc.), a wristwatch-type terminal device, a smart television, or the like. Further, the terminal device 20 is not limited to a general-purpose device, but may be a dedicated device.

As shown in FIG. 3, the terminal device 20 includes an input unit 21, a presentation unit 22, a communication unit 23, and a processing unit 24.

The input unit 21 is used to input information to the terminal device 20. The input unit 21 includes an input device for operating the terminal device 20. The input device has, for example, a touch pad and / or one or more buttons. The input device is not limited to the touch pad, but may be a keyboard, a pointing device, a mechanical switch, or the like. The input device may include a voice input device. The input unit 21 may include a plurality of input devices.

The presentation unit 22 is used to present (output) information from the terminal device 20. The presentation unit 22 includes a presentation device for presenting information. The presenting device includes an image display device for displaying information. The image display device is a thin display device such as a liquid crystal display or an organic EL display. A touch panel may be configured by the touch pad of the input unit 21 and the image display device of the presentation unit 22. The presenting device may include a voice output device that outputs information by sound.

The communication unit 23 includes a first communication unit 231 and a second communication unit 232.

The first communication unit 231 is a communication module for communicating with the information processing device 10. The first communication unit 231 can be connected to the communication network 40 and has a function of performing communication through the communication network 40. The first communication unit 231 conforms to a predetermined communication protocol (first communication protocol). The first communication protocol can be selected from a variety of well-known wired and wireless communication standards.

The second communication unit 232 is a communication module for communicating with the measuring device 30. The second communication unit 232 here conforms to a predetermined second communication protocol different from the first communication protocol. The second communication protocol can be selected from a variety of well-known wired and wireless communication standards. As the second communication protocol, for example, a protocol suitable for short-range wireless communication (for example, a protocol used in Bluetooth®) may be adopted.

The first communication protocol and the second communication protocol may be the same. The first communication unit 231 and the second communication unit 232 may be configured by one communication module.

The processing unit 24 can be realized by, for example, a computer system including one or more processors (microprocessors) and one or more memories. The processing unit 24 can be realized by, for example, a computer system including one or more processors (microprocessors) and one or more memories. That is, one or more processors execute one or more (computer) programs (applications) stored in one or more memories, thereby functioning as the processing unit 24. Although the program is recorded in advance in the memory of the processing unit 24 here, it may be recorded and provided through a telecommunication line such as the Internet or on a non-temporary recording medium such as a memory card.

The processing unit 24 is configured to control the entire terminal device 20, that is, the input unit 21, the presentation unit 22, and the communication unit 23. The function of the processing unit 24 is realized by executing a program by one or more processors of the processing unit 24.

When the processing unit 24 executes the program, the presentation unit 22 presents information (screen, voice, etc.) that prompts the user 200 to input various information. The user 200 inputs various information to the information presented to the presentation unit 22 via the input unit 21.

The information input by the input unit 21 includes, for example, attribute information regarding the attributes of the user 200, possessed clothing information regarding clothes that can be used by the user 200 (for example, owned by the user 200), target information regarding the goal of the user 200, and the user. Examples include availability information indicating whether or not to adopt the 200 training information. That is, the input unit 21 receives input of various information such as attribute information.

The processing unit 24 has a function of transmitting the information input in response to the operation of the input unit 21 from the first communication unit 231 to the information processing device 10 through the communication network 40. The first communication unit 231 functions as a communication unit (transmission unit) that transmits the information input to the input unit 21 to the information processing device 10.

The processing unit 24 has a function of receiving information from the information processing device 10 by the first communication unit 231 through the communication network 40. The processing unit 24 has a function of presenting information from the information processing device 10 by the presentation unit 22. In this way, the processing unit 24 receives various information from the information processing apparatus 10 by the first communication unit 231 and presents the information received by the presentation unit 22.

Examples of the information presented by the presentation unit 22 include training information regarding training to be performed by the user 200, evaluation result information regarding training evaluation results, schedule information regarding training schedules, and the like.

Further, as described above, the terminal device 20 can communicate with the measuring device 30 by the second communication unit 232. The measuring device 30 is a device used for measuring the biological information of the user 200.

(2.2) Measuring device The measuring device 30 measures the biological information of the user 200. In the present embodiment, the biological information of the user 200 measured by the measuring device 30 is the body temperature of the user 200, and the measuring device 30 is a so-called temperature sensor. The measuring device 30 of the present embodiment is attached to the ear of the user 200 and is configured to measure the eardrum temperature of the user 200.

As shown in FIG. 4, the measuring device 30 includes a measuring unit 31, a communication unit 32, a storage unit 33, and a housing 34.

The communication unit 32 is a communication module for communicating with the second communication unit 232 of the terminal device 20. The communication unit 32 conforms to the second communication protocol.

The housing 34 holds the measurement unit 31, the communication unit 32, and the storage unit 33. The housing 34 is made of resin, for example, and has a shape that can be attached to the ear of the user 200 as shown in FIG. The housing 34 includes an insertion portion that is inserted into the ear canal of the user 200's ear. The housing 34 may include a hooking portion that is hooked on the pinna of the user 200's ear. The housing 34 is created according to the shape of the installation portion (ear) of the user 200 in order to improve the wearability of the user 200 to the ear and prevent discomfort and deterioration of measurement accuracy due to misalignment. It is preferable to be done. Of course, a plurality of housings 34 having different sizes or shapes may be prepared, and the user 200 may be able to select a housing 34 having a size and shape suitable for himself / herself.

The measuring unit 31 is arranged in the insertion unit. That is, the measuring unit 31 is arranged so as to face the eardrum of the user 200 with the housing 34 attached to the ear of the user 200.

The measuring unit 31 includes, for example, a temperature detecting element for detecting the environmental temperature and an infrared detecting element for detecting infrared rays radiated from the temperature measuring portion (the eardrum of the user 200 or its vicinity). The temperature detecting element is, for example, a thermistor. The infrared detection element is, for example, a thermopile including a cold contact and a warm contact. The measuring unit 31 measures the eardrum temperature of the user 200 based on the environmental temperature detected by the temperature detecting element and the infrared intensity detected by the infrared detecting element.

In short, the measuring device 30 of the present embodiment measures the body temperature of the user 200 as the biological information of the user 200. In particular, the measuring device 30 of the present embodiment measures the eardrum temperature of the user 200. Here, the eardrum temperature is said to be close to the core body temperature of the user 200. The core body temperature is the temperature inside the human body (brain, internal organs, etc.). Unlike body surface temperature (skin temperature), core body temperature is less susceptible to environmental effects such as outside air temperature due to the homeostasis of the human body, which is mainly controlled by the hypothalamus. That is, the measuring device 30 of the present embodiment measures the core body temperature of the user 200 as the body temperature of the user 200.

As described above, the measuring device 30 of the present embodiment can measure the biological information (deep body temperature) of the user 200 non-invasively by measuring the eardrum temperature of the user 200. Further, since the measuring device 30 can be attached to the ear of the user 200, it does not easily interfere with the user 200 during training.

The measurement unit 31 measures the eardrum temperature (initial temperature T0) before the user 200 performs training as the biological information of the user 200. Further, the measuring unit 31 measures the eardrum temperature (exercise temperature) when the user 200 is performing training as the biological information of the user 200. For example, as shown in FIG. 5, the measuring device 30 is attached to the ear of the user 200 who is performing running as training, and measures the exercise temperature of the user 200. The measuring unit 31 continuously measures the biological information (tympanic membrane temperature) of the user 200 when the user 200 is training (during the training). In the present disclosure, "continuously measuring biological information" means that the measured value of biological information (temperature) is obtained a plurality of times periodically or irregularly. In one example, "continuously measuring biological information" may mean measuring biological information at least once per minute, and may be measuring at least 10 times per minute. It may be measured 30 times or more, or it may be measured 60 times or more per minute. In the present disclosure, "continuously measuring biometric information" does not include measuring biometric information only once.

The eardrum temperature measured by the measuring unit 31 is a terminal via the communication unit 32 in response to a request from the terminal device 20 or an operation to an appropriate operation unit provided in the housing 34 of the measuring device 30. It is transmitted to the device 20. The measuring device 30 may transmit the eardrum temperature measured by the measuring unit 31 to the terminal device 20 in real time. The terminal device 20 that has received the information on the eardrum temperature (exercise temperature) of the user 200 at the time of training transmits the received eardrum temperature information to the information processing device 10.

The storage unit 33 stores the biological information (tympanic membrane temperature) measured by the measurement unit 31. The storage unit 33 includes one or more storage devices. The storage device is, for example, RAM (RandomAccessMemory), EEPROM (ElectricallyErasableProgrammableReadOnlyMemory), or the like.

Here, as an example of the biological information measured by the measuring device 30, the tympanic membrane temperature close to the core body temperature has been described. However, if the biological information of the user 200 is changed by training, the same effect is applied. Needless to say, a more optimal training menu (training information) can be provided according to the measured biological information.

Among the biological information obtained from the user 200, examples other than the tympanic membrane temperature (deep body temperature) (other examples of the biological information of the user 200 measured by the measuring unit 31) include the heart rate, respiratory rate, and heart rate variability of the user 200. Arterial oxygen saturation (SPO ₂ ), body surface temperature (skin temperature), average skin temperature, temperature inside the clothes worn by the user 200, humidity inside the clothes worn by the user 200, user 200. The amount of local sweating on the body surface, the amount of whole body sweating, and the like can be mentioned. Heart rate, respiratory rate, and heart rate variability can be measured, for example, by an optical heart rate sensor attached to the wrist of the user 200. Further, the arterial oxygen saturation (SPO ₂ ) can be measured by an optical arterial oxygen saturation sensor attached to the wrist of the user 200. The body surface temperature (skin temperature) can be measured by a temperature sensor (thermistor or the like) attached to the body surface of the user 200. The average skin temperature can be calculated based on the skin temperature of each site as one of the representative skin temperatures of the whole body. For example, the formula of the four-point method of Ramanathan: (average skin temperature) = 0.3 × (chest skin temperature) It can be calculated by +0.3 × (upper arm skin temperature) +0.2 × (thigh skin temperature) +0.2 × (lower leg skin temperature). However, among the skin temperatures of the chest, upper arm, thigh, and lower leg, the measurement result of only the skin temperature of the chest, which is not easily affected by the outside air temperature, may be used. The temperature inside the clothes can be acquired by a temperature sensor (thermistor or the like) attached to the clothes worn by the user 200. The humidity inside the clothes can be acquired by the humidity sensor attached to the clothes worn by the user 200. The amount of local sweating on the body surface can be measured by detecting the humidity via the capsule attached to the site of the user 200 with a humidity sensor or the like. The whole-body sweating amount can be measured by estimating from the local sweating amount at a plurality of representative parts on the body surface of the user 200 in consideration of the body surface area and the sweating rate of each part. The measuring unit 31 of the measuring device 30 may have the function of one or more of the above-mentioned devices (optical heart rate sensor, temperature sensor, etc.).

In addition, here, we have described an example of measuring the tympanic membrane temperature close to the core body temperature when the core body temperature, which is less susceptible to disturbances such as temperature and can accurately determine the training load received by the user 200, is taken in as biological information. The core body temperature measured by the measuring device 30 may be esophageal temperature, rectal temperature, or the like. As a method for measuring the esophageal temperature and the rectal temperature, there is a method in which the user 200 drinks a small capsule (measuring device 30) having a built-in temperature sensor and measures the temperature of the capsule passing through the body of the user 200. Also in this method, the core body temperature of the user 200 can be measured non-invasively, and the training of the user 200 is less likely to be disturbed. The core body temperature can be estimated from the temperature measured at various sites such as the armpit or under the tongue, the chest, and the abdomen (navel) in addition to the eardrum temperature. Further, the core body temperature may be estimated from the body surface temperature or the expiratory temperature which can be easily measured as compared with the eardrum temperature. For example, the measuring device 30 may use a value obtained by converting the measured value of the temperature according to a predetermined conversion formula, an algorithm, or the like as the measured value of the core body temperature of the user 200.

The non-invasive method using infrared rays and thermistor described as the method for measuring the eardrum temperature can greatly improve the accuracy of the measured eardrum temperature, but the method for measuring the eardrum temperature by the measuring device 30 is limited to such a method. not. The measuring device 30 may adopt a configuration in which the skin temperature or the temperature in the ear canal is directly measured by the thermistor. As a result, the size, weight, and cost can be reduced, and the usability and usability of the user can be improved.

Further, the biological information measured by the measuring device 30 is not limited to one. The measuring device 30 may measure a plurality of biological information such as eardrum temperature and heart rate. This makes it possible to more accurately estimate the exercise load applied to the user 200, and further improve the accuracy of the training menu (training information).

(2.3) Information Processing Device As shown in FIG. 2, the information processing device 10 includes a communication unit 11, a storage unit 12, and a processing unit 13. The information processing device 10 can be realized by, for example, a server.

The communication unit 11 is a communication interface. The communication unit 11 can be connected to the communication network 40 and has a function of performing communication through the communication network 40. The communication unit 11 conforms to a predetermined communication protocol (first communication protocol). The first communication protocol can be selected from a variety of well-known wired and wireless communication standards. The communication unit 11 is communicably connected to the terminal device 20. As a result, the information processing device 10 can communicate with the terminal device 20.

(2.3.1) Storage unit The storage unit 12 is used to store information used by the processing unit 13 and information generated by the processing unit 13. The storage unit 12 includes one or more storage devices. The storage device is, for example, a RAM (Random Access Memory), an EEPROM (Electrically Erasable Programmable Read Only Memory), or the like. The storage unit 12 may be shared with the memory of the processing unit 13.

As shown in FIG. 2, the storage unit 12 includes a user information storage unit 121, an environmental information storage unit 122, a clothing information storage unit 123, a training type storage unit 124, and a predictive storage unit 125. There is.

The user information storage unit 121 stores user information, which is information of the user 200. Here, the user information storage unit 121 stores user information of a plurality of users 200. The user information of the plurality of users 200 is stored for each user 200 in association with the identification information (ID) assigned to each user 200.

Here, the user information includes attribute information regarding the attributes of the user 200, possessed clothing information regarding the clothes that the user 200 can use, target information regarding the goal of the user 200, and history information regarding the training history of the user 200. Includes. Therefore, the user information storage unit 121 stores the attribute information storage unit 1211 that stores the attribute information, the possession clothing information storage unit 1212 that stores the possessed clothing information, the target information storage unit 1213 that stores the target information, and the history information. It is provided with a history information storage unit 1214 to be stored.

As described above, the attribute information is information on the characteristics / properties of the user 200, and particularly includes information related to the user's physical function and / or motor function (motor function information).

The attribute information may include attribute information that may fluctuate in the medium term and attribute information that has little fluctuation (no fluctuation) in the long term. Here, "medium-term" refers to a weekly / monthly period, and "long-term" refers to a yearly period, but is not limited thereto.

Attribute information that does not fluctuate over the long term includes the age, date of birth, race, gender, height (in the case of an adult), etc. of the user 200. Attribute information that does not fluctuate over the long term may be registered once and then not updated.

As attribute information that can change in the medium term, various so-called users such as height (in the case of minors), place of residence, weight, lean body mass, muscle mass, body fat mass, body fat percentage, training level, etc. of the user 200 200 profile information can be mentioned.

The training level is an index showing the degree of the current physical function (motor function) of the user 200. Typical examples of training levels include information on maximal oxygen uptake (VO ₂ max), anaerobic threshold (AT), maximal work (WRmax), resting heart rate, and maximal heart rate. As a method for measuring maximal oxygen uptake (VO ₂ max) and anoxic work threshold (AT), there is a method of directly measuring using a device such as an exhaled gas analyzer, but the maximum oxygen uptake (VO ₂ max) can also be measured by a 12-minute run (Cooper run) in which the running time is determined, a 1500 m run in which the running distance is determined, a 3000 m run, a 20 m shuttle run, or the like. In addition, when the user 200 owns a smart watch, a running watch, or the like, the maximum oxygen uptake (VO ₂ max) and anaerobic work can be performed by monitoring the heart rate during daily activities or training other than during training. It is also possible to measure the threshold (AT). An example of another training level is the shortest time per kilometer when the user 200 runs for 30 minutes. The shortest time per kilometer means the personal best of the value obtained by dividing the running time (30 minutes) by the distance (km) that the user 200 has run in the running time (30 minutes). The shortest time per kilometer may change depending on the time of running. Therefore, for example, the shortest time is stored every 30 minutes between 30 and 90 minutes (that is, the shortest time per kilometer for 30 minutes running, the shortest time per kilometer for 60 minutes running, and the shortest time per kilometer for 90 minutes running. It is desirable to memorize the shortest time respectively).

It is preferable that the attribute information that may change in the medium term is registered once when the user 200 uses the system for the first time, and then the information is updated in a timely manner. In particular, the training level of the user 200 can be changed by accumulating training or not performing training for a long period of time. Therefore, it is preferable that the training level is updated as appropriate. When the user 200 regularly trains using the support system 100, the training level is based on the training performance of the user 200 (maximum oxygen uptake measured at the time of training ₍ VO2 max), etc.). The information processing device 10 or an external server may be used for estimation.

The attribute information may include the normal temperature of the user 200. The initial temperature T0 of the user 200 is used for determining the intensity of training, predicting the core body temperature of the user 200, etc. (described later), but the estimated value of the core body temperature estimated from the normal temperature or the normal temperature is used as the initial temperature. By using it instead of T0, the measurement of the initial temperature T0 can be omitted. However, it is preferable to use the initial temperature T0 because the accuracy of predicting the core body temperature of the user 200 during training is improved. Normal body temperature can be measured with a thermometer that is measured once at a site such as the armpit, sublingual, or forehead for health management or disease diagnosis. The normal body temperature included in the attribute information may be the eardrum temperature, esophageal temperature, rectal temperature, etc. of the user 200 measured in normal times (that is, the core body temperature of the user 200 measured in normal times).

The attribute information is acquired from the terminal device 20 by the attribute information acquisition unit 1311 of the processing unit 13 via the communication network 40.

The possessed clothing information is information on clothing that can be used by the user 200. The clothes that can be used by the user 200 include clothes owned by the user 200. The clothes that can be used by the user 200 may include clothes that the user 200 can borrow from another person, a rental company, or the like.

The possessed clothing information is acquired by the possessed clothing information acquisition unit 1312 of the processing unit 13 from the terminal device 20 via the communication network 40.

The goal information is information about the goal of the user 200, particularly the training goal of the user 200. The training goal can be any goal that the user 200 wants to achieve through training. As an example of the training goal, the user 200, whose fastest time for a full marathon so far is 3 hours and 13 minutes, may shorten the time by 3 minutes at a full marathon event to be held two months later.

The target information is acquired from the terminal device 20 by the target information acquisition unit 1313 of the processing unit 13 via the communication network 40.

The history information is information related to the history of training performed by the user 200 using the support system 100 (information processing device 10). The training history may include a history of training information determined by the determination unit 132, a history of comparison results by the comparison unit 134, a history of evaluation results by the evaluation unit 135, and the like.

The environmental information storage unit 122 stores environmental information. As described above, the environmental information is information about the training environment of the user 200, and may include information on the environment of the training implementation time and the implementation location designated by the user 200.

The environmental information may include environmental information with short-term fluctuations and environmental information with little fluctuation (no fluctuation) in the long term. Here, "short-term" refers to a period of minutes, hours, and days, and "long-term" refers to a period of years, but is not limited to this.

Environmental information with short-term fluctuations includes weather, temperature, humidity, wind speed, wind direction, and amount of solar radiation. These environmental information is measured, for example, by an appropriate measuring device installed at the training site. These environmental information may be substituted with prediction data (or measurement data) provided by a service provider or the like that provides information on weather.

Examples of environmental information with little change over the long term include topographical information on the training site. Topographical information includes, for example, the type of road surface, the undulations of the road surface, the surrounding buildings, and the like. For example, when running is selected as training, the type of running course (road surface), undulations of the running course, altitude, and the like may be included in the environmental information. Examples of the types of running courses include urban areas, track and field tracks, and trail courses that utilize mountain trails. The undulations and altitude of the running course are measured by, for example, a device (tablet type information terminal, wristwatch type information terminal, etc.) having a built-in GPS (Global Positioning System), and can be acquired via the communication network 40.

The clothing information storage unit 123 stores clothing information related to many types of clothing. The clothing information storage unit 123 stores, for example, a data table in which a large number of types of clothing and heat retention of each clothing are associated with each other. Examples of the heat insulating property of clothes include a clo value. The clo value is the thermal resistance of basic clothing, and is a value representing the heat retaining ability of clothing when worn. The higher the heat retention of the clothes, the lower the amount of heat dissipated from the body, so that the core body temperature of the user during training tends to rise. As shown in Table 1 below, in addition to the clo value, the data table (clothing information) includes clothing weight (g), clothing thickness (mm), clothing color, clothing material, and moisture permeability (g). / m ² / 24h) is preferably included. Moisture permeability is a numerical value indicating how many grams of water permeated per 1 m ² of fabric in 24 hours. The higher the moisture permeability, the higher the moisture permeability of clothes. The lower the moisture permeability of clothing, the lower the amount of heat dissipated from the body due to sweating, so the core body temperature of the user during training tends to rise.

The training type storage unit 124 stores training type information regarding a plurality of training types. The type of training may include, for example, running, biking, and the like.

The training type storage unit 124 further stores a plurality of types (items) of training for each type.

Examples of types of running include pace running, interval running, build-up running, and the like. Pace running is a training run that keeps running at a constant pace. Interval running is a training run that repeats a fast pace and a slow pace. A build-up run is a training run that starts running at a comfortable pace and gradually increases the pace to run a certain distance or time. The type of running may include running outdoors and running indoors using a treadmill or the like.

Examples of types of bicycle running include training to ride a bicycle outdoors and training to be performed indoors using a bicycle ergometer or the like.

The prediction formula storage unit 125 stores the prediction formula used for processing in the determination unit 132 and the prediction unit 133. Here, the prediction formula includes a first prediction formula that predicts the core body temperature of the user 200 at the time of training, and a second prediction formula that calculates the intensity for maintaining the core body temperature of the user 200 at the time of training.

(2.3.2) Processing unit The processing unit 13 mainly includes a computer system (including a server or cloud computing) having one or more processors and one or more memories. One or more processors realize the function of the processing unit 13 by executing a program recorded in one or more memories. The program may be pre-recorded in memory, may be recorded and provided on a non-temporary recording medium such as a memory card, or may be provided through a telecommunication line. In other words, the above program is a program for making one or more processors function as the processing unit 13.

As shown in FIG. 2, the processing unit 13 includes an information acquisition unit 131, a determination unit 132, a prediction unit 133, a comparison unit 134, an evaluation unit 135, a schedule unit 136, and an output unit 137. There is. The information acquisition unit 131, the determination unit 132, the prediction unit 133, the comparison unit 134, the evaluation unit 135, the schedule unit 136, and the output unit 137 show the functions realized by the processing unit 13 rather than the actual configuration. ..

The information acquisition unit 131 includes an attribute information acquisition unit 1311, a possessed clothing information acquisition unit 1312 (“possessed clothing acquisition unit” in FIG. 2), a target information acquisition unit 1313 (“target acquisition unit” in FIG. 2), and a living body. It includes an information acquisition unit 1314 and an environmental information acquisition unit 1315.

The attribute information acquisition unit 1311 acquires the attribute information. The attribute information acquisition unit 1311 acquires the attribute information of the user 200 mainly by receiving the attribute information of the user 200 input by the input unit 21 of the terminal device 20 via the communication network 40 using the communication unit 11. .. As some information (for example, training level) of the attribute information, for example, information estimated by the information processing apparatus 10 itself based on other information may be used. The attribute information acquisition unit 1311 stores the acquired attribute information of the user 200 in the user information storage unit 121 (attribute information storage unit 1211) in association with the identification information of the user 200.

The possessed clothing information acquisition unit 1312 acquires the possessed clothing information. The possessed clothing information acquisition unit 1312 receives the possessed clothing information of the user 200 input by the input unit 21 of the terminal device 20 via the communication network 40 using the communication unit 11 to receive the possessed clothing information of the user 200. get. The possessed clothing information acquisition unit 1312 stores the acquired clothing information of the user 200 in the user information storage unit 121 (possessed clothing information storage unit 1212) in association with the identification information of the user 200.

The target information acquisition unit 1313 acquires target information. The target information acquisition unit 1313 acquires the target information of the user 200 by receiving the target information of the user 200 input by the input unit 21 of the terminal device 20 via the communication network 40 using the communication unit 11. The target information acquisition unit 1313 stores the acquired target information of the user 200 in the user information storage unit 121 (target information storage unit 1213) in association with the identification information of the user 200.

The biometric information acquisition unit 1314 acquires the biometric information of the user 200. The biometric information acquisition unit 1314 acquires the measured value of the biometric information of the user 200 measured at the time when the user 200 carries out the training.

The biological information acquisition unit 1314 includes a body temperature information acquisition unit 1319 (“body temperature acquisition unit” in FIG. 2). The body temperature information acquisition unit 1319 acquires the body temperature information of the user 200. The body temperature information acquisition unit 1319 acquires the eardrum temperature of the user 200 measured by the measuring device 30 and transmitted to the terminal device 20 as a measured value of the body temperature (deep body temperature) of the user 200. The body temperature information acquisition unit 1319 acquires the measured value of the body temperature (deep body temperature) of the user 200 from the terminal device 20 via the communication network 40 using the communication unit 11. The body temperature information acquired by the body temperature information acquisition unit 1319 may include information on the initial temperature T0 and information on the exercise temperature. That is, the body temperature information acquisition unit 1319 acquires the initial temperature T0, which is the body temperature of the user 200 measured by the measuring device 30 before performing the training according to the training information. In addition, the body temperature information acquisition unit 1319 acquires the exercise temperature, which is the body temperature of the user 200 when the user 200 performs training.

The environmental information acquisition unit 1315 acquires environmental information. The environmental information acquisition unit 1315 uses the communication unit 11 to provide environmental information via the communication network 40 from an appropriate measurement device installed at the training site, a service provider that provides information on weather, a device with a built-in GPS, and the like. To get. The environmental information acquisition unit 1315 stores the acquired environmental information in the environmental information storage unit 122.

The decision unit 132 determines training information regarding the training to be performed by the user. The training information includes at least one of the type of training, the intensity of training, the time of training, and the clothing of the user 200 in training. In particular, the training information includes the combination of training intensity and user 200 clothing in training. Here, the determination unit 132 determines training information including the type of training, the intensity of training, the time of training and the clothing of the user 200 in training.

The type of training (whether running, biking, etc.) is determined by the user 200 here. The determination unit 132 presents, for example, a plurality of training types stored in the training type storage unit 124 to the user 200 by the terminal device 20 as a plurality of selection candidates. The determination unit 132 determines the selection candidate selected by the user 200 from the plurality of presented selection candidates as the type of training to be performed.

The clothes of the user 200 in the training are selected from the clothes stored in the possessed clothes information storage unit 1212. The determination unit 132 selects an appropriate clothing combination from the clothes stored in the possessed clothing information storage unit 1212 with reference to the clo value and the like. That is, the determination unit 132 selects the clothes of the user 200 from the clothes stored in the possessed clothes information storage unit 1212. For example, common winter runner clothing combinations include clothing combinations including windbreakers, shorts, long-sleeved shirts, tights, caps, neck warmers, and gloves. If you choose clothes with a larger clo value, such as changing the windbreaker to a batting jacket or changing shorts to batting pants, it will be a combination of clothes that can easily raise the core body temperature. The clothing of the user 200 may be selected (designated) by the user 200 using the terminal device 20.

The training intensity and training time are determined by the determination unit 132 so that the training is carried out so that the core body temperature of the user 200 is maintained at the threshold temperature Tth or higher for a predetermined period P0. The training time is, here, the time (duration) from the start to the end of the training.

It is known that continuous training to apply heat load to the body induces heat acclimatization response and improves endurance exercise capacity and / or performance. For example, in Reference 1 (Lorenzo S, Halliwill JR, Sawka MN, et al. Heat acclimation improves exercise performance. J Appl Physiol. 2010; 109: 1140-1147), there are bicycle athletes who are training on a daily basis. As a result of training to apply heat load to the body (heat load training) for 10 consecutive days by bicycle ergometer exercise in a room with a temperature of 40 ° C, a heat acclimation response is triggered and maximal oxygen that reflects endurance exercise capacity. It has been reported that intake (VO ₂ max) and lactic acid threshold were improved by about 5% each compared to before training. It has also been reported that in the 1-hour bicycle time trial, the maximum output increased by about 6% compared to before training. That is, by determining the training intensity and time so that the core body temperature of the user 200 is maintained at the threshold temperature Tth or higher for a predetermined period P0 as described above, the user 200 is caused to acclimatize to heat, and eventually the user. 200 endurance exercise capacity and / or performance can be improved.

The training intensity is an index of the magnitude of the heat load applied to the user 200 here. For example, if the type of training is running, the intensity of training can be expressed as a set time per kilometer. The smaller the set time per kilometer, the greater the intensity. The determination unit 132 adjusts the intensity of training, for example, by changing the set time per kilometer.

The intensity of training determined by the determination unit 132 may include the type of training (pace running, interval running, etc.). For example, the determination unit 132 may decide to perform an interval run in which a fast pace and a slow pace are repeated for the user 200 or the like having a high training level. By incorporating interval running, in addition to heat load, the effect of improving endurance athletic ability by interval running can be expected. On the other hand, for users 200 who want to suppress the intensity as much as possible, such as users 200 with a low training level and users who are undergoing treatment for injuries, the core body temperature is determined by lengthening the training time by the amount of lowering the intensity. It may be kept above the threshold temperature Tth for the period P0.

The threshold temperature Tth is, for example, a value in the range of 38.0 ° C to 39.5 ° C. The threshold temperature Tth is more preferably a value in the range of 38.5 to 39.0 ° C. When the threshold temperature Tth is less than 38.0 ° C., it may be difficult to obtain a heat load sufficient to improve endurance athletic performance or performance. When the threshold temperature Tth is a value larger than 39.5 ° C., the training may apply an excessive heat load to the user 200. The threshold temperature Tth may be determined by adding a value within the specified temperature range to the initial temperature T0 of the user 200. The temperature range is preferably 1.0 ° C to 2.5 ° C, more preferably 1.2 ° C to 2.0 ° C. If the lower limit of the temperature range is less than 1.0 ° C., the heat load may not be sufficient to improve endurance athletic performance and performance. If the upper limit of the temperature range is larger than 2.0 ° C., the training may apply an excessive heat load to the user 200. There are individual differences in core body temperature. Therefore, it is preferable that the threshold temperature Tth is set for each user 200 according to the attribute information of the user 200 (for example, the height, weight, gender, normal body temperature of the user 200) and the like.

The determination unit 132 uses the prediction formula stored in the prediction formula storage unit 125 to provide clothing information (for example, clo value), user 200 attribute information (for example, initial temperature T0), environmental information (for example, temperature), and training. With reference to the intensity (for example, set time) and the like, the training intensity and the training time are determined so that the core body temperature of the user 200 is maintained at the threshold temperature Tth or higher during the predetermined period P0.

More specifically, the determination unit 132 determines the training information so as to include the first training and the second training. The first training is training for raising the core body temperature of the user 200 to the threshold temperature Tth or higher. The second training is training performed after the first training, and is training for maintaining the core body temperature of the user 200 at P0 for a predetermined period at or above the threshold temperature Tth.

FIG. 6 shows an example of the prediction result (predicted deep body temperature transition TP) of the core body temperature transition of the user 200 when the first training and the second training are performed.

The first training is training performed from the start time t0 of the training to the elapsed time t1 of the first period P1, so that the core body temperature of the user 200 reaches the threshold temperature Tth at the end time of the first training (time point t1). Will be decided.

The determination unit 132 determines the intensity of the first training (first training intensity) and the first period P1 using the prediction formula. The determination unit 132 determines the first training intensity and the first period P1 using at least the first prediction formula. The first prediction formula is a formula for predicting the core body temperature of the user 200 at the time of training. The first prediction formula is a formula for predicting the core body temperature of the user 200 when the user 200 performs training of a certain intensity for a certain period of time. The first prediction formula may include attribute information of the user 200 (for example, initial temperature T0), clothing information (for example, clo value), training intensity (for example, set time), environmental information (for example, temperature), and the like as parameters. The determination unit 132 uses the first prediction formula to obtain the first training intensity and the first period so that the core body temperature of the user 200 reaches the threshold temperature Tth from the start time t0 of the training to the elapsed time t1 of the first period P1. Determine with P1.

The second training is a training performed from the end of the first training (time point t1) to the elapsed time point t2 of the second period P2, and the core body temperature of the user 200 is maintained above the threshold temperature Tth in the second period P2. Is decided to be.

Here, the determination unit 132 adopts the above-mentioned predetermined period P0 as the second period P2 which is the period for performing the second training. The length of the predetermined period P0 may be predetermined, or may be determined for each user 200 or for each training based on attribute information such as training level, environmental information, and the like. The second period P2 may be longer than the predetermined period P0.

Further, the determination unit 132 determines the intensity of the second training (second training intensity) using the prediction formula. The determination unit 132 determines the second training intensity using at least the second prediction formula. The second prediction formula is a prediction formula for calculating the strength for maintaining the core body temperature of the user 200 when the user 200 performs a certain intensity training. The second prediction formula may include attribute information of the user 200 (for example, initial temperature T0), clothing information (for example, clo value), training intensity (for example, set time), environmental information (for example, temperature), and the like as parameters. The determination unit 132 uses the second prediction formula to determine the second training intensity so that the core body temperature of the user 200 is maintained above the threshold temperature Tth.

The first period P1 is selected from, for example, a range of 30 minutes to 100 minutes. If it is less than 30 minutes, it may not be enough time to raise the core body temperature to the threshold temperature Tth. If the time is longer than 100 minutes, the total training time will be long, which may not be preferable for the user.

The second period P2 (predetermined period P0) is selected from, for example, a range of 20 minutes or more. Less than 20 minutes may be less likely to result in a heat load that improves endurance athletic performance or performance.

The first training is training for raising the core body temperature, and the second training is training for maintaining the core body temperature. Therefore, in the present embodiment, the second training intensity is determined to be equal to or less than the first training intensity.

In this way, by dividing the training intensity into two stages and setting the second training intensity to be equal to or less than the first training intensity, the load can be suppressed to the minimum necessary and the risk of malfunction or injury of the user 200 is reduced. can do.

In short, the determination unit 132 determines the training information so that the core body temperature of the user 200 is maintained at or higher than the threshold temperature Tth of P0 (second period P2) for a predetermined period when the training is performed.

Further, the training intensity is the first training intensity at which the core body temperature of the user 200 reaches the threshold temperature Tth at the elapsed time t1 of the first period P1 from the start time t0 of the training, and after the lapse of the first period P1. It includes a second period P2 as a predetermined period P0, and a second training intensity for maintaining the core body temperature of the user 200 at a temperature equal to or higher than the threshold temperature Tth. The second training intensity is equal to or less than the first training intensity.

An example of the relationship between the training intensity and the training time (first period P1) will be described with reference to FIGS. 20 and 21.

The upper part of FIG. 20 shows the time change of the load applied to the user 200 when the training of the first example is performed, and the upper part of FIG. 21 shows the time change of the core body temperature of the user 200 in the first example (predicted deep body temperature transition). TP) is shown. As can be seen from the upper graph of FIG. 20, in the first example, a constant load L11 is applied to the user 200 in the first training, a constant load L12 is applied to the user 200 in the second training, and the user in the second training. This is a training in which the load L12 on the 200 is smaller than the load L11 on the user 200 in the first training. As can be seen from the upper graph of FIG. 21, in the first example, the body temperature (deep body temperature) rises during the first period P1 during the first training, and the body temperature during the second period P2 during the second training. (Deep body temperature) is maintained.

The middle part of FIG. 20 shows the time change of the load applied to the user 200 when the training of the second example is performed, and the middle part of FIG. 21 shows the time change of the core body temperature of the user 200 in the second example (predicted deep body temperature transition). TP) is shown. As can be seen from the graph in the middle of FIG. 20, the second example is training (for example, interval running) in which a large load L21 and a small load L22 are repeatedly performed in both the first training and the second training. In the middle of FIG. 20, for reference, the load L11 in the first training of the first example is also shown by a dotted line. As can be seen from the graph in the middle of FIG. 21, in the second example, the body temperature (deep body temperature) rises during the first period P1 in which the first training is performed and the second period P2 in which the second training is performed.

The lower part of FIG. 20 shows the time change of the load applied to the user 200 when the training of the third example is performed, and the lower part of FIG. 21 shows the time change of the core body temperature of the user 200 in the third example (predicted deep body temperature transition). TP) is shown. As can be seen from the lower graph of FIG. 20, the third example is training in which a constant load L30 smaller than the load L11 of the first training of the first example is applied in both the first training and the second training. As can be seen from the lower graph of FIG. 21, in the third example, the body temperature (deep body temperature) rises during the first period P1 in which the first training is performed and the second period P2 in which the second training is performed. However, in the case of the third case, the rate of increase in body temperature in P1 during the first period is smaller than that in the first and second cases.

As shown in FIG. 21, if the magnitude of the load is appropriately set, the core body temperature of the user 200 can be maintained above the threshold temperature Tth during the predetermined period P0 in any of the first to third examples. Actually, even if the training of the same intensity is continued, the body temperature does not continue to rise at a constant inclination, but for convenience of explanation, in FIG. 21, the training time and the body temperature are assumed to increase at a constant inclination. The relationship between is shown schematically.

The first example has merits such that the physical load on the user 200 can be suppressed as compared with the second example, and the total training time is shorter than that of the third example (t21 <t23). Therefore, the first example is easily adopted by the user 200 of a wide range of training levels.

The second example has merits such as a short total training time (t22 <t21, t22 <t23) and an effect of improving endurance athletic ability, and the training intensity is higher than that of the first example. There are disadvantages such as a large physical load on the user 200. Therefore, the second example is for the user 200 having a relatively high training level.

The third example has an advantage that the physical load on the user 200 can be suppressed as compared with the first example, and has a demerit such that the total training time is long (t23> t21, t23> t22). Therefore, the third example is for the user 200 whose training level is relatively low.

When determining the training intensity, the determination unit 132 may appropriately select any one of the first example to the third example according to the training level of the user 200 and the like. Of course, the determination unit 132 may select training other than the first to third examples.

The output unit 137 outputs the training information determined by the determination unit 132 to the terminal device 20. The training information output to the terminal device 20 is presented (for example, displayed) to the user 200 by the presentation unit 22 of the terminal device 20.

In the support system 100 of the present embodiment, the user 200 can select whether or not to adopt the training information determined by the determination unit 132. Therefore, the information acquisition unit 131 of the information processing apparatus 10 further includes a possibility / rejection information acquisition unit 1316 (“pass / fail acquisition unit” in FIG. 2). The approval / disapproval information acquisition unit 1316 acquires the approval / disapproval information indicating adoption / non-adoption for the training information.

That is, the user whose training information is presented by the presentation unit 22 of the terminal device 20 decides whether or not to adopt the training information. Whether or not the training information is adopted here includes either the adoption of the training information or the non-adoption of the training information (non-adoption). Also, not adopting training information may include not adopting only some elements of the training information (eg, clothing type). The user 200 inputs whether or not the training information is adopted by the terminal device 20 and causes the user 200 to transmit the training information to the information processing device 10.

If the training information is rejected, the decision unit 132 determines new training information.

For example, the determination unit 132 replaces an element not adopted by the user 200 with another element, and then determines new training information using a prediction formula (recalculation). For example, when the user 200 selects to wear clothes different from the clothes determined by the determination unit 132, the determination unit 132 uses the information (clo value, etc.) of the clothes selected by the user 200 as the basis for the user 200. Determine new training information.

In short, the information processing apparatus 10 includes a propriety information acquisition unit 1316 that acquires propriety information indicating whether or not the training information (original training information) output from the output unit 137 can be adopted. When the approval / disapproval information denies the adoption of the training information (original training information), the determination unit 132 determines new training information different from the training information (original training information).

The information processing device 10 determines new training information and proposes it to the user 200 until the training information is adopted by the user 200. The training information adopted by the user 200 is stored in the user information storage unit 121 (history information storage unit 1214) in association with the identification information of the user 200 together with the date and time information and the like.

The information processing device 10 (processing unit 13) further has a function of generating new training information more suitable for the user 200 based on the result of the training actually performed by the user 200. As described above, the processing unit 13 includes a biological information acquisition unit 1314 (body temperature information acquisition unit 1319).

The biometric information acquisition unit 1314 acquires the measured value of the biometric information of the user 200 measured at the time of performing the training from the measuring device 30. The body temperature information acquisition unit 1319 acquires the measured value of the body temperature (tympanic membrane temperature) of the user 200 measured at the time of performing the training from the measuring device 30 as the biological information of the user 200. The body temperature information acquisition unit 1319 acquires the measured value (exercise temperature) of the core body temperature of the user 200 measured at the time of performing the training from the measuring device 30. The determination unit 132 determines new training information regarding the new training to be performed by the user 200, at least based on the measured value information of the biological information (body temperature), the attribute information, and the environmental information.

Further, the processing unit 13 further includes a prediction unit 133 and a comparison unit 134.

The prediction unit 133 predicts the core body temperature of the user 200 when the user 200 performs training according to the training information. More specifically, the prediction unit 133 creates a core body temperature transition (predicted deep body temperature transition TP) of the user 200 at the time of training by using the prediction formula.

The prediction unit 133 has the core body temperature (initial temperature T0) of the user 200 before the training performed measured by the measurement unit 31, and the predicted core body temperature of t1 at the end of the first training predicted by the prediction formula (first prediction formula). By connecting TP1 and the predicted deep body temperature TP2 at the end of the second training t2, the predicted deep body temperature transition TP (FIG. 6) is created. In the present embodiment, since the second training is determined so that the core body temperature of the user 200 is maintained, the predicted deep body temperature TP2 at the end time of the second training t2 is the predicted deep body temperature TP1 at the end time of the first training t1. Is the same as (TP2 = TP1). In short, the prediction unit 133 predicts the core body temperature of the user 200 when the training is performed, based on at least the initial temperature T0 and the training information.

The comparison unit 134 compares the predicted value of the core body temperature of the user 200 with the measured value of the core body temperature of the user 200. More specifically, the comparison unit 134 creates a deep body temperature transition (measured deep body temperature transition TR) from the measured value (exercise temperature) of the core body temperature of the user 200, and uses the created measured deep body temperature transition TR as the prediction unit 133. Compare with the predicted core body temperature transition TP predicted in.

The comparison unit 134 compares the measured deep body temperature transition TR of the user 200 with the predicted deep body temperature transition TP at the time of the first training (first period P1). The comparison unit 134 calculates the slope of the measured deep body temperature transition TR and the slope of the predicted deep body temperature transition TP at the time of performing the first training, and compares the magnitude relationship.

As shown in FIGS. 7 and 8, the comparison unit 134 regards the straight line connecting the initial temperature T0 and the measured deep body temperature TR1 at the end of the first training t1 as the measured deep body temperature transition TR at the time of the first training. (Approximate). Then, paying attention to this straight line, the slope α of the measured deep body temperature transition TR at the time of the first training is calculated.

Specifically, the slope α of the measured deep body temperature transition TR during the first training is expressed by the following equation (1).
[Number 1]
α = (TR1-T0) / P1 ・・・ (1)

Further, the comparison unit 134 calculates the slope β of the predicted deep body temperature at the time of the first training from the initial temperature T0 and the predicted deep body temperature TP1 at the end time t1 of the first training.

The slope β of the predicted deep body temperature transition TP at the time of the first training is expressed by the following equation (2).
[Number 2]
β = (TP1-T0) / P1 ... (2)

The comparison unit 134 compares the magnitude relationship between the slope α of the measured deep body temperature transition TR and the slope β of the predicted deep body temperature transition TP. FIG. 7 shows an example of the comparison result when the slope α of the measured deep body temperature transition TR is smaller than the slope β of the predicted deep body temperature transition TP. FIG. 8 shows an example of the comparison result when the slope α of the measured deep body temperature transition TR is larger than the slope β of the predicted deep body temperature transition TP.

Further, the comparison unit 134 compares the actually measured deep body temperature transition TR of the user 200 with the predicted deep body temperature transition TP at the time of performing the second training (second period P2). The comparison unit 134 calculates the slope of the measured deep body temperature transition TR and the slope of the predicted deep body temperature transition TP at the time of performing the second training, and compares the magnitude relationship.

As shown in FIGS. 9 and 10, the comparison unit 134 draws a straight line connecting the measured deep body temperature TR1 at the end of the first training t1 and the measured deep body temperature TR2 at the end of the second training t2 at the time of the second training. It is regarded as (approximate) the measured deep body temperature transition TR. Then, paying attention to this straight line, the slope γ of the measured deep body temperature transition TR at the time of the second training is calculated.

Specifically, the slope γ of the measured deep body temperature transition TR at the time of performing the second training is expressed by the following equation (3).
[Number 3]
γ = (TR2-TR1) / P2 ・・・ (3)

Further, the comparison unit 134 calculates the slope δ of the predicted deep body temperature at the time of the second training from the predicted deep body temperature TP1 at the end of the first training t1 and the predicted deep body temperature TP2 at the end of the second training t2.

The slope δ of the predicted deep body temperature transition TP at the time of the second training is expressed by the following equation (4).
[Number 4]
δ = (TP2-TP1) / P2 ... (4)

Here, since the second training is determined so that the core body temperature of the user 200 is maintained, the predicted deep body temperature TP2 at the end time of the second training t2 is the same as the predicted deep body temperature TP1 at the end time t1 of the first training. Yes, the slope δ of the predicted deep body temperature transition TP at the time of the second training is 0.

The comparison unit 134 compares the magnitude relationship between the slope γ of the measured deep body temperature transition TR and the slope δ of the predicted deep body temperature transition TP. Since the slope δ of the predicted deep body temperature transition TP in the second training is 0, the comparison unit 134 shows that the slope γ of the measured deep body temperature transition TR at the time of performing the second training is 0 or more (positive value) or less than 0. Compare whether it is (negative value).

FIG. 9 shows an example of the comparison result when the slope γ of the measured deep body temperature transition TR is smaller than the slope δ (= 0) of the predicted deep body temperature transition TP. FIG. 10 shows an example of the comparison result when the slope γ of the measured deep body temperature transition TR is larger than the slope δ (= 0) of the predicted deep body temperature transition TP.

The decision unit 132 generates (determines) new training information based on the comparison result by the comparison unit 134.

In the present embodiment, the determination unit 132 changes the training intensity based on the comparison result by the comparison unit 134.

For example, the determination unit 132 changes the training intensity based on the magnitude relationship between the slope of the measured deep body temperature transition TR and the slope of the predicted deep body temperature transition TP.

Specifically, in the determination unit 132, when the slope α of the measured deep body temperature transition TR during the first training is smaller than the slope β of the predicted deep body temperature transition TP during the first training (α <β; FIG. 7). See), make the intensity of the first training higher than the original intensity.

Further, in the determination unit 132, when the slope α of the measured deep body temperature transition TR during the first training is larger than the slope β of the predicted deep body temperature transition TP during the first training (α> β; see FIG. 8). Make the intensity of the first training smaller than the original intensity. However, in this case (when α> β), whether or not to change the intensity of the first training to be reduced depends on the subjectivity of the user 200 (for example, feedback from the user 200 via the terminal device 20) and the training policy. It may be decided by such as. That is, if the user 200 does not feel that the load is large even with the original strength and does not want to reduce the strength of the first training, it is not always necessary to reduce the strength of the first training.

Further, in the determination unit 132, when the slope γ of the measured deep body temperature transition TR during the second training is smaller than the slope δ (= 0) of the predicted deep body temperature transition TP during the second training (γ <0; FIG. 9), make the intensity of the second training higher than the original intensity. However, even in this case (when γ <0), if the measured deep body temperature TR2 at the end point t2 of the second training is equal to or higher than the predicted deep body temperature TP2, it is not always necessary to increase the intensity of the second training. ..

Further, in the determination unit 132, when the slope γ of the measured deep body temperature transition TR during the second training is larger than the slope δ (= 0) of the predicted deep body temperature transition TP during the second training (γ> 0; FIG. 10), make the intensity of the second training smaller than the original intensity. However, in this case (when γ> 0), whether or not to change the intensity of the second training to be reduced may be determined by the subjectivity of the user 200, the training policy, and the like. That is, if the user 200 does not feel that the load is large even with the original strength and does not want to reduce the strength of the second training, it is not always necessary to reduce the strength of the second training.

11 to 19 show typical examples of comparison results by the comparison unit 134.

In FIG. 11, the slope α of the measured deep body temperature transition TR during the first training is equal to the slope β of the predicted deep body temperature transition TP (α = β), and the slope γ of the measured deep body temperature transition TR during the second training is The case where the slope δ (= 0) of the predicted deep body temperature transition TP is equal to (γ = 0) is shown. In this case, the determination unit 132 does not have to generate new training information.

FIG. 12 shows the case where α = β and γ <0. In this case, the determination unit 132 may generate new training information. In the new training information, the determination unit 132 may make the intensity of the second training higher than the original intensity.

FIG. 13 shows the case where α = β and γ> 0. In this case, the determination unit 132 may generate new training information. In the new training information, the determination unit 132 may make the intensity of the second training smaller than the original intensity.

FIG. 14 shows the case where α <β and γ = 0. In this case, the determination unit 132 may generate new training information. In the new training information, the determination unit 132 may make the intensity of the first training higher than the original intensity.

FIG. 15 shows the case where α <β and γ <0. In this case, the determination unit 132 may generate new training information. In the new training information, the determination unit 132 may make the intensity of the first training higher than the original strength and the strength of the second training higher than the original strength.

FIG. 16 shows the case where α <β and γ> 0. In this case, the determination unit 132 may generate new training information. In the new training information, the determination unit 132 may make the intensity of the first training larger than the original intensity and the intensity of the second training smaller than the original intensity.

FIG. 17 shows the case where α> β and γ = 0. In this case, the determination unit 132 may generate new training information. In the new training information, the determination unit 132 may make the intensity of the first training smaller than the original intensity.

FIG. 18 shows the case where α> β and γ <0. In this case, the determination unit 132 may generate new training information. In the new training information, the determination unit 132 may make the intensity of the first training smaller than the original intensity. In this case, as shown by the broken line in FIG. 18, the measured deep body temperature at t2 at the end of the second training may be smaller than the threshold temperature Tth. In this case, the determination unit 132 may make the intensity of the first training smaller than the original intensity and the intensity of the second training larger than the original intensity in the new training information.

FIG. 19 shows the case where α> β and γ> 0. In this case, the determination unit 132 may generate new training information. In the new training information, the determination unit 132 may make the intensity of the first training smaller than the original intensity and the intensity of the second training smaller than the original intensity.

Note that the comparison unit 134 does not have to be configured to obtain (linearly approximate) the measured deep body temperature transition TR only from the initial temperature T0 and the measured deep body temperature TR1 and TR2. The comparison unit 134 further uses one or more measured values measured in the first period P1 and the second period P2 other than the initial temperature T0 and the measured deep body temperature TR1 and TR2 to obtain the measured deep body temperature transition TR. You may ask.

In this way, the determination unit 132 generates (determines) new training information in which the training intensity is changed, based on the result of the training actually performed by the user 200. Therefore, it is possible to determine the intensity of training according to the individual characteristics of the user 200. In particular, by generating (determining) training information based on the measured value of the core body temperature of the user 200 as the biological information of the user 200, it becomes easy for the user 200 to achieve the heat load training.

The determination unit 132 may change the clothes of the user 200 based on the comparison result compared by the comparison unit 134. For example, by changing the combination of clothes to a combination in which the core body temperature of the user 200 is more likely to increase (for example, the clo value is higher), it is possible to promote the increase in the body temperature of the user 200 by training. Thereby, for example, it is possible to promote an increase in the core body temperature (exercise temperature) of the user 200 without changing the intensity of training.

For example, if the intensity of the training newly determined by the determination unit 132 (set time per kilometer) is larger than the training level of the user 200 (shortest time per kilometer), the training of the newly determined intensity is performed by the user 200. Is difficult to implement. In such a case, the clothing can be changed to a combination in which the temperature rises more easily, and the training intensity can be reduced.

If the strength cannot be equal to or lower than the training level even if the combination of clothes is changed, the information processing apparatus 10 uses the combination of clothes whose temperature tends to rise more easily, and shifts to a warmer time zone during the training implementation time. The change of the training type, the change of the training type, and the like may be proposed to the user 200.

The comparison result by the comparison unit 134 and the new training information determined by the determination unit 132 are stored in the user information storage unit 121 (history information storage unit 1214) in association with the identification information of the user 200 together with the date and time information and the like. Will be done.

When the user 200 carries out the next training, the output unit 137 outputs the new training information determined by the determination unit 132 to the terminal device 20.

In short, the prediction unit 133 predicts the body temperature of the user 200 (here, the core body temperature) when the user 200 performs training according to the training information (original training information) determined by the determination unit 132. The body temperature information acquisition unit 1319 acquires the measured value of the body temperature of the user 200 (here, the core body temperature) measured at the time when the user 200 carries out the training according to the training information (original training information). The comparison unit 134 is a predicted value of the body temperature of the user 200 predicted by the prediction unit 133 (here, the core body temperature) and a measured value of the body temperature of the user 200 (here, the core body temperature) acquired by the body temperature information acquisition unit 1310. And compare. The determination unit 132 determines new training information regarding new training to be performed by the user, which is different from the original training information, based on at least the comparison result in the comparison unit 134, the attribute information, and the environmental information. The output unit 137 outputs new training information determined by the determination unit 132.

The evaluation unit 135 evaluates the training result of the user 200 who has performed training according to the training information based on the comparison result in the comparison unit 134.

Here, the evaluation unit 135 evaluates the degree of achievement of the heat load training. More specifically, in the evaluation unit 135, when the user's core body temperature (exercise temperature) measured by the measurement unit 31 at the time of training is maintained at P0 and the threshold temperature Tth or more for a predetermined period, the training is heat-loaded. Evaluate that the training has been achieved. For example, the evaluation unit 135 evaluates as "achieved" in the case of FIGS. 11, 13, 17, and 19. Further, the evaluation unit 135 evaluates as "not achieved" in the case of FIGS. 12, 14, 15, and 16. Further, the evaluation unit 135 evaluates as "achieved" in the case of the solid line in FIG. 18, and evaluates as "not achieved" in the case of the broken line. The evaluation result information representing the evaluation result by the evaluation unit 135 is output (transmitted) to the terminal device 20 after the training is completed.

The evaluation result by the evaluation unit 135 is stored in the user information storage unit 121 (history information storage unit 1214) in association with the identification information of the user 200 together with the date and time information and the like.

The target information acquisition unit 1313 acquires target information regarding the target of the user 200. The schedule unit 136 determines the training schedule of the user 200 based on the target information. The schedule unit 136 updates the training schedule based on the evaluation result by the evaluation unit 135.

The goal of the user 200 is any training goal that the user 200 wants to achieve. The goal is, for example, to participate in a full marathon event that will be held two months later.

Here, in order to acquire endurance exercise ability and performance improvement (to obtain the effect of heat acclimatization), heat load training should be performed about 3 to 10 times, and the training interval should not be open for 3 consecutive days or more. Is recommended (Reference 2: Heat Measures Guidebook for Athletes, Page 13, Lines 9-14, Lines 8; published by Japan Institute of Sports Sciences, Japan Institute of Sports Sciences). For example, if the heat load training is scheduled to be performed 10 times and it is evaluated that the heat load training has been achieved once, the training progress at that time can be expressed as 10%. If the heat load training is achieved again within 3 days, the training progress increases to 20%, but if the training is not performed within 3 days, or if the training is performed, the heat load training cannot be achieved. If so, the training progress drops to 0%.

The schedule unit 136 determines the training schedule of the user 200 based on the target of the user 200 stored in the target information storage unit 1213. For example, if the goal of the user 200 is to participate in a full marathon event held two months later, the heat load training should be completed at least one month before the event (for example, the above-mentioned training progress is 100). %) Is preferable. Therefore, the schedule unit 136 determines the schedule for carrying out the heat load training once every three days. If the training progress created by the schedule unit 136 cannot be achieved within the scheduled schedule while the user 200 is executing the schedule, the schedule unit 136 updates the schedule. By doing so, it becomes easier for the user 200 to complete the heat load training.

Of course, the goal and training schedule of the user 200 is not limited to the above example of the full marathon, and may be any goal and a schedule corresponding to the goal.

The output unit 137 outputs various information via the communication unit 11.

The output unit 137 outputs (transmits) the training information (training type, training intensity, training time, clothing of the user 200 in the training) determined by the determination unit 132 to the terminal device 20. The output unit 137 outputs (transmits) new training information determined by the determination unit 132 to the terminal device 20. The output unit 137 outputs (transmits) the comparison result information regarding the comparison result by the comparison unit 134 to the terminal device 20. The output unit 137 outputs (transmits) the evaluation result information regarding the evaluation result of the training by the evaluation unit 135 to the terminal device 20. The output unit 137 outputs (transmits) the schedule information regarding the training schedule determined by the schedule unit 136 to the terminal device 20. For example, the evaluation result information may include advice information indicating how to improve the training. The advice information may include not only information on how to improve training, but also information on behavior, sleep, or diet in daily life.

(2.4) Operation Hereinafter, an example of the operation of the support system 100 will be briefly described with reference to FIGS. 22 to 24.

FIG. 22 shows a flowchart of the operation of the support system 100 (information processing apparatus 10) when the user 200 performs initial registration. The user 200 accesses the information processing device 10 by using the terminal device 20, and registers the user by creating his / her own account (ST1). At this time, unique identification information (ID) is assigned to each user 200. The identification information (ID) may be managed by a password.

Further, the user 200 inputs attribute information (attribute information with little fluctuation in the long term), possessed clothing information, etc. via the terminal device 20 while logged in to his / her own account. The information processing device 10 acquires the attribute information of the user 200 from the terminal device 20 (ST2), acquires the possessed clothing information (ST3), associates it with the identification information of the user 200, and stores it in the user information storage unit 121. do. If the user 200 desires, operations such as user registration and input of attribute information may be performed by an agent (family, trainer, etc.) of the user 200.

FIG. 23 shows a flowchart of the operation of the support system 100 (information processing apparatus 10) when the user 200 performs training once according to the training information.

The user 200 logs in to his / her own account using the terminal device 20 and instructs the information processing device 10 to start training (ST101). The training start instruction may include, for example, information such as a training implementation time, a training location, and a training type. The start time of the training may be replaced with the current time. Further, when the terminal device 20 has a GPS function, the training location may be substituted by the position of the terminal device 20 indicated by GPS.

When the start of training is instructed, the information processing apparatus 10 acquires the attribute information (attribute information that may fluctuate in the medium term and the initial temperature T0) of the user 200 (ST102). The attribute information (attribute information that may change in the medium term) of the user 200 may be acquired in advance before the start instruction.

The information processing device 10 acquires environmental information (environmental information that fluctuates in the short term) (ST103). The information processing device 10 acquires environmental information from a measuring device, a service provider, etc. installed at the training site.

Further, in the information processing apparatus 10, whether or not the user 200 has previously trained using the support system 100 (that is, whether or not the history information storage unit 1214 has a history of comparison results). Is determined (ST104).

When the history information storage unit 1214 has a history (ST104: Yes), the information processing device 10 (decision unit 132) determines the training information based on at least the comparison result, the attribute information, and the environmental information (ST105). Even if there is a history, for example, the date of the previous training may be a past date to the extent that the effect of the training is lost, or the weather may be significantly different from the date of the previous training. If there are circumstances, the comparison results do not necessarily have to be referred to when determining training information. Further, the comparison result referred to when determining the training information is not limited to the previous training, but may be the result of the past multiple trainings.

On the other hand, when there is no history (ST104: No), the information processing apparatus 10 (decision unit 132) determines the training information at least based on the attribute information and the environment information (ST106).

The information processing device 10 outputs the determined training information to the terminal device 20 by the output unit 137 (ST107).

The user 200 confirms the training information output from the output unit 137 on the terminal device 20, and decides whether to adopt or not (ST108).

When the information on whether or not to adopt the training information is obtained (ST108: No), the information processing apparatus 10 performs a process of generating (determining) new training information by changing the elements that have been rejected.

When the training information is adopted (ST108: Yes), the user 200 carries out the training according to the training information.

The information processing apparatus 10 predicts the core body temperature (deep body temperature transition) of the user 200 at the time of training based on the adopted training information (ST109). The user 200 carries out training based on the adopted training information (ST110).

Further, after the training is completed, the information processing device 10 acquires the measured value of the core body temperature (exercise temperature) of the user 200 from the terminal device 20 (ST111).

The information processing apparatus 10 compares the predicted value of the core body temperature with the measured value of the core body temperature (ST112), evaluates the training result based on the comparison result (ST113), and outputs the evaluation result to the terminal device 20 (ST113). ST114). The user 200 confirms the training result on the terminal device 20.

FIG. 24 shows a flowchart of the operation of the support system 100 (information processing apparatus 10) when the user 200 performs training according to the training schedule.

The user 200 logs in to his / her own account using the terminal device 20 and inputs the target information. The information processing apparatus 10 acquires the input target information (ST201), and determines the training schedule based on the target information, the user's attribute information, and the like (ST202).

User 200 trains using the support system 100 (ST203). The flow of training using the support system 100 is as described with reference to FIG. 23.

When the training is completed, the information processing apparatus 10 determines whether or not the training result has achieved the heat load training (ST204). If not achieved (ST204: No), the information processing apparatus 10 updates the training schedule.

If the training schedule is completed by completing this training (ST206: Yes), the information processing apparatus 10 completes the process. If the training schedule is not completed (ST206: No), the information processing apparatus 10 waits for the next training. The information processing device 10 may notify the terminal device 20 when the next training scheduled in the training schedule is approaching.

(3) Modifications The embodiments of the present disclosure are not limited to the above embodiments. The above embodiment can be variously modified according to the design and the like as long as the subject of the present disclosure can be achieved. Further, the same function as the information processing apparatus 10 may be embodied by an information processing method, a (computer) program, a non-temporary recording medium on which the program is recorded, or the like.

In the information processing method according to one aspect, attribute information regarding the attributes of the user 200 is acquired (ST2, ST102), environmental information regarding the training environment of the user 200 is acquired (ST103), and at least based on the attribute information and the environmental information. , The training information regarding the training to be performed by the user is determined (ST105, ST106), and the determined training information is output (ST107).

The following is a list of modified examples of the above embodiment. Hereinafter, the above-described embodiment will be referred to as a “basic example”. The basic example and the modification described below can be applied in combination as appropriate.

The support system 100 in the present disclosure includes a computer system in, for example, an information processing device 10. The computer system mainly consists of a processor and a memory as hardware. The function as the information processing apparatus 10 in the present disclosure is realized by the processor executing the program recorded in the memory of the computer system. The program may be pre-recorded in the memory of the computer system or may be provided through a telecommunication line, and may be recorded on a non-temporary recording medium such as a memory card, an optical disk, a hard disk drive, etc., which can be read by the computer system. May be provided. The processor of a computer system is composed of one or more electronic circuits including a semiconductor integrated circuit (IC) or a large scale integrated circuit (LSI). The integrated circuit such as IC or LSI referred to here has a different name depending on the degree of integration, and includes an integrated circuit called a system LSI, VLSI (Very Large Scale Integration), or ULSI (Ultra Large Scale Integration). Further, an FPGA (Field-Programmable Gate Array) programmed after the LSI is manufactured, or a logical device capable of reconstructing the junction relationship inside the LSI or reconfiguring the circuit partition inside the LSI should also be adopted as a processor. Can be done. A plurality of electronic circuits may be integrated on one chip, or may be distributed on a plurality of chips. A plurality of chips may be integrated in one device, or may be distributed in a plurality of devices. The computer system referred to here includes a microcontroller having one or more processors and one or more memories. Therefore, the microprocessor is also composed of one or a plurality of electronic circuits including a semiconductor integrated circuit or a large-scale integrated circuit.

Further, it is not an essential configuration for the support system 100 that a plurality of functions in each of the information processing device 10, the terminal device 20, and the measuring device 30 of the support system 100 are integrated in one housing. The components of the information processing device 10, the terminal device 20, or the measuring device 30 may be dispersedly provided in a plurality of housings. On the contrary, some of the functions of the information processing device 10, the terminal device 20, and the measuring device 30 may be integrated in one housing. For example, the information processing device 10 and the terminal device 20 may be integrated in one housing. Further, at least a part of the functions of the support system 100, for example, at least a part of the functions of the processing unit 13 and the like may be realized by, for example, a server or a cloud (cloud computing).

(3.1) Modification 1
In the support system 100 of this modification, mainly, as shown in FIG. 25, the information acquisition unit 131 of the processing unit 13 of the information processing apparatus 10 is the pre-training information acquisition unit 1317 (“pre-information acquisition unit” in FIG. 25). It is different from the support system 100 of the basic example in that it is further provided. In this modification, the same components as those of the support system 100 of the basic example may be designated by the same reference numerals and description thereof may be omitted as appropriate.

The pre-training information acquisition unit 1317 acquires pre-training information which is information of the user 200 related to the training before the user 200 performs the training.

The pre-training information may include health status information regarding the health status of the user 200 before training. The health status information may include information indicating at least one of the physical condition, constitution, and behavior history of the user 200. Examples of the physical condition of the user 200 include the presence / absence of accumulated fatigue, the presence / absence of diarrhea, the presence / absence of fever, the presence / absence of sleep deprivation, the presence / absence of a hangover, and the like. Examples of the constitution of the user 200 include the presence or absence of obesity, the presence or absence of a history of heat stroke, and the like. Examples of the behavior history of the user 200 include the presence / absence of movement between areas with a large temperature difference, the amount of training on the previous day, and the like. Movements between regions with large temperature differences include, for example, short-term movements from regions located in the northern hemisphere to regions located in the southern hemisphere during the year-end and early year, from regions with hot climates to hot climates. Short-time movement to an area, or conversely, short-time movement from a hot climate area to a cool climate area. For example, when moving from a cool climate area to a hot climate area in a short time, the body of the user 200 is not yet accustomed to the hot environment. Therefore, when determining the training information, it is preferable to consider the behavior history of the user 200.

The health condition information can be acquired from the terminal device 20 by the pre-training information acquisition unit 1317 of the processing unit 13, for example, via the communication network 40.

Health status information can include information that can change in the short term and information that can change in the medium term.

Information that can change in the short term includes the physical condition of the user 200, the behavior history, and the like.

Information that can change in the medium term includes the constitution of the user 200 and the like.

Information that may change in the short term may be acquired every time the user 200 conducts training. The information that may change in the short term is after the user 200 instructs the start of training (ST101 in FIG. 23), after the attribute information of the user 200 is acquired (ST102), or after the environmental information is acquired (ST103). It may be acquired each time later. Information that may change in the short term is presented to the user 200 by displaying the question screen Sc1 on which the question items as shown in FIG. 26 are described on the image display device of the terminal device 20, and the user 200 presents the terminal. It may be obtained by answering a question using the device 20. On the question screen Sc1 of FIG. 26, the presence / absence of thirst, the presence / absence of poor physical condition, the presence / absence of sleep deprivation, the presence / absence of a hangover, and the presence / absence of excessive training on the previous day are presented to the user 200 as questions, and each question is answered. On the other hand, the user 200 is made to select an answer from two options of "Yes" or "No". The question on the question screen Sc1 in FIG. 26 is a logical sum in which the result is "Yes" if even one of the plurality of questions is answered "Yes". In the question screen Sc1 of FIG. 26, there are two choices for the question, "Yes" or "No", but the question is not limited to this, and the answer to the question (for example, the degree of thirst) is 3 or more. It may be an alternative type that allows selection from a plurality of stages. The terminal device 20 may output a question by sound by a voice output device. The terminal device 20 may receive a response from the user 200 by voice by the voice input device.

It is preferable that the information that can change in the medium term is registered once when the user 200 uses the system for the first time, and then the information is updated in a timely manner. Among the constitutions of the user 200, for example, the presence or absence of obesity is estimated by the information processing device 10 (processing unit 13) or an external processing device based on the height, weight, body fat percentage, etc. acquired by the attribute information acquisition unit 1311. You may.

The decision unit 132 may determine the training information based on the health condition information. The prediction formula may include health information as a parameter.

The decision unit 132 may decide that the training should not be performed based on the pre-training information. For example, if it is determined that training should not be performed based on the health condition information acquired by the pre-training information acquisition unit 1317, the determination unit 132 determines that training should not be performed. For example, if even one of the questions described in the question screen Sc1 of FIG. 26 answers "Yes", the decision-making unit 132 determines that the training should not be carried out. If it is determined that training should not be carried out, the decision-making unit 132 does not carry out, for example, the creation of training information. When it is determined that the training should not be performed, the information processing apparatus 10 causes, for example, the terminal apparatus 20 of the user 200 to display a caution screen Sc2 indicating that the training menu as shown in FIG. 27 cannot be displayed. May be good.

The pre-training information may include warm-up information regarding the warm-up performed by the user 200 before the training. The warm-up information may include at least one of warm-up presence / absence, warm-up type, time, pace, and distance. Types of warm-up include running, walking, stretching and the like. As an example of warm-up, running for 1 km at a pace of 7 minutes / km can be mentioned.

The warm-up information can be acquired from the terminal device 20 by the pre-training information acquisition unit 1317 of the processing unit 13, for example, via the communication network 40.

The warm-up information is presented to the user 200 by displaying the question screen Sc3 on which the question items as shown in FIG. 28 are described on the image display device of the terminal device 20, and the user 200 uses the terminal device 20. It may be obtained by answering the question. The terminal device 20 may output a question by sound by a voice output device. The terminal device 20 may receive a response from the user 200 by voice by the voice input device. On the question screen Sc3 of FIG. 28, the presence / absence of warm-up, the pace and distance of running as warm-up are presented to the user 200 as questions.

The decision unit 132 may determine the training information based on the warm-up information in the pre-training information. The determination unit 132 may determine the first training intensity and the first period P1 based on the warm-up information in the pre-training information. For example, the processing unit 13 predicts the core body temperature Tw at the warm-up end time tw when the warm-up period Pw has elapsed from the training start time t0 by using the user's initial temperature T0 and the warm-up information (FIG. 29). The determination unit 132 determines the first training intensity and the first period P1 by regarding the core body temperature Tw of the warm-up end time tw as the initial temperature of the user 200 in the first prediction formula. As the first prediction formula and the second prediction formula used by the determination unit 132 for determining the training information, for example, the same formula may be used depending on whether the warm-up is performed or not. In that case, as shown in FIG. 29, the time change of the body temperature of the user 200 when the warm-up is performed (see the solid line A1 in FIG. 29) is the time change of the body temperature of the user 200 when the warm-up is not performed (FIG. 29). With respect to the broken line A0 of 29), the engine is shifted backward by the time considering the warm-up period Pw while maintaining its inclination. In FIG. 29, the first period in which the first training is carried out is shown as “P1” when the warm-up is carried out, and the second period in which the second training is carried out in the case of carrying out the warm-up is shown as “P2”. There is. Further, in FIG. 29, for reference, the first period in which the first training is carried out when the warm-up is not carried out is “P1A”, and the second period in which the second training is carried out when the warm-up is not carried out is “P2A”. Is shown as. Of course, the determination unit 132 may use different prediction formulas depending on whether the warm-up is performed or not. The prediction formula when performing warm-up may include warm-up information (pace, distance, etc.) as parameters.

The pre-training information may include ingested water intake information regarding the water that the user 200 plans to ingest during training. The water intake information may include at least one of the presence or absence of water intake, the amount of water intake, the water temperature, and the timing of intake.

The intake water intake information can be acquired from the terminal device 20 by the pre-training information acquisition unit 1317 of the processing unit 13, for example, via the communication network 40.

The intake water intake information is presented to the user 200 by displaying the question screen Sc3 on which the question items as shown in FIG. 28 are described on the image display device of the terminal device 20, and the user 200 uses the terminal device 20. It may be obtained by answering the question. The terminal device 20 may output a question by sound by a voice output device. The terminal device 20 may receive a response from the user 200 by voice by the voice input device. On the question screen Sc3 of FIG. 28, the presence / absence of (planned) water intake, the amount of water intake, the water temperature (whether it is cold or normal temperature), and the timing of intake are presented to the user 200 as questions.

The decision unit 132 may determine the training information based on the water intake information in the pre-training information.

For example, the first prediction formula includes attribute information of the user 200 (for example, initial temperature T0), clothing information (for example, clo value), training intensity (for example, set time), environmental information (for example, temperature), and pre-training information (for example). , Water intake during training, water temperature, water intake timing) and the like can be included as parameters.

In addition, the second prediction formula includes attribute information of the user 200 (for example, initial temperature T0), clothing information (for example, clo value), training intensity (for example, set time), environmental information (for example, temperature), and pre-training information (for example). , Water intake during training, water temperature, water intake timing) and the like can be included as parameters.

As described above, in the support system 100 of this modification, the processing unit 13 of the information processing apparatus 10 includes a pre-training information acquisition unit 1317 that acquires pre-training information. The pre-training information is at least one of health condition information regarding the health condition of the user 200 before training, warm-up information regarding the warm-up performed by the user 200 before the training, and water intake information regarding the water intake by the user 200 during the training. Can include one.

The pre-training health status of the user 200 affects the core body temperature transition of the user 200 during training. For example, if fatigue is accumulated due to long-term high-intensity training the day before, the core body temperature is likely to rise as compared with the case where it is not. By acquiring the health condition information about the health condition of the user 200 before the training, it is possible to infer the cause when the magnitude relationship between the inclination of the predicted deep body temperature transition TP and the inclination of the measured deep body temperature transition TR is significantly different. Therefore, the accuracy of the training information presented to the user 200 can be improved, and the safety of the user 200 during training can be improved.

Further, the warm-up performed by the user 200 before the training and the water intake during the training may affect the transition of the core body temperature of the user 200 in the training. Therefore, by acquiring warm-up information and / or water intake information before training, it is possible to predict the core body temperature including the heat balance by them, and the accuracy of the training information presented to the user 200 is improved.

(3.2) Modification 2
In the support system 100 of this modification, mainly, as shown in FIG. 30, the information acquisition unit 131 of the processing unit 13 of the information processing apparatus 10 is the training result information acquisition unit 1318 (“actual information acquisition unit” in FIG. 30). It is different from the support system 100 of the modification 1 in that it is further provided. In this modification, the same components as those of the support system 100 of the modification 1 may be designated by the same reference numerals and description thereof may be omitted as appropriate.

The training performance information acquisition unit 1318 acquires training performance information regarding the training results actually performed by the user.

The training performance information may include at least one of the pace, time, distance, wearing condition of clothes during training, water intake, water intake temperature, water intake timing, and environmental information of the training carried out by the user 200. It is preferable that the training performance information is acquired every time after the measured value of the biological information (body temperature) of the user 200 is acquired by the biological information acquisition unit 1314 (body temperature information acquisition unit 1319). The training performance information is presented to the user 200 by displaying the question screen Sc4 on which the question items as shown in FIG. 31 are described on the image display device of the terminal device 20, and the user 200 uses the terminal device 20. It may be obtained by answering the question. The terminal device 20 may output a question by sound by a voice output device. The terminal device 20 may receive a response from the user 200 by voice by the voice input device. On the question screen Sc4 of FIG. 31, whether the training intensity (first training intensity: 5 minutes 30 seconds / km, second training intensity: 6 minutes 30 seconds / km) was achieved, or the training time (first period P1: 50 minutes). , 2nd period P2: 20 minutes), how the clothes were worn (worn according to the menu, taken off in the middle, or rolled up), the amount of water actually ingested, the temperature of the ingested water (cold) Whether it is at room temperature) and the timing of water intake are presented to the user 200 as questions.

At least a part of the training performance information may be acquired via an information terminal (for example, the terminal device 20) possessed by the user 200. For example, the pace, time, distance, etc. of training (running, biking, etc.) are measured by devices with built-in GPS (tablet-type information terminals, wristwatch-type information terminals, and other wearable terminals), and are measured via a communication network. May be obtained. The wearing status of clothes during training can be determined by using techniques such as image analysis to capture images of the user 200 taken by a camera built into a mobile information terminal such as a smartphone or tablet terminal or a surveillance camera installed on the street. It may be estimated by analysis. The water intake amount and the water intake temperature may be estimated from the purchase information when the user 200 purchases the beverage through a mobile information terminal such as a smartphone or a tablet terminal, a wristwatch-type information terminal, or the like before and after the training. .. The environmental information may be measured, for example, by an appropriate measuring device installed at the training site. The environmental information may be substituted with actual data provided by a service provider or the like that provides information on the weather. The type, undulation, altitude, etc. of the running course may be measured by, for example, a device having a built-in GPS (tablet type information terminal, wristwatch type information terminal, etc.) and acquired via a communication network.

As described in the basic example, the prediction unit 133 predicts the core body temperature of the user 200 when the user 200 performs training according to the training information. The prediction unit 133 creates a core body temperature transition (predicted deep body temperature transition TP) of the user 200 at the time of training by using the prediction formula.

In this modification, the prediction unit 133 predicts the core body temperature of the user 200 based on the training performance information. For example, if the values of the parameters included in the prediction formula are different between the pre-training information or the information at the time of determining the training information and the training performance information, the prediction unit 133 uses the training performance information as the basis for the user. (Re) predict core body temperature transitions during 200 trainings.

The first prediction formula includes attribute information of the user 200 (for example, initial temperature T0), clothing information (for example, clo value), training intensity (for example, actual running time), environmental information (for example, actual measured value of temperature), and training. The amount of water ingested, the temperature of water, the timing of water intake, the wearing condition of clothes, and the like can be included as parameters.

The second prediction formula includes attribute information of the user 200 (for example, initial temperature T0), clothing information (for example, clo value), training intensity (for example, actual running time), environmental information (for example, actual measured value of temperature), and training. The amount of water ingested, the temperature of water, the timing of water intake, the wearing condition of clothes, and the like can be included as parameters.

With reference to FIG. 32, an example of the operation in which the information processing apparatus 10 (prediction unit 133) of this modification predicts the core body temperature transition of the user 200 will be described. The operation of the information processing apparatus 10 of this modification is basically the same as the operation of the information processing apparatus 10 of the basic example described with reference to FIG. The difference is that the process ST303 is further provided.

For example, the information processing apparatus 10 predicts the core body temperature (deep body temperature transition) of the user 200 at the time of training based on the adopted training information, pre-training information, and the like (ST109). The user 200 performs training according to the training information (ST110).

Subsequently, the information processing apparatus 10 acquires training performance information (ST301). The information processing apparatus 10 determines whether or not there is a difference between the content of the pre-training information and the content of the training performance information (ST302). When there is no difference (ST302: No), the information processing apparatus 10 adopts the predicted deep body temperature transition TP predicted by ST109 as the prediction result. On the other hand, when there is a difference (ST302: Yes), the information processing apparatus 10 re-predicts the core body temperature (deep body temperature transition) of the user 200 at the time of training based on the training performance information (ST303). Then, the information processing apparatus 10 adopts the predicted deep body temperature transition TP predicted by ST303 as the prediction result. Subsequent steps (step ST110 and subsequent steps) are the same as in the case of the basic example.

The comparison unit 134 compares the predicted value of the core body temperature based on the training record information with the measured value of the core body temperature.

The evaluation unit 135 evaluates the training result based on the comparison result in the comparison unit 134. The evaluation unit 135 evaluates the degree of achievement of the heat load training. The evaluation result information representing the evaluation result is output to the terminal device 20 after the training is completed. If the cause of the evaluation result can be found from the pre-training information, training performance information, etc., the output unit 137 may output an explanation regarding the cause. For example, if it is presumed that the heat load training has not been achieved due to excessive fluid intake during training, the output unit 137 may output an explanation to that effect.

The determination unit 132 may determine new training information based on the comparison result between the predicted value of the core body temperature and the measured value of the core body temperature based on the training record information by the comparison unit 134.

In the support system 100 of this modification, the processing unit 13 of the information processing apparatus 10 includes a training result information acquisition unit 1318 for acquiring training result information. Therefore, by creating, comparing, and evaluating predicted values based on the training performance information, it is possible to determine training information that is more suitable for the characteristics of each individual user 200. In addition, by predicting the core body temperature based on the training performance information, the accuracy of predicting the core body temperature during training is improved.

In the support system 100 of this modification, it is not essential that the information processing device 10 includes the pre-training information acquisition unit 1317. In that case, in the operation of the information processing apparatus 10 (see FIG. 32), the step (step S109 and step ST302) in which the information processing apparatus 10 predicts the core body temperature transition based on the training information and the pre-training information may be omitted. ..

(3.3) Modification 3
The support system 100 of the present modification is different from the support system 100 of the basic example in that it mainly has a function (notification unit 35) of notifying the user 200 who is performing the training of information about the training. In the support system 100 of this modification, the same components as those of the support system 100 of the basic example may be designated by the same reference numerals and description thereof may be omitted as appropriate.

As shown in FIG. 33, the measuring device 30 includes a measuring unit 31, a communication unit 32, a storage unit 33, a notification unit 35, and a housing 34.

The measuring device 30 of this modification transmits the body temperature (tympanic membrane temperature) measured by the measuring unit 31 to the terminal device 20 in real time by the communication unit 32. The body temperature information acquisition unit 1319 of the processing unit 13 of the information processing apparatus 10 acquires the measured value of the body temperature (tympanic membrane temperature) from the terminal device 20 in real time.

As described above, the prediction unit 133 predicts the core body temperature of the user 200 when the user 200 performs training based on the prediction formula. The prediction unit 133 generates a predicted value of the core body temperature at an arbitrary time after the user 200 starts the training before the user 200 starts the training or when the user 200 is performing the training.

The comparison unit 134 determines the measured value of the body temperature (deep body temperature) acquired in real time from the measuring device 30 via the terminal device 20 and the deep body temperature predicted by the predicting unit 133 when the user 200 is training. Compare with the predicted value. The comparison unit 134 compares, for example, the magnitude relationship between the slope of the measured value of body temperature (measured deep body temperature transition TR) and the slope of the predicted value (predicted deep body temperature transition TP).

The decision unit 132 redetermines the training information (training pace, time, etc.) based on the comparison result by the comparison unit 134.

For example, in the first training, when the slope of the measured value of the core body temperature is smaller than the slope of the predicted value of the core body temperature, the determination unit 132 may increase the training pace and / or extend the training time. If the slope of the core body temperature measurement is greater than the slope of the core body temperature prediction, the determination unit 132 may slow down the training pace and / or shorten the training time.

Further, in the second training, when the slope of the measured value of the core body temperature is smaller than the slope of the predicted value of the core body temperature, the determination unit 132 may increase the training pace and / or extend the training time. When the slope of the measured value of core body temperature is larger than the slope of the predicted value of core body temperature, the determination unit 132 may reduce the training pace.

The timing of re-determining the training information may be the time when the first training is carried out, the time when the first training is completed, or the time when the second training is carried out. Further, the timing of re-determining the training information may be not only once but also a plurality of times.

The output unit 137 outputs the re-determined training information when the user 200 is conducting the training. The measuring device 30 receives the redetermined training information via the terminal device 20. The notification unit 35 of the measuring device 30 notifies the user 200 of the redetermined training information when the user 200 is performing training. Examples of the notification method to the user 200 include voice and the like. The information notified by the notification unit 35 is not limited to the re-determined training information itself, but is a voice or the like prompting the training according to the re-determined training information such as "Let's increase the pace a little more". There may be.

With reference to FIG. 34, an example of the operation in which the support system 100 of this modified example notifies the user 200 will be described. The operation of the support system 100 of this modification is performed during the process ST110 of the basic example described with reference to FIG. 23 (when the user 200 is training).

For example, the information processing apparatus 10 predicts the predicted deep body temperature transition TP of the user 200 when the user 200 performs training based on the training information adopted by the user 200 in advance.

When the user 200 starts training, the measuring device 30 measures the body temperature of the user 200 in real time and transmits it to the information processing device 10. The information processing apparatus 10 acquires the body temperature of the user 200 in real time (ST401).

When the information processing apparatus 10 acquires the measured value of the body temperature (deep body temperature) from the measuring device 30, the information processing apparatus 10 creates an actually measured deep body temperature transition TR using the measured value acquired this time (and the measured value acquired before the previous time), and creates the body temperature. The magnitude relationship between the slope of the measured value (measured deep body temperature transition TR) and the slope of the predicted value (predicted deep body temperature transition TP) is compared (ST402).

When there is no difference in inclination (ST403: No), the information processing apparatus 10 determines that the body temperature (deep body temperature) of the user 200 is changing as planned and it is not necessary to correct the training content, and ends this process. do. User 200 continues training according to the current training information.

When there is a difference in inclination (ST403: Yes), the information processing device 10 determines that the training content needs to be corrected, redetermines the training information (ST404), and notifies the user 200 via the measuring device 30 (ST403: Yes). ST405). The user 200 continues training according to the re-determined training information.

The determination unit 132 may redetermine the training information based on the magnitude relationship between the measured value of the body temperature and the predicted value.

According to the support system 100 of this modification, the measured value and the predicted value of the core body temperature of the user 200 are compared, the training information is redetermined based on the comparison result, and the user is notified via the notification unit 35. .. As a result, for example, when the slope of the measured value of the core body temperature is smaller than the slope of the predicted value of the core body temperature (when the degree of increase in the core body temperature is smaller than the prediction), the user 200 so that the core body temperature exceeds the threshold temperature Tth. It is possible to change the training of the heat load training, and the success rate of heat load training is increased. In addition, when the slope of the measured value of the core body temperature is larger than the slope of the predicted value of the core body temperature (when the degree of increase in the core body temperature is larger than the prediction), the core body temperature is excessive (for example, 38.0 ° C. to 39. It is possible to suppress an increase (above the threshold temperature Th set in the range of 5 ° C.), and it is possible to prevent an excessive heat load from being applied to the user 200.

(3.4) Modification 4
The support system 100 of this modification is different from the support system 100 of the basic example in that it mainly has a normal mode and a test mode as operation modes. In the support system 100 of this modification, the same components as those of the support system 100 of the basic example may be designated by the same reference numerals and description thereof may be omitted as appropriate.

In the support system 100 of this modification, when determining the training information, if the predetermined conditions are satisfied, the user 200 is made to select whether to create the training information in the normal mode or the test mode. It has a function. For example, when receiving an instruction to start training from the user 200 via the terminal device 20 (ST101 in FIG. 23), if the predetermined conditions are satisfied, the information processing device 10 trains the user 200 in the test mode. May be answered via the terminal device 20 as to whether or not to carry out the above.

The predetermined conditions are, for example, that there is no comparison result by the comparison unit 134 (that is, this user 200 is the first user to perform training using the support system 100), and the comparison result is in the past (for example, the past 3 months). It may be information on the date (that is, it is estimated that the training level of the user 200 is low) or the like. The predetermined condition may be to receive a predetermined instruction from the user 200 via the terminal device 20.

When the user 200 answers that he / she wants training in the test mode, the determination unit 132 determines the training information in the test mode. If the user 200 does not want to train in the test mode, the determination unit 132 may determine the training information in the normal mode.

The test mode is a mode in which training information is determined so that the load on the user 200 is smaller than that in the normal mode. For example, when the test mode is selected, the determination unit 132 determines training information (training intensity and training time) based on a threshold temperature Tth lower than that of the normal mode. For example, the threshold temperature Tth in the normal mode is a value in the range of 38.0 ° C to 39.5 ° C as described in the basic example. On the other hand, the threshold temperature Tth in the test mode is a value in the range of 37.0 ° C to 38.5 ° C. That is, in the test mode, the determination unit 132 determines that the core body temperature of the user 200 is equal to or higher than this relatively low threshold temperature Tth (a value within the range of 37.0 ° C to 38.5 ° C) during the predetermined period P0. Determine training information to be maintained.

With reference to FIG. 35, an example of the operation in which the information processing apparatus 10 (decision unit 132) of this modified example determines the training information will be described. The operation of the information processing apparatus 10 of this modification is basically the same as the operation of the information processing apparatus 10 of the basic example described with reference to FIG. It differs in that it replaces it.

For example, when the information processing apparatus 10 receives an instruction to start training from the user 200 (ST101 in FIG. 23), the information processing apparatus 10 acquires attribute information and environmental information (ST102, ST103 in FIG. 23). Further, the information processing apparatus 10 determines whether or not the user 200 has previously trained using the support system 100 (whether or not the history information storage unit 1214 has a history of comparison results). (ST104).

When the history information storage unit 1214 has a history (ST104: Yes), the information processing apparatus 10 (decision unit 132) determines whether or not the latest date of training is in the past (3 months or more in the past). Is determined (ST501).

When the latest date of training is the latest (S501: No), the information processing apparatus 10 (decision unit 132) determines the training information at least based on the comparison result, the attribute information, and the environmental information. (ST502).

On the other hand, when there is no history (ST104: No) or when the latest date of training is in the past (3 months or more in the past) (ST501: Yes), the information processing apparatus 10 tests the user 200. Inquire whether to perform training in the mode (ST503).

When the user 200 replies that he / she wants to perform the training in the test mode (ST503: Yes), the information processing apparatus 10 (decision unit 132) determines the training information in the test mode (ST504). .. When the user 200 replies that he / she does not want to train in the test mode (ST503: No), the information processing apparatus 10 (decision unit 132) determines the training information in the normal mode (ST505).

If the heat load training has not been carried out for a long period of time, or if it is carried out for the first time, there is a possibility that an excessive heat load will occur at the threshold temperature Tth in the normal mode. In the support system 100 of this modification, it is possible to improve the safety of the user 200 by performing training from the test mode in which the threshold temperature Tth is lowered. In addition, if training is performed aiming at the threshold temperature Tth in the normal mode immediately after the start of training, the training pace may become too fast depending on the user 200, and the training cannot be completed and the measured value of the core body temperature. And the predicted value of core body temperature may not be sufficiently compared. By conducting training from a relatively slow pace and accumulating comparison results, it is possible to improve the accuracy of training information.

(3.5) Other Modifications In one modification, the measuring device 30 may be a device that measures a temperature other than the eardrum temperature of the user 200. For example, the measuring device 30 may be a device that measures the temperature of a part other than the eardrum, such as the armpit, the sublingual, the rectum, the esophagus, and the umbilical cord, which is considered to be the deep body temperature of the human body. The measuring device 30 may estimate the core body temperature from the measured value of the temperature based on a predetermined conversion formula, an algorithm, or the like. The measuring device 30 is preferably in a mode that does not easily interfere with the user 200 during training. The measuring device 30 may be, for example, a wristwatch type device. The measuring device 30 may be, for example, a patch-type device attached to a part of the human body (above the navel, under the armpit).

In one modification, the measuring device 30 may be a device that measures the body surface temperature of the user 200 as the body temperature of the user 200. The body temperature information acquisition unit 1319 may acquire the body surface temperature measured by the measuring device 30 as the measured value of the body temperature of the user 200. The determination unit 132 may determine training information regarding the training to be performed by the user 200 based on at least the measured value information of the body temperature (body surface temperature), the attribute information, and the environmental information.

In one modification, the biological information measured by the measuring device 30 may be biological information that does not include the eardrum temperature, such as heart rate and whole body sweating amount, or heart rate, temperature inside clothes, and humidity inside clothes. The eardrum temperature is one of the information that can accurately reflect the training load received by the user 200, but by combining multiple biological information, it is possible to grasp the training load without measuring the eardrum temperature. Therefore, the number of measuring devices worn by the user 200 can be reduced, and the burden of wearing the user 200 can be reduced.

In one modification, the determination unit 132 determines new training information regarding new training to be performed by the user 200 based on the measured value information of the biological information (other than body temperature) of the user 200, the attribute information, and the environmental information. You may.

In one modification, the measuring device 30 may substitute biometric information acquired by, for example, an information terminal (smartphone, smart watch, etc.) owned by the user 200, thereby simplifying the device and the user 200. Usability is improved. In addition to the biometric information measured by the measuring device 30, the information processing device 10 takes in the biometric information acquired by, for example, an information terminal (smartphone, smart watch, etc.) owned by the user 200, and performs calculations to complicate the device. It is possible to propose a more accurate training menu (training information) while avoiding the change. The information processing device 10 does not necessarily have to use the biological information measured by the measuring device 30, for example, the information processing device 10 is a living body acquired by an information terminal (smartphone, smart watch, etc.) owned by the user 200. Information may be captured.

In one modification, the information processing apparatus 10 has physical information such as stride (step length), pitch (step count), ground contact time, left-right balance, vertical movement, vertical movement ratio, etc. of the user 200 during training as information other than biological information. Information on the target index may be obtained. These physical indicators can also be measured by an information terminal such as a smartphone or a smart watch. By combining the biological information and the physical index, the exercise load applied to the user 200 can be estimated more accurately, and the accuracy of the training menu (training information) can be further improved.

In one modification, the body temperature information acquisition unit 1319 may directly acquire body temperature information from the measuring device 30. That is, the terminal device 20 does not necessarily have to acquire the body temperature information from the measuring device 30. In this case, it is preferable that the communication unit 32 of the measuring device 30 complies with the first communication protocol. Further, the body temperature information does not necessarily have to be acquired by the measuring device 30, and the user 200 may measure the body temperature by himself / herself and then input it to the terminal device 20 by the input unit 21 of the terminal device 20. .. In this case, the user 200 may measure the body temperature under the armpit or the lower part of the tongue.

In one modification, the storage unit 12 does not need to store all the above information. For example, environmental information, clothing information, etc. that have short-term fluctuations may be appropriately acquired from an external server or the like when the determination unit 132 actually performs processing.

In one modification, the information processing apparatus 10 is not limited to one that determines the second training so as to keep the core body temperature of the user 200 constant, for example, the second training so as to raise or decrease the core body temperature of the user 200. May be determined. In this case, the slope δ of the predicted deep body temperature transition TP at the time of performing the second training calculated by the comparison unit 134 can be a value other than 0. Even when the second training is determined to increase or decrease the core body temperature of the user 200, the information processing apparatus 10 preferably keeps the core body temperature of the user 200 above the threshold temperature Tth. 2 The intensity of training may be determined.

In one modification, the information processing apparatus 10 does not have to include the prediction unit 133 and the comparison unit 134. The determination unit 132 may determine new training information so that the core body temperature of the user 200 is maintained above the T0 threshold temperature Tth for a predetermined period based only on the measured core body temperature transition TR. However, it is easier to determine more appropriate training information by determining new training information based on the comparison result between the predicted deep body temperature transition TP obtained based on the initial temperature T0 and the measured deep body temperature transition TR. preferable. The measured deep body temperature transition TR can also be estimated from the measured values of biological information other than the body temperature.

In one modification, the determination unit 132 may determine training information using the trained model. The trained model here outputs training information by inputting at least attribute information, environment information (and comparison result), for example. In this case, the storage unit 12 of the information processing apparatus 10 may include a trained model storage unit that stores the trained model in place of or in addition to the predictive storage unit 125.

In one modification, the determination unit 132 may determine the training information by using the data table.

In one modification, the determination unit 132 may allow the user 200 to determine the type (item) of each type in addition to the type of training. For example, when running is selected as the training type, the determination unit 132 presents the user 200 with pace running, interval running, build-up running, and the like as a plurality of selection candidates regarding the running type. The determination unit 132 determines the selection candidate selected by the user 200 from the plurality of presented selection candidates as the type of training (running) to be performed.

In one modification, the user 200 may be able to specify the start time and end time of the training. In this case, the determination unit 132 may determine the intensity of training and the like so that the heat load training can be achieved in the time between the start time and the end time of the training.

In one modification, the evaluation result information output by the output unit 137 may be changed according to the feedback from the user 200. For example, the information processing device 10 receives feedback from the user 200 with respect to the evaluation result information (for example, advice information) presented (displayed) by the terminal device 20. For example, when the user 200 feels that the evaluation result has the same content every time the training is performed and the reliability of the evaluation result is low, the user 200 gives feedback that the content of the evaluation result is not good. In this case, the output unit 137 may output different evaluation results in the evaluation results from the next time onward, for example.

(4) Aspects As is clear from the above embodiments and modifications, the present disclosure includes the following aspects.

The information processing device (10) of the first aspect includes an attribute information acquisition unit (1311), an environment information acquisition unit (1315), a determination unit (132), and an output unit (137). The attribute information acquisition unit (1311) acquires attribute information related to the attributes of the user (200). The environmental information acquisition unit (1315) acquires environmental information regarding the training environment of the user (200). The decision unit (132) determines the training information regarding the training to be performed by the user (200) based on at least the attribute information and the environmental information. The output unit (137) outputs the training information determined by the determination unit (132).

According to this aspect, it is possible to provide training information suitable for the user (200).

In the information processing apparatus (10) of the second aspect, in the first aspect, the training information is at least one of the training type, the training intensity, the training time, and the clothing of the user (200) in the training. including.

In the information processing apparatus (10) of the third aspect, in the second aspect, the training information includes the combination of the intensity of training and the clothing of the user (200) in the training.

The information processing apparatus (10) of the fourth aspect further includes a possessed clothing information storage unit (1212) that stores information on clothing that can be used by the user (200) in the second or third aspect. The determination unit (132) selects the clothes of the user (200) from the clothes stored in the possessed clothing information storage unit (1212).

According to this aspect, the possibility that clothes that cannot be used by the user (200) are proposed is reduced, and the convenience of the user (200) is improved.

The information processing apparatus (10) of the fifth aspect further includes a biological information acquisition unit (1314) in any one of the first to fourth aspects. The biometric information acquisition unit (1314) acquires the measured value of the biometric information of the user (200) measured at the time when the user (200) carries out the training according to the training information. The determination unit (132) determines new training information regarding the new training to be performed by the user (200) based on at least the measured value information of the biological information, the attribute information, and the environmental information. The output unit (137) outputs new training information determined by the determination unit (132).

According to this aspect, since new training information is determined based on the measured value of the biometric information of the user (200) measured at the time of training, it is possible to provide training information more suitable for the user (200). It will be possible.

In the information processing apparatus (10) of the sixth aspect, in the fifth aspect, the biological information acquisition unit (1314) includes the body temperature information acquisition unit (1319). The body temperature information acquisition unit (1319) acquires the measured value of the body temperature of the user (200) measured at the time when the user (200) carries out the training according to the training information. The determination unit (132) determines new training information regarding the new training to be performed by the user (200) based on at least the information of the measured value of the body temperature, the attribute information, and the environmental information.

According to this aspect, since new training information is determined based on the measured value of the body temperature of the user (200) measured at the time of training, it is possible to provide training information more suitable for the user (200). It becomes.

In the information processing apparatus (10) of the seventh aspect, in the sixth aspect, the body temperature information acquisition unit (1319) uses the measured value of the core body temperature of the user (200) as the measured value of the body temperature of the user (200). get.

According to this aspect, it is possible to generate (determine) training information that facilitates the user (200) to achieve heat load training.

In the information processing apparatus (10) of the eighth aspect, in the seventh aspect, the body temperature information acquisition unit (1319) measures the tympanic membrane temperature of the user (200) as the measured value of the core body temperature of the user (200). The temperature measured by the measuring device (30) is acquired.

According to this aspect, it becomes possible to acquire the measured value of the core body temperature of the user (200) non-invasively, and the burden on the user (200) at the time of training is reduced. In addition, it becomes possible to provide training information more suitable for the user (200).

The information processing apparatus (10) of the ninth aspect further includes a prediction unit (133) and a comparison unit (134) in any one of the sixth to eighth aspects. The prediction unit (133) predicts the body temperature of the user (200) when the user (200) performs training according to the training information determined by the determination unit (132). The comparison unit (134) compares the predicted value of the body temperature of the user (200) predicted by the prediction unit (133) with the measured value of the body temperature of the user (200) acquired by the body temperature information acquisition unit (1319). .. The determination unit (132) determines new training information regarding the new training to be performed by the user (200) based on at least the comparison result in the comparison unit (134), the attribute information, and the environmental information. The output unit (137) outputs new training information determined by the determination unit (132).

According to this aspect, new training information is determined based on the comparison result, so that it is possible to provide training information more suitable for the user (200).

The information processing apparatus (10) of the tenth aspect has the prediction unit (133), the body temperature information acquisition unit (1314), and the comparison unit (134) in any one of the first to fourth aspects. Further prepare. The prediction unit (133) predicts the body temperature of the user (200) when the user (200) performs training according to the training information determined by the determination unit (132). The body temperature information acquisition unit (1314) acquires the measured value of the body temperature of the user (200) measured at the time when the user (200) carries out the training according to the training information. The comparison unit (134) compares the predicted value of the body temperature of the user (200) predicted by the prediction unit (133) with the measured value of the body temperature of the user (200) acquired by the body temperature information acquisition unit (1314). .. The determination unit (132) determines new training information regarding the new training to be performed by the user (200) based on at least the comparison result in the comparison unit (134), the attribute information, and the environmental information. The output unit (137) outputs new training information determined by the determination unit (132).

In the information processing apparatus (10) of the eleventh aspect, in the tenth aspect, the body temperature information acquisition unit (1314) measures the tympanic membrane temperature of the user (200) as the measured value of the body temperature of the user (200). The temperature measured by the device (30) is acquired.

In the information processing device (10) of the twelfth aspect, in any one of the ninth to eleventh aspects, the body temperature information acquisition unit (1319) further, before performing the training according to the training information, the measuring device (30). ), Which is the body temperature of the user (200), which is the initial temperature (T0). The prediction unit (133) predicts the body temperature of the user (200) when the training is performed, at least based on the initial temperature (T0) and the training information.

According to this aspect, the accuracy of the prediction result of the body temperature of the user (200) by the prediction unit (133) is improved, and it becomes possible to provide training information more suitable for the user (200).

The information processing apparatus (10) of the thirteenth aspect further includes an evaluation unit (135) in any one of the ninth to twelfth aspects. The evaluation unit (135) evaluates the training result of the user (200) who has performed the training according to the training information based on the comparison result.

According to this aspect, it is possible to confirm the evaluation result of the training.

The information processing apparatus (10) of the fourteenth aspect further includes a target information acquisition unit (1313) and a schedule unit (136) in the thirteenth aspect. The target information acquisition unit (1313) acquires target information regarding the target of the user (200). The schedule unit determines the training schedule of the user (200) based on the target information. The schedule unit (136) updates the training schedule based on the evaluation result by the evaluation unit (135).

According to this aspect, a training schedule based on the training evaluation result can be created, and training information more suitable for the user (200) can be provided.

In the information processing apparatus (10) of the fifteenth aspect, in any one of the sixth to the fourteenth aspects, the body temperature information acquisition unit (1319) is a user when the user (200) performs training according to the training information. The temperature obtained by continuously measuring the body temperature of (200) is acquired.

According to this aspect, it is possible to provide training information more suitable for the user (200).

The information processing apparatus (10) of the sixteenth aspect further includes a pre-training information acquisition unit (1317) in any one of the first to fifteenth aspects. The pre-training information acquisition unit (1317) acquires pre-training information which is information of the user (200) related to the training before the user (200) performs training. The decision unit (132) further determines the training information based on the pre-training information.

According to this aspect, since the determination unit (132) further determines the training information based on the pre-training information, it is possible to provide the training information more suitable for the user (200).

In the information processing apparatus (10) of the 17th aspect, in the 16th aspect, the pre-training information is the health state information regarding the health state of the user (200) before the training, and the user (200) performs it before the training. Includes at least one selected from the group consisting of warm-up information about the warm-up to be performed and water intake information about the water that the user (200) plans to consume during training.

According to this aspect, since the pre-training information referred to by the determination unit (132) when determining the training information includes at least one of the health condition information, the warm-up information, and the water intake information, the user is more likely to use the information. It becomes possible to provide training information suitable for (200).

The information processing apparatus (10) of the eighteenth aspect further includes a training result information acquisition unit (1318) in any one of the first to the seventeenth aspects. The training performance information acquisition unit (1318) acquires training performance information regarding the training performance performed by the user (200). The determination unit (132) determines new training information regarding the new training to be performed by the user (200), at least based on the training performance information, the attribute information, and the environmental information. The output unit (137) outputs new training information determined by the determination unit (132).

According to this aspect, new training information is determined based on the training performance information, so that it is possible to provide training information more suitable for the user (200).

In the information processing apparatus (10) of the nineteenth aspect, in any one of the first to eighteenth aspects, the determination unit (132) determines the training information by using the prediction formula, the data table, or the trained model. do.

In the information processing apparatus (10) of the twentieth aspect, in any one of the first to the nineteenth aspects, the determination unit (132) determines that the core body temperature of the user (200) is set for a predetermined period when the training is performed. (P0), the training information is determined so as to be maintained above the threshold temperature (Tth).

According to this aspect, improvement in endurance athletic ability and / or performance of the user (200) can be expected.

In the information processing apparatus (10) of the 21st aspect, in the 20th aspect, the training information includes the training intensity and the training time. The training intensity is the first training intensity that causes the core body temperature of the user (200) to reach the threshold temperature (Tth) at the lapse of the first period (P1) from the start of training, and the first period (P1). A second period (P2) as a predetermined period (P0) after the lapse of time, and a second training intensity for maintaining the core body temperature of the user (200) at a temperature equal to or higher than the threshold temperature (Tth). The second training intensity is equal to or less than the first training intensity.

According to this aspect, improvement in endurance athletic ability and / or performance of the user (200) can be further expected.

The information processing apparatus (10) of the 22nd aspect further includes a possibility / rejection information acquisition unit (1316) in any one of the first to the 21st aspects. The approval / disapproval information acquisition unit (1316) acquires the approval / disapproval information indicating whether or not the training information output from the output unit (137) can be adopted. The decision unit (132) determines new training information different from the training information when the adoption of the training information is denied by the approval / disapproval information.

According to this aspect, the user (200) can select the training according to his / her preference.

The support system (100) of the 23rd aspect is the information processing device (10) of any one of the first to the 22nd aspects, and the terminal device (20) that presents the information output from the output unit (137). And.

In the support system (100) of the 24th aspect, in the 23rd aspect, the terminal device (20) inputs the input unit (21) for receiving the input of the attribute information and the attribute information input to the input unit (21). A communication unit (first communication unit 231) for transmitting to the information processing device (10) is further provided.

According to this aspect, the user (200) can input the attribute information using the terminal device (20), and the convenience of the user (200) is improved.

The terminal device (20) of the 25th aspect is used as the terminal device (20) in the support system (100) of the 23rd or 24th aspect.

The support system (100) according to the 26th aspect includes an information processing device (10) according to any one of the first to the 22nd aspects and a measuring device (30) for measuring biometric information of the user (200). Be prepared. The determination unit (132) determines the training information at least based on the biometric information, the attribute information, and the environmental information of the user (200) measured by the measuring device (30).

In the support system (100) of the 27th aspect, in the 26th aspect, the measuring device (30) notifies the user (200) of information about the training when the user (200) performs the training. 35) is provided.

According to this aspect, it is possible to notify the user (200) of the training information during the training, and it is possible to increase the success rate of the heat load training or prevent the user (200) from being subjected to an excessive heat load. It becomes possible to do.

In the information processing method of the 28th aspect, the attribute information regarding the attribute of the user (200) is acquired, the environmental information regarding the training environment of the user (200) is acquired, and the user (at least based on the attribute information and the environmental information). 200) determines the training information regarding the training to be performed, and outputs the determined training information.

The program of the 29th aspect is a program for causing one or more processors to execute the information processing method of the 28th aspect.

10 Information processing device 1212 Possessed clothes information storage unit 1311 Attribute information acquisition unit 1313 Target information acquisition unit 1314 Biological information acquisition unit 1315 Environmental information acquisition unit 1316 Possibility information acquisition unit 1317 Pre-training information acquisition unit 1318 Training performance information acquisition unit 1319 Body temperature information Acquisition unit 132 Decision unit 133 Prediction unit 134 Comparison unit 135 Evaluation unit 136 Schedule unit 137 Output unit 20 Terminal device 21 Input unit 231 First communication unit (communication unit)
30 Measuring device 35 Notification unit 100 Support system 200 User T0 Initial temperature Tth Threshold temperature P0 Predetermined period P1 First period P2 Second period

Claims

Attribute information acquisition unit that acquires attribute information related to user attributes,
The environmental information acquisition unit that acquires environmental information related to the user's training environment,
A decision unit that determines training information regarding training to be performed by the user based on at least the attribute information and the environment information.
An output unit that outputs the training information determined by the determination unit, and
To prepare
Information processing equipment.
The training information includes at least one of the training type, the training intensity, the training time, and the user's clothing in the training.
The information processing apparatus according to claim 1.
The training information includes a combination of the intensity of the training and the clothing of the user in the training.
The information processing apparatus according to claim 2.
Further provided with a possessed clothing information storage unit for storing information on clothing that can be used by the user.
The determination unit selects the clothing of the user from the clothing stored in the possessed clothing information storage unit.
The information processing apparatus according to claim 2 or 3.
The information processing apparatus further includes a biometric information acquisition unit that acquires a measured value of the user's biometric information measured at the time when the user performs the training according to the training information.
The determination unit determines new training information regarding new training to be performed by the user based on at least the measured value information of the biological information, the attribute information, and the environmental information.
The output unit outputs the new training information determined by the determination unit.
The information processing apparatus according to any one of claims 1 to 4.
The biological information acquisition unit includes a body temperature information acquisition unit that acquires a measured value of the user's body temperature measured at the time when the user performs the training according to the training information.
The determination unit determines new training information regarding new training to be performed by the user, based on at least the information of the measured value of the body temperature, the attribute information, and the environmental information.
The information processing apparatus according to claim 5.
The body temperature information acquisition unit acquires the measured value of the core body temperature of the user as the measured value of the body temperature of the user.
The information processing apparatus according to claim 6.
The body temperature information acquisition unit acquires the temperature measured by the measuring device for measuring the eardrum temperature of the user as the measured value of the core body temperature of the user.
The information processing apparatus according to claim 7.
The information processing device is
A prediction unit that predicts the body temperature of the user when the user performs the training according to the training information determined by the determination unit.
A comparison unit that compares the predicted value of the user's body temperature predicted by the prediction unit with the measured value of the user's body temperature acquired by the body temperature information acquisition unit.
Further prepare
The determination unit determines new training information regarding new training to be performed by the user based on at least the comparison result in the comparison unit, the attribute information, and the environment information.
The output unit outputs the new training information determined by the determination unit.
The information processing apparatus according to any one of claims 6 to 8.
The information processing device is
A prediction unit that predicts the body temperature of the user when the user performs the training according to the training information determined by the determination unit.
A body temperature information acquisition unit that acquires a measured value of the user's body temperature measured at the time when the user performs the training according to the training information.
A comparison unit that compares the predicted value of the user's body temperature predicted by the prediction unit with the measured value of the user's body temperature acquired by the body temperature information acquisition unit.
Further prepare
The determination unit determines new training information regarding new training to be performed by the user based on at least the comparison result in the comparison unit, the attribute information, and the environment information.
The output unit outputs the new training information determined by the determination unit.
The information processing apparatus according to any one of claims 1 to 4.
The body temperature information acquisition unit acquires the temperature measured by the measuring device for measuring the eardrum temperature of the user as the measured value of the body temperature of the user.
The information processing apparatus according to claim 10.
The body temperature information acquisition unit further acquires an initial temperature which is the body temperature of the user measured before performing the training according to the training information.
The prediction unit predicts the body temperature of the user when the training is performed, based on at least the initial temperature and the training information.
The information processing apparatus according to any one of claims 9 to 11.
Further, an evaluation unit for evaluating the training result of the user who has performed the training according to the training information based on the comparison result is provided.
The information processing apparatus according to any one of claims 9 to 12.
The target information acquisition unit that acquires target information related to the user's target,
A schedule unit that determines the training schedule of the user based on the target information,
Further prepare
The schedule unit updates the training schedule based on the evaluation result by the evaluation unit.
The information processing apparatus according to claim 13.
The body temperature information acquisition unit acquires a temperature at which the user's body temperature is continuously measured when the user performs the training according to the training information.
The information processing apparatus according to any one of claims 6 to 14.
Further provided with a pre-training information acquisition unit for acquiring pre-training information which is information of the user regarding the training before the user performs the training.
The determination unit determines the training information based on the pre-training information.
The information processing apparatus according to any one of claims 1 to 15.
The pre-training information includes health information regarding the health condition of the user before the training, warm-up information regarding the warm-up performed by the user before the training, and the user is scheduled to ingest during the training. Containing at least one selected from the group consisting of ingested water information on water,
The information processing apparatus according to claim 16.
Further equipped with a training performance information acquisition unit for acquiring training performance information regarding the training performance performed by the user.
The determination unit determines new training information regarding new training to be performed by the user, based on at least the training performance information, the attribute information, and the environmental information.
The output unit outputs the new training information determined by the determination unit.
The information processing apparatus according to any one of claims 1 to 17.
The decision unit determines the training information using a predictive formula, a data table or a trained model.
The information processing apparatus according to any one of claims 1 to 18.
The determination unit determines the training information so that the core body temperature of the user is maintained above the threshold temperature for a predetermined period when the training is performed.
The information processing apparatus according to any one of claims 1 to 19.
The training information includes the intensity of the training and the time of the training.
The intensity of the training is
The first training intensity that causes the user's core body temperature to reach the threshold temperature at the time when the first period elapses from the start time of the training, and
After the lapse of the first period, in the second period as the predetermined period, the second training intensity for maintaining the core body temperature of the user at a temperature equal to or higher than the threshold temperature, and
Including
The second training intensity is equal to or less than the first training intensity.
The information processing apparatus according to claim 20.
Further, it is provided with a propriety information acquisition unit for acquiring propriety information indicating whether or not to adopt the training information output from the output unit.
When the adoption of the training information is denied by the approval / disapproval information, the determination unit determines new training information different from the training information.
The information processing apparatus according to any one of claims 1 to 21.
The information processing apparatus according to any one of claims 1 to 22 and
A terminal device that presents information output from the output unit, and
To prepare
Support system.
The terminal device includes an input unit that accepts input of the attribute information and
A communication unit that transmits the attribute information input to the input unit to the information processing device, and a communication unit.
Further prepare,
The support system according to claim 23.
The support system according to claim 23 or 24 is used as the terminal device.
Terminal device.
The information processing apparatus according to any one of claims 1 to 22 and
A measuring device that measures the biometric information of the user,
Equipped with
The determination unit determines the training information based on at least the biometric information of the user, the attribute information, and the environmental information measured by the measuring device.
Support system.
The measuring device includes a notification unit that notifies the user of information about the training when the user performs the training.
The support system according to claim 26.
Get attribute information about user's attributes
Obtain environmental information about the user's training environment and
Based on at least the attribute information and the environment information, the training information regarding the training to be performed by the user is determined.
Output the determined training information,
Information processing method.
To cause one or more processors to execute the information processing method according to claim 28.
program.