Fatigue Degree Calculation Device, Fatigue Degree Calculation Method, and Program

Abstract

A fatigue degree calculation device (1X) has primarily a fatigue degree calculation pattern acquisition means (14X) and a fatigue degree calculation means (17X). The fatigue degree calculation pattern acquisition means (14X) acquires a fatigue degree calculation pattern which is a calculation pattern for the degree of fatigue of a measurement subject, determined on the basis of a state of the measurement subject and measurement subject information relating to an environment. The fatigue degree calculation means (17X) calculates the degree of fatigue of the measurement subject on the basis of the fatigue degree calculation pattern.

Description

Technical Field

[0001] The present disclosure relates to the technical field of a fatigue degree calculation device, a fatigue degree calculation method, and a storage medium for estimating fatigue.

Background Art

[0002] An apparatus or system for estimating the fatigue degree of a subject is known. For example, Patent Document 1 discloses a fatigue determination method in which the higher of the RPE value of mental fatigue and the RPE of physical fatigue is used as the RPE value of the overall fatigue degree of the subject, and the category is determined as the category of the overall fatigue degree.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The fatigue degree varies among individuals, and such individual differences cannot be considered only by biological data generally used in calculating the fatigue degree. Therefore, in order to accurately calculate the fatigue degree, it is necessary to calculate the fatigue degree considering such individual differences.

[0005] In view of the above problems, a main object of the present disclosure is to provide a fatigue degree calculation device, a fatigue degree calculation method, and a storage medium capable of suitably calculating the fatigue degree.

Means for Solving the Problems

[0006] One aspect of the fatigue degree calculation device is Is a sports player Based on the information of the subject An acquisition means for acquiring a measurement value and subject information indicating at least any one of season information regarding the schedule on which the competition performed by the subject is held, state information regarding an injury of the subject, or practice history information of the subject The A fatigue degree calculation pattern, which is a calculation pattern of the fatigue degree of the subject, based on the subject informationJudgment Fatigue degree calculation pattern Judgment means, and The measurement value and the fatigue degree calculation pattern And Based on the above, a fatigue degree calculation means for calculating the fatigue degree of the subject, and A fatigue degree calculation device having the above.

[0007] One aspect of the fatigue degree calculation method is that a computer Is a sports player of the subject Acquire a measurement value and subject information indicating at least any one of season information regarding the schedule on which the competition performed by the subject is held, state information regarding an injury of the subject, or practice history information of the subject The Based on the subject information, a fatigue degree calculation pattern that is the fatigue degree calculation pattern of the subject is Judgment performed, and The measurement value and the fatigue degree calculation pattern And Based on the above, the fatigue degree of the subject is calculated. This is a fatigue degree calculation method. Note that "computer" includes any electronic device (which may be a processor included in the electronic device) and may be composed of a plurality of electronic devices.

[0008] One aspect of the program is that Is a sports player of the subject Acquire a measurement value and subject information indicating at least any one of season information regarding the schedule on which the competition performed by the subject is held, state information regarding an injury of the subject, or practice history information of the subject The Based on the subject information, a fatigue degree calculation pattern that is the fatigue degree calculation pattern of the subject is Judgment performed, and The measurement value and the fatigue degree calculation pattern And Based on the above, a program for causing a computer to execute a process of calculating the fatigue degree of the subject.

Advantages of the Invention

[0009] According to the present disclosure, the fatigue degree of the subject can be accurately calculated.

Brief Description of the Drawings

[0010]

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Embodiments for Carrying Out the Invention

[0011] Hereinafter, embodiments of the fatigue degree calculation device, the fatigue degree calculation method, and the storage medium will be described with reference to the drawings.

[0012] <First Embodiment> (1) System configuration FIG. 1 shows the schematic configuration of a fatigue degree calculation system 100 according to the first embodiment. The fatigue degree calculation system 100 mainly includes a fatigue degree calculation device 1, an input device 2, an output device 3, a storage device 4, and a sensor 5.

[0013] The fatigue degree calculation device 1 performs processes related to the calculation of the fatigue degree of the person to be measured. In this embodiment, as an example, the fatigue degree is classified into a mental fatigue degree and a physical fatigue degree. Then, after calculating the mental fatigue degree and the physical fatigue degree respectively, the fatigue degree calculation device 1 calculates the comprehensive fatigue degree, which is the fatigue degree obtained by integrating these based on the mental fatigue degree and the physical fatigue degree. The fatigue degree calculation device 1 performs data communication with the input device 2, the output device 3, and the sensor 5 via a communication network or by direct communication, whether wireless or wired. And the fatigue degree calculation device 1 performs the fatigue degree calculation process of the person to be measured based on the input signal "S1" supplied from the input device 2, the sensor signal "S3" supplied from the sensor 5, and the information stored in the storage device 4. Also, the fatigue degree calculation device 1 generates an output signal "S2" based on the fatigue calculation result and supplies the generated output signal S2 to the output device 3.

[0014] The input device 2 is an interface that accepts manual input (external input) of information regarding each person to be measured. Note that the user who inputs information using the input device 2 may be the person to be measured himself / herself or a person who manages or supervises the activities of the person to be measured. The input device 2 may be various user input interfaces such as, for example, a touch panel, buttons, a keyboard, a mouse, a voice input device, etc. The input device 2 supplies the generated input signal S1 to the fatigue degree calculation device 1. The output device 3 displays or outputs sound a predetermined information based on the output signal S2 supplied from the fatigue degree calculation device 1. The output device 3 is, for example, a display, a projector, a speaker, etc.

[0015] The sensor 5 measures the biological signals of the person to be measured and supplies the measured biological signals to the fatigue degree calculation device 1 as a sensor signal S3. In this case, the sensor signal S3 may be any biological data (including vital information) such as the heartbeat, brain wave, sweating amount, hormone secretion amount, cerebral blood flow, blood pressure, body temperature, electromyogram, electrocardiogram, and respiratory rate of the person to be measured. Further, the sensor 5 may be a device that analyzes the blood collected from the person to be measured and outputs a sensor signal S3 indicating the analysis result. Further, the sensor 5 may be a device that performs physical measurements such as jumps for measuring physical fatigue degree and the like. Thus, the sensor 5 may be any device or apparatus for obtaining objective measurement values. Hereinafter, the objective measurement value shall be any measurement value obtained by a measuring device without depending on human judgment or evaluation (including the above-described biological data and the like).

[0016] The storage device 4 is a memory that stores various information necessary for calculating various fatigue degrees. The storage device 4 may be an external storage device such as a hard disk connected to or built in the fatigue degree calculation device 1, or may be a storage medium such as a flash memory. Further, the storage device 4 may be a server device that performs data communication with the fatigue degree calculation device 1. Further, the storage device 4 may be composed of a plurality of devices.

[0017] Functionally, the storage device 4 has a person to be measured information storage unit 41 and a fatigue degree calculation model storage unit 42.

[0018] The measured person information storage unit 41 stores the information of the measured person. The measured person information is information regarding the state or environment of the measured person, and is information that affects the physical or mental aspect of the measured person. The measured person information is used to determine the calculation pattern of the fatigue level of the measured person. The first example of the measured person information is information regarding the season in which the competition that the measured person participates in as a sports player is mainly held and / or the schedule of important games (also referred to as "season information") when the measured person is a sports player. The second example of the measured person information is information regarding the health state of the measured person (also referred to as "measured person health information"). The measured person health information may be information regarding the medical history, or may be information regarding the current condition of the measured person (for example, the state of injury classified into "state immediately after injury", "during treatment", "after recovery or not injured", etc.) or information regarding the amount of practice when the measured person is a sports player. The third example of the measured person information is historical information regarding the practice menu or the load amount of practice that the measured person has executed when the measured person is a sports player (also referred to as "practice history information"). The load amount may be, for example, a value obtained by converting the amount of exercise of the measured person into a predetermined index such as calories, or a value represented by a physical quantity such as the moving distance or acceleration.

[0019] Note that the measured person information is not limited to the above examples, and may be any information regarding the state or environment of the measured person that affects the physical or mental aspect of the measured person. Also, the measured person information stored in the measured person information storage unit 41 may be periodically updated according to the situation of the measured person. The update of the measured person information stored in the measured person information storage unit 41 may be performed by the fatigue level calculation device 1, or may be performed by a device other than the fatigue level calculation device 1.

[0020] The fatigue degree calculation model storage unit 42 stores information regarding a fatigue degree calculation model, which is a model for calculating the fatigue degree of the person to be measured. As will be described later, the fatigue degree calculation model is pre-learned for each type of fatigue degree to be calculated and for each fatigue degree calculation pattern determined by the fatigue degree calculation device 1. Then, the parameters obtained through learning are stored in the fatigue degree calculation model storage unit 42. For example, when each fatigue degree calculation model is a linear model, the fatigue degree calculation model storage unit 42 stores information on the parameters (weights) of each linear model. Note that the fatigue degree calculation model is not limited to a linear model, and may be a regression model (statistical model) other than a linear model or a machine learning model. In these cases, the fatigue degree calculation model storage unit 42 stores information on the parameters necessary for constructing each fatigue degree calculation model. For example, when each fatigue degree calculation model is a model based on a neural network such as a convolutional neural network, the fatigue degree calculation model storage unit 42 stores information on various parameters such as the layer structure, the neuron structure of each layer, the number and size of filters in each layer, and the weights of each element of each filter.

[0021] Note that the configuration of the fatigue degree calculation system 100 shown in FIG. 1 is an example, and various changes may be made to the configuration. For example, the input device 2 and the output device 3 may be integrally configured. In this case, the input device 2 and the output device 3 may be configured as a tablet-type terminal that is integrated with or separate from the fatigue degree calculation device 1. Also, the input device 2 and the sensor 5 may be integrally configured. Further, the fatigue degree calculation device 1 may be composed of a plurality of devices. In this case, the plurality of devices constituting the fatigue degree calculation device 1 exchange information necessary for executing the pre-assigned processing among these plurality of devices.

[0022] (2) Hardware configuration of the fatigue calculation device FIG. 2 shows the hardware configuration of the fatigue degree calculation device 1. As hardware, the fatigue degree calculation device 1 includes a processor 11, a memory 12, and an interface 13. The processor 11, the memory 12, and the interface 13 are connected via a data bus 19.

[0023] By executing the program stored in the memory 12, the processor 11 functions as a controller (arithmetic unit) that controls the entire fatigue degree calculation device 1. The processor 11 is, for example, a processor such as a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), or a TPU (Tensor Processing Unit). The processor 11 may be composed of a plurality of processors. The processor 11 is an example of a computer.

[0024] The memory 12 is composed of various volatile memories and non-volatile memories such as RAM (Random Access Memory), ROM (Read Only Memory), and flash memory. Further, a program for executing the processing executed by the fatigue degree calculation device 1 is stored in the memory 12. Note that part of the information stored in the memory 12 may be stored by one or more external storage devices that can communicate with the fatigue degree calculation device 1, or may be stored by a storage medium detachable from the fatigue degree calculation device 1.

[0025] The interface 13 is an interface for electrically connecting the fatigue degree calculation device 1 and other devices. These interfaces may be wireless interfaces such as network adapters for wirelessly transmitting and receiving data with other devices, or may be hardware interfaces for connecting to other devices by a cable or the like.

[0026] Note that the hardware configuration of the fatigue degree calculation device 1 is not limited to the configuration shown in FIG. 2. For example, the fatigue degree calculation device 1 may include at least one of the input device 2 or the output device 3. Further, the fatigue degree calculation device 1 may be connected to or incorporated with a sound output device such as a speaker.

[0027] (3) Functional block FIG. 3 is an example of the functional blocks of the fatigue level calculation device 1. Functionally, the processor 11 of the fatigue level calculation device 1 includes a fatigue level calculation pattern determination unit 14, a mental fatigue level calculation unit 15, a physical fatigue level calculation unit 16, a comprehensive fatigue level calculation unit 17, and an output control unit 18. In FIG. 3, blocks that exchange data are connected by solid lines, but the combinations of blocks that exchange data are not limited to those shown in FIG. 3. The same applies to the diagrams of other functional blocks described later.

[0028] Based on the information of the person to be measured stored in the person-to-be-measured information storage unit 41, the fatigue level calculation pattern determination unit 14 determines a fatigue level calculation pattern "β", which is identification information associated with the fatigue level calculation model to be used. Details of the method for determining the fatigue level calculation pattern β by the fatigue level calculation pattern determination unit 14 will be described later. The fatigue level calculation pattern determination unit 14 supplies the determined fatigue level calculation pattern β to the mental fatigue level calculation unit 15, the physical fatigue level calculation unit 16, and the comprehensive fatigue level calculation unit 17, respectively.

[0029] Based on the fatigue level calculation pattern β determined by the fatigue level calculation pattern determination unit 14 and the objective measurement values (e.g., biological data such as heart rate and brain waves) of the person to be measured represented by the sensor signal S3, the mental fatigue level calculation unit 15 calculates the mental fatigue level of the person to be measured. In this case, based on the fatigue level calculation pattern β determined by the fatigue level calculation pattern determination unit 14, the mental fatigue level calculation unit 15 selects a mental fatigue level calculation model to be used from the mental fatigue level calculation models registered in the fatigue level calculation model storage unit 42 (also referred to as "mental fatigue level calculation models"). Then, the mental fatigue level calculation unit 15 inputs the objective measurement values of the person to be measured or their feature quantities into the selected mental fatigue level calculation model to obtain the mental fatigue level of the person to be measured as a score. In this case, the mental fatigue level calculation model is pre-trained to output the mental fatigue level of the person to be measured when the objective measurement values or their feature quantities are input, and the learned parameters are stored in the fatigue level calculation model storage unit 42. The mental fatigue level calculation unit 15 supplies the calculated mental fatigue level to the comprehensive fatigue level calculation unit 17.

[0030] Note that the mental fatigue degree calculation unit 15 may calculate the mental fatigue degree of the subject based on the subjective measurement value (such as the result of a questionnaire for measuring mental fatigue degree) indicated by the input signal S1 instead of the objective measurement value of the subject indicated by the sensor signal S3. In this case, the mental fatigue degree calculation model used by the mental fatigue degree calculation unit 15 is, for example, pre-learned to output the mental fatigue degree of the subject when the subjective measurement value indicated by the input signal S1 is input, and the learned parameters thereof are stored in the fatigue degree calculation model storage unit 42. In yet another example, the mental fatigue degree calculation unit 15 may calculate the mental fatigue degree of the subject based on the subject information (such as information regarding the amount of practice) stored in the subject information storage unit 41 instead of or in addition to the objective measurement value of the subject indicated by the sensor signal S3. In this case, the mental fatigue degree calculation model used by the mental fatigue degree calculation unit 15 is pre-learned to output the mental fatigue degree of the subject when the subject information is input, and the learned parameters thereof are stored in the fatigue degree calculation model storage unit 42.

[0031] The physical fatigue degree calculation unit 16 calculates the physical fatigue degree of the subject based on the fatigue degree calculation pattern β determined by the fatigue degree calculation pattern determination unit 14 and the objective measurement value (such as the measurement results of heart rate and jump) of the subject indicated by the sensor signal S3. In this case, the physical fatigue degree calculation unit 16 selects the physical fatigue degree calculation model to be used from the physical fatigue degree calculation models (also referred to as "physical fatigue degree calculation models") registered in the fatigue degree calculation model storage unit 42 based on the fatigue degree calculation pattern β determined by the fatigue degree calculation pattern determination unit 14. Then, the physical fatigue degree calculation unit 16 inputs the objective measurement value or its feature amount of the subject to the selected physical fatigue degree calculation model to obtain the physical fatigue degree of the subject as a score. In this case, the physical fatigue degree calculation model is pre-learned to output the physical fatigue degree of the subject when the objective measurement value or its feature amount is input, and the learned parameters thereof are stored in the fatigue degree calculation model storage unit 42. The physical fatigue degree calculation unit 16 supplies the calculated physical fatigue degree to the comprehensive fatigue degree calculation unit 17.

[0032] Note that the physical fatigue degree calculation unit 16 may calculate the physical fatigue degree of the subject based on the subjective measurement value (such as the questionnaire result for measuring physical fatigue degree) indicated by the input signal S1 instead of the objective measurement value of the subject indicated by the sensor signal S3. In this case, the physical fatigue degree calculation model used by the physical fatigue degree calculation unit 16 is pre-trained to output the physical fatigue degree of the subject when the subjective measurement value indicated by the input signal S1 is input, and the learned parameters thereof are stored in the fatigue degree calculation model storage unit 42. In yet another example, the physical fatigue degree calculation unit 16 may calculate the physical fatigue degree of the subject based on the subject information (such as information related to the training volume) stored in the subject information storage unit 41 instead of or in addition to the objective measurement value of the subject indicated by the sensor signal S3. In this case, the physical fatigue degree calculation model used by the physical fatigue degree calculation unit 16 is pre-trained to output the physical fatigue degree of the subject when the subject information is input, and the learned parameters thereof are stored in the fatigue degree calculation model storage unit 42.

[0033] Also, the objective measurement value used by the mental fatigue degree calculation unit 15 and the objective measurement value used by the physical fatigue degree calculation unit 16 may be different. In this case, the mental fatigue degree calculation unit 15 calculates the mental fatigue degree using a specific type of objective measurement value suitable for the calculation of mental fatigue degree, and the physical fatigue degree calculation unit 16 calculates the physical fatigue degree using a specific type of objective measurement value suitable for the calculation of physical fatigue degree.

[0034] The overall fatigue degree calculation unit 17 calculates the overall fatigue degree of the subject based on the fatigue degree calculation pattern β determined by the fatigue degree calculation pattern determination unit 14, the mental fatigue degree calculated by the mental fatigue degree calculation unit 15, and the physical fatigue degree calculated by the physical fatigue degree calculation unit 16. In this case, based on the fatigue degree calculation pattern β determined by the fatigue degree calculation pattern determination unit 14, the overall fatigue degree calculation unit 17 selects the overall fatigue degree calculation model to be used from the overall fatigue degree calculation models registered in the fatigue degree calculation model storage unit 42 (also referred to as the "overall fatigue degree calculation model"). Then, the overall fatigue degree calculation unit 17 inputs the mental fatigue degree and the physical fatigue degree into the selected overall fatigue degree calculation model to obtain the overall fatigue degree of the subject. In this case, the overall fatigue degree calculation model is a model that has been previously learned to output the overall fatigue degree of the subject when the mental fatigue degree and the physical fatigue degree are input. The overall fatigue degree calculation unit 17 supplies the calculated overall fatigue degree to the output control unit 18.

[0035] The output control unit 18 displays information regarding the overall fatigue degree calculated by the overall fatigue degree calculation unit 17 on the display unit or outputs it as audio by the audio output unit. In this case, the output control unit 18 may, for example, determine the level of the fatigue state of the subject and notify the determined level. In this case, the output control unit 18 compares the overall fatigue degree of the subject with a threshold value previously stored in the storage device 4 or the memory 12 to determine whether the subject is in a high fatigue state that requires attention or countermeasures, and outputs the determination result. Note that a plurality of the above threshold values may be provided to classify the fatigue state step by step. Further, in addition to the overall fatigue degree, the output control unit 18 may also target for output information regarding the mental fatigue degree and the physical fatigue degree.

[0036] Note that each component of the fatigue degree calculation pattern determination unit 14, mental fatigue degree calculation unit 15, physical fatigue degree calculation unit 16, comprehensive fatigue degree calculation unit 17, and output control unit 18 described in FIG. 3 can be realized, for example, by the processor 11 executing a program. Further, by recording a necessary program in an arbitrary non-volatile storage medium and installing it as needed, each component may be realized. Note that at least a part of each of these components is not limited to being realized by software according to a program, and may be realized by any combination of hardware, firmware, and software. Further, at least a part of each of these components may be realized using, for example, an integrated circuit programmable by a user, such as an FPGA (Field-Programmable Gate Array) or a microcontroller. It may also be realized in this way. In this case, a program composed of the above-described components may be realized using this integrated circuit. Further, at least a part of each component may be composed of an ASSP (Application Specific Standard Produce), an ASIC (Application Specific Integrated Circuit), or a quantum processor (quantum computer control chip). Thus, each component may be realized by various hardware. The above also applies to other embodiments described later. Further, each of these components may be realized by the cooperation of a plurality of computers using, for example, cloud computing technology or the like. The above also applies to other embodiments described later.

[0037] (4) Selection of fatigue calculation model FIG. 4 is a table showing a physical fatigue degree calculation model, a mental fatigue degree calculation model, and a comprehensive fatigue degree calculation model associated with each fatigue degree calculation pattern β. Hereinafter, "n" (n is an integer of 2 or more) represents the total number of patterns of the fatigue degree calculation pattern β. For convenience of explanation, each fatigue degree calculation pattern β is assumed to be a sequential number from 1 to n.

[0038] As shown in FIG. 4, for each fatigue degree calculation pattern β, a physical fatigue degree calculation model, a mental fatigue degree calculation model, and a comprehensive fatigue degree calculation model are respectively associated. Here, models "P1" to "Pn" are physical fatigue degree calculation models, models "M1" to "Mn" are mental fatigue degree calculation models, and models "T1" to "Tn" are comprehensive fatigue degree calculation models. And each fatigue degree calculation model is learned for each corresponding fatigue degree calculation pattern β.

[0039] For example, model P1 is learned using the objective measurement value of the subject when the fatigue degree calculation pattern β is "β = 1" and the physical fatigue degree (for example, a score based on a questionnaire, etc.) that is the correct answer. Similarly, model M1 is learned using the objective measurement value of the subject when the fatigue degree calculation pattern β is "β = 1" and the mental fatigue degree that is the correct answer. Also, model T1 is learned using the physical fatigue degree and mental fatigue degree of the subject when the fatigue degree calculation pattern β is "β = 1" and the comprehensive fatigue degree that is the correct answer. Each fatigue degree calculation model associated with a fatigue degree calculation pattern β other than "β = 1" is also learned in the same way. And the parameters and the like obtained by these learnings are stored in the fatigue degree calculation model storage unit 42. Note that the physical fatigue degree calculation models P1 to Pn do not necessarily all have to be different, and at least some of them may be the same model. The same applies to the mental fatigue degree calculation models M1 to Mn.

[0040] Next, a method for determining the fatigue degree calculation pattern β based on the subject information will be described.

[0041] In the first example, when the information of the person to be measured is season information, the fatigue degree calculation pattern determination unit 14 determines whether the person to be measured is during the season at the measurement time based on the season information and the measurement date and time, and determines the fatigue degree calculation pattern β based on the determination result. In this case, the total number of patterns n is 2, and the fatigue degree calculation pattern β becomes, for example, "1" when it is during the season and "2" when it is off-season. Note that the fatigue degree calculation pattern β may be designed so that the total number of patterns n is 3 or more (4 in this example) by further subdividing the period, such as "immediately before the season", "during the season", "immediately after the off-season", and "other periods".

[0042] In the second example, when the information of the person to be measured is the health information of the person to be measured, the fatigue degree calculation pattern determination unit 14 determines whether the injury state of the person to be measured at the measurement time is "the state immediately after injury", "the state during treatment", or "the state after recovery from injury or without injury" based on the health information of the person to be measured and the measurement date and time, and determines the fatigue degree calculation pattern β based on the determination result. In this case, the total number of patterns n is 3. Note that the total number of patterns n may be 2 by determining whether the person to be measured is injured at the measurement time, or the total number of patterns n may be 4 or more by more finely determining the injury state and the like.

[0043] In the third example, when the information of the person to be measured is exercise history information, the fatigue degree calculation pattern determination unit 14 determines the fatigue degree calculation pattern β based on the exercise menu or the exercise load executed within a predetermined time (predetermined number of days) at the measurement time. In this case, for example, the correspondence information indicating the correspondence between each fatigue degree calculation pattern β and the type (and number of executions) of the corresponding exercise menu or / and the exercise load is stored in the storage device 4 or the memory 12. Then, the fatigue degree calculation pattern determination unit 14 determines the fatigue degree calculation pattern β from the exercise history information by referring to the correspondence information. In this case, the total number of patterns n may be any number of 2 or more.

[0044] Note that the fatigue level calculation pattern determination unit 14 may determine the fatigue level calculation pattern β based on a plurality of pieces of information among the season information, the measured person's health information, and the practice history information. In this case, for example, the fatigue level calculation pattern determination unit 14 determines the fatigue level calculation pattern β based on the state or / and environment of the measured person at the measurement time represented by these pieces of information. In this case, for example, correspondence information indicating the correspondence relationship between each fatigue level calculation pattern β and the corresponding state or / and environment of the measured person is stored in the storage device 4 or the memory 12, and the fatigue level calculation pattern determination unit 14 refers to the correspondence information to determine the corresponding fatigue level calculation pattern β.

[0045] As described above, the fatigue level calculation pattern determination unit 14 determines the fatigue level calculation pattern β based on the measured person information that is information affecting the physical or mental aspect of the measured person. Thereby, the fatigue level calculation pattern determination unit 14 can cause the mental fatigue level calculation unit 15, the physical fatigue level calculation unit 16, and the comprehensive fatigue level calculation unit 17 to use appropriate fatigue level calculation models.

[0046] (5) Calculation example of total fatigue Next, a specific example of the comprehensive fatigue level calculation by the comprehensive fatigue level calculation unit 17 will be described.

[0047] Assuming that the comprehensive fatigue level calculated by the comprehensive fatigue level calculation unit 17 is "Y", the mental fatigue level is "X1", the physical fatigue level is "X2", and the function representing the comprehensive fatigue level calculation model is "G", the following equation holds. Y = G(X1, X2, β) Here, as an example, assume that the function G is a multiple regression model. In this case, if the fatigue level calculation pattern β is i (i = 1 to n), the comprehensive fatigue level Y i is represented by the following equation (1). Y i = w1 i X1 i + w2 i X2 i + w0 i (1) "w0 i ", "w1 i ", "w2 iis a parameter to be obtained by learning, and represents the parameter when the fatigue degree calculation pattern β is "β = i". For example, assuming n = 2, and "β = 1" corresponds to season on and "β = 2" corresponds to season off. In this case, a plurality of sets of mental fatigue degree X1, physical fatigue degree X2, and comprehensive fatigue degree Y measured within a certain period during season on are used as a learning dataset, and "w01", "w11", "w21" are learned. Also, a plurality of sets of mental fatigue degree X1, physical fatigue degree X2, and comprehensive fatigue degree Y measured during a certain period during season off are used as a learning dataset, and "w02", "w12", "w22" are learned. These parameters are stored in the fatigue degree calculation model storage unit 42. Note that, for example, in the collection of the learning dataset, for example, the mental fatigue degree X1 is the fatigue score obtained by the POMS (Profile of Mood States) questionnaire, the physical fatigue degree X2 is, for example, the score based on the maximum speed measured by a jump test, and the comprehensive fatigue degree Y is, for example, the score based on the analysis result of the saliva of the subject being measured.

[0048] And when the comprehensive fatigue degree calculation unit 17 calculates the comprehensive fatigue degree Y using Equation (1), it sets each parameter of Equation (1) based on the fatigue degree calculation pattern β determined by the fatigue degree calculation pattern determination unit 14. Then, the comprehensive fatigue degree calculation unit 17 uses the mental fatigue degree calculated by the mental fatigue degree calculation unit 15 as X1 and the physical fatigue degree calculated by the physical fatigue degree calculation unit 16 as X2, and calculates the comprehensive fatigue degree Y based on Equation (1) with each parameter set.

[0049] Note that the comprehensive fatigue degree calculation unit 17 may calculate the comprehensive fatigue degree based on a plurality of scores representing the mental fatigue degree and a plurality of scores representing the physical fatigue degree. Here, the plurality of scores of the mental fatigue degree and the plurality of scores of the physical fatigue degree may be scores calculated by the mental fatigue degree calculation unit 15 and the physical fatigue degree calculation unit 16 within a predetermined period from the measurement time point, or may be scores calculated by a plurality of mental fatigue degree methods and a plurality of physical fatigue degree calculation methods.

[0050] In this case, assuming that the index of a plurality of combinations of mental fatigue levels and physical fatigue levels to be used is "j" (j = 1, ..., m), the overall fatigue level Y corresponding to "β = i" i is represented by the following formula (2). Y i = Σ j=1,…m (w1 ij X1 ij + w2 ij X2 ij ) + w0 i (2) Note that the parameters corresponding to "w0 i ", "w1 ij ", and "w2 ij " are obtained in advance through learning and stored in the fatigue level calculation model storage unit 42. According to formula (2), the overall fatigue level calculation unit 17 can accurately calculate the overall fatigue level Y based on a plurality of combinations of mental fatigue levels and physical fatigue levels.

[0051] (6) Processing flow FIG. 5 is an example of a flowchart executed by the fatigue level calculation device 1 in the first embodiment. The fatigue level calculation device 1 repeatedly executes the processing of the flowchart shown in FIG. 5.

[0052] First, the fatigue level calculation device 1 acquires measurement values (objective measurement values or subjective measurement values) regarding the subject to be measured, subject information, etc. (step S11). In this case, the fatigue level calculation device 1 acquires the measurement values used for calculating the mental fatigue level and the physical fatigue level by receiving the input signal S1 or the sensor signal S3 via the interface 13. Also, the fatigue level calculation device 1 extracts the subject information corresponding to the subject to be measured from the subject information storage unit 41.

[0053] Next, the fatigue level calculation pattern determination unit 14 of the fatigue level calculation device 1 determines the fatigue level calculation pattern β based on the information of the person to be measured (step S12). Then, the fatigue level calculation device 1 calculates the mental fatigue level and the physical fatigue level (step S13). In this case, the mental fatigue level calculation unit 15 calculates the mental fatigue level based on the mental fatigue level calculation model associated with the fatigue level calculation pattern β determined in step S12 and the measurement values of the person to be measured obtained in step S11, and the physical fatigue level calculation unit 16 calculates the physical fatigue level based on the physical fatigue level calculation model associated with the fatigue level calculation pattern β determined in step S12 and the measurement values of the person to be measured obtained in step S11.

[0054] Next, the fatigue level calculation device 1 calculates the comprehensive fatigue level (step S14). In this case, the comprehensive fatigue level calculation unit 17 calculates the comprehensive fatigue level based on the comprehensive fatigue level calculation model based on the fatigue level calculation pattern β determined in step S12 and the mental fatigue level and the physical fatigue level calculated in step S13. Then, the output control unit 18 outputs information regarding the calculated fatigue level (step S15). In this case, the output control unit 18 notifies the person to be measured or the administrator of the person to be measured of the fatigue state of the person to be measured by, for example, displaying or audibly outputting the calculation result such as the comprehensive fatigue level.

[0055] (7) Modification example Next, a modification suitable for the first embodiment will be described. The following modifications may be applied in combination.

[0056] (First Modification) Instead of calculating the comprehensive fatigue level based on the mental fatigue level and the physical fatigue level, the comprehensive fatigue level calculation unit 17 may calculate the comprehensive fatigue level based on the measurement values of the person to be measured.

[0057] FIG. 6 is an example of the functional blocks of the processor 11 according to the first modification. In the example of FIG. 6, the comprehensive fatigue degree calculation unit 17 acquires the fatigue degree calculation pattern β determined by the fatigue degree calculation pattern determination unit 14 and the input signal S1 or the sensor signal S3. Then, the comprehensive fatigue degree calculation unit 17 selects a comprehensive fatigue degree calculation model based on the fatigue degree calculation pattern β, and inputs the measured value of the subject indicated by the input signal S1 or the sensor signal S3 into the selected comprehensive fatigue degree calculation model, thereby calculating the comprehensive fatigue degree. In this case, the comprehensive fatigue degree calculation model is a model that has been pre-trained to output the comprehensive fatigue degree of the subject when the measured value of the subject or its feature amount is input.

[0058] In this way, the fatigue degree calculation device 1 may calculate the comprehensive fatigue degree without calculating the physical fatigue degree and the mental fatigue degree. Further, the fatigue degree calculation device 1 may calculate the comprehensive fatigue degree using only one of the physical fatigue degree and the mental fatigue degree. In this case, the comprehensive fatigue degree calculation unit 17 calculates the comprehensive fatigue degree based on the comprehensive fatigue degree calculation model associated with the fatigue degree calculation pattern β determined by the fatigue degree calculation pattern determination unit 14 and the fatigue degree (and the measured value of the subject) calculated by the mental fatigue degree calculation unit 15 or the physical fatigue degree calculation unit 16. In this way, the fatigue degree calculation device 1 may calculate at least one of the physical fatigue degree and the mental fatigue degree, and calculate the comprehensive fatigue degree based on at least one of the physical fatigue degree and the mental fatigue degree and the fatigue degree calculation pattern β.

[0059] (Second Modification) Fatigue is not limited to the classification of physical fatigue and mental fatigue.

[0060] Alternatively, for example, fatigue may be classified into central fatigue and peripheral fatigue. In this case, central fatigue refers to the fatigue felt by the brain due to mental activities, and peripheral fatigue represents fatigue in the periphery other than the center. In this case, the fatigue degree calculation device 1 calculates the central fatigue degree and the peripheral fatigue degree by the same process as the calculation process of the mental fatigue degree and the physical fatigue degree, and based on the calculated central fatigue degree and peripheral fatigue degree and the comprehensive fatigue degree calculation model associated with the fatigue degree calculation pattern β, calculates the comprehensive fatigue degree obtained by combining the central fatigue and the peripheral fatigue. In this case, in the fatigue degree calculation model storage unit 42, instead of the mental fatigue degree calculation model and the physical fatigue degree calculation model, the parameters of the central fatigue degree calculation model and the peripheral fatigue degree calculation model associated with each fatigue degree calculation pattern β are stored.

[0061] In other examples, fatigue may be classified according to the cause of occurrence. In this case, for example, Fatigue due to pure exercise (first fatigue degree) Mental fatigue due to stress in human relationships (second fatigue degree) Fatigue caused by the environment such as heat or cold (third fatigue degree) In this case, the fatigue degree calculation device 1 calculates the comprehensive fatigue degree obtained by combining the above three fatigue degrees based on these three fatigue degrees and the comprehensive fatigue degree calculation model associated with the fatigue degree calculation pattern β. Also, the fatigue degree calculation device 1 calculates these three fatigue degrees as well by the same process as the calculation process of the mental fatigue degree and the physical fatigue degree. In this case, in the fatigue degree calculation model storage unit 42, instead of the mental fatigue degree calculation model and the physical fatigue degree calculation model, the parameters of the calculation models of the above three fatigue degrees associated with each fatigue degree calculation pattern β are stored.

[0062] As described above, the fatigue degree calculation device 1 may calculate the comprehensive fatigue degree based on the fatigue degrees based on various classifications. The mental fatigue degree, the physical fatigue degree, the central fatigue degree, the peripheral fatigue degree, and the first to third fatigue degrees are examples of "classified fatigue degrees". Also, the mental fatigue degree calculation unit 15 and the physical fatigue degree calculation unit 16 are examples of "classified fatigue degree calculation means".

[0063] <Second Embodiment> FIG. 7 shows a schematic configuration of the fatigue degree calculation system 100A in the second embodiment. The fatigue degree calculation system 100A according to the second embodiment is a server-client model system, and a fatigue degree calculation device 1A functioning as a server device performs the processing of the fatigue degree calculation device 1 in the first embodiment. Hereinafter, for the same components as those in the first embodiment, the same reference numerals will be appropriately assigned, and the description thereof will be omitted.

[0064] As shown in FIG. 7, the fatigue degree calculation system 100A mainly includes a fatigue degree calculation device 1A functioning as a server, a storage device 4 that stores data necessary for the fatigue degree calculation process, a terminal device 8 functioning as a client, and a management device 9 that manages personal information of a plurality of persons including the person to be measured. The fatigue degree calculation device 1A and the terminal device 8 perform data communication via a network 7.

[0065] The terminal device 8 is a terminal having an input function, a display function, and a communication function, and functions as the input device 2 and the output device 3 shown in FIG. 1. The terminal device 8 may be, for example, a personal computer, a tablet-type terminal, a PDA (Personal Digital Assistant), or the like. The terminal device 8 transmits an objective measurement value of the person to be measured output by the sensor 5 (that is, information corresponding to the sensor signal S3 in FIG. 1) or a subjective measurement value based on user input (that is, information corresponding to the input signal S1 in FIG. 1) to the fatigue degree calculation device 1A.

[0066] The fatigue degree calculation device 1A has the same hardware configuration as that of the fatigue degree calculation device 1 shown in FIG. 2, and the processor 11 of the fatigue degree calculation device 1A has the functional blocks shown in FIG. 3. Then, the fatigue degree calculation device 1A receives information such as the information obtained by the fatigue degree calculation device 1 shown in FIG. 1 from the input device 2 and the sensor 5 from the terminal device 8 via the network 7. In addition, the fatigue degree calculation device 1A transmits an output signal indicating information regarding the fatigue degree of the subject to the terminal device 8 via the network 7 based on a request from the terminal device 8. Further, when the subject information corresponding to the subject is not stored in the storage device 4, the fatigue degree calculation device 1A receives subject information such as the past history of the subject from the management device 9 via the network 7, and determines the fatigue degree calculation pattern β based on the received subject information.

[0067] As described above, the fatigue degree calculation device 1A according to the second embodiment can suitably present information regarding the fatigue degree to the user of the terminal device 8.

[0068] <Third Embodiment> FIG. 8 is a block diagram of the fatigue degree calculation device 1X in the third embodiment. The fatigue degree calculation device 1X mainly includes a fatigue degree calculation pattern acquisition unit 14X and a fatigue degree calculation unit 17X. Note that the fatigue degree calculation device 1X may be configured by a plurality of devices.

[0069] The fatigue degree calculation pattern acquisition unit 14X acquires a fatigue degree calculation pattern, which is a calculation pattern of the fatigue degree of the subject determined based on the subject information regarding the state or environment of the subject. In this case, the fatigue degree calculation pattern acquisition unit 14X may determine the fatigue degree calculation pattern based on the subject information, or may acquire a fatigue degree calculation pattern that has been determined in advance based on the subject information and is associated with the subject from a storage device or the like. The fatigue degree calculation pattern acquisition unit 14X can be, for example, the fatigue degree calculation pattern determination unit 14 in the first embodiment (including modifications, the same applies hereinafter) or the second embodiment.

[0070] The fatigue level calculation means 17X calculates the fatigue level of the person to be measured based on the fatigue level calculation pattern. The fatigue level in this case may be a physical fatigue level, a mental fatigue level, a comprehensive fatigue level, or any other arbitrary fatigue level. The fatigue level calculation means 17X can be, for example, the mental fatigue level calculation unit 15, the physical fatigue level calculation unit 16, or the comprehensive fatigue level calculation unit 17 in the first embodiment or the second embodiment.

[0071] FIG. 9 is an example of a flowchart executed by the fatigue level calculation device 1X in the third embodiment. First, the fatigue level calculation pattern acquisition means 14X acquires a fatigue level calculation pattern, which is a fatigue level calculation pattern of the person to be measured determined based on the person to be measured information regarding the state or environment of the person to be measured (step S21). The fatigue level calculation means 17X calculates the fatigue level of the person to be measured based on the fatigue level calculation pattern (step S22).

[0072] The fatigue level calculation device 1X according to the third embodiment can accurately calculate the fatigue level of the person to be measured.

[0073] In addition, in each of the above-described embodiments, the program is stored using various types of non-transitory computer readable media and can be supplied to a processor or the like that is a computer. The non-transitory computer readable media include various types of tangible storage media. Examples of the non-transitory computer readable media are magnetic storage media (e.g., flexible disks, magnetic tapes, hard disk drives), magneto-optical storage media (e.g., magneto-optical disks), CD-ROM (Read Only Memory), CD-R, CD-R / W, semiconductor memories (e.g., mask ROM, PROM (Programmable ROM), EPROM (Erasable PROM), flash ROM ​, including a RAM (Random Access Memory). Also, the program may be supplied to the computer by various types of transitory computer readable media. Examples of transitory computer readable media include electrical signals, optical signals, and electromagnetic waves. The transitory computer readable media can supply the program to the computer via wired communication channels such as electric wires and optical fibers, or wireless communication channels. The computer may be supplied with the program by various types of transitory computer readable media. Examples of transitory computer readable media include electrical signals, optical signals, and electromagnetic waves. The transitory computer readable media can supply the program to the computer via wired communication channels such as electric wires and optical fibers, or wireless communication channels.

[0074] As described above, the present invention has been described with reference to the embodiments, but the present invention is not limited to the above embodiments. Various changes that can be understood by those skilled in the art can be made to the configuration and details of the present invention within the scope of the present invention. That is, the present invention naturally includes various modifications and corrections that those skilled in the art could make in accordance with the entire disclosure including the claims and the technical idea. Also, each disclosure of the above-cited patent documents and the like is incorporated herein by reference.

Explanation of Reference Numerals

[0075] 1, 1A, 1X Fatigue Degree Calculation Device 2 Input Device 3 Output Device 4 Storage Device 5 Sensor 8 Terminal Device 100, 100A Fatigue Degree Calculation System

Claims

Claim 1. Acquisition means for acquiring at least one of measurement values of a subject who is a sports player, season information regarding a schedule on which a competition performed by the subject is held, state information regarding an injury of the subject, or practice history information of the subject, and subject information indicating the same; Fatigue degree calculation pattern determination means for determining a fatigue degree calculation pattern that is a calculation pattern of the fatigue degree of the subject based on the subject information; Fatigue degree calculation means for calculating the fatigue degree of the subject based on the measurement value and the fatigue degree calculation pattern; A fatigue degree calculation device having the above.

2. The fatigue degree calculation means selects a fatigue degree calculation model associated with the fatigue degree calculation pattern, and calculates the fatigue degree based on the selected fatigue degree calculation model. The fatigue degree calculation device according to Claim 1.

3. The fatigue degree calculation means Classification fatigue degree calculation means for calculating a classified fatigue degree that is a classified fatigue degree of the subject based on the measurement value; Comprehensive fatigue degree calculation means for calculating a comprehensive fatigue degree that is a comprehensive fatigue degree of the subject based on the classified fatigue degree and the fatigue degree calculation pattern; The fatigue degree calculation device according to Claim 1 or 2, having the above.

4. The classification fatigue degree calculation means calculates the classification fatigue degree based on the measurement value and a calculation model of the classification fatigue degree associated with the fatigue degree calculation pattern. The comprehensive fatigue degree calculation means calculates the comprehensive fatigue degree based on the classification fatigue degree and a calculation model of the comprehensive fatigue degree associated with the fatigue degree calculation pattern. The fatigue degree calculation device according to Claim 3.

5. The classification fatigue degree calculation means calculates at least one of a mental fatigue degree that is a mental fatigue degree of the subject and a physical fatigue degree that is a physical fatigue degree of the subject as the classification fatigue degree. The comprehensive fatigue degree calculation means calculates the comprehensive fatigue degree based on at least one of the mental fatigue degree or the physical fatigue degree and the fatigue degree calculation pattern. The fatigue degree calculation device according to Claim 3 or 4.

6. The fatigue degree calculation device according to any one of Claims 1 to 5, further comprising output control means for displaying or audibly outputting information regarding the fatigue degree.

7. A computer Obtain measurement values of a subject who is an athlete, and subject information indicating at least any one of season information regarding the schedule on which the competition performed by the subject is held, status information regarding injuries of the subject, or practice history information of the subject. Determine a fatigue level calculation pattern, which is a calculation pattern of the fatigue level of the subject, based on the subject information. Calculate the fatigue level of the subject based on the measurement values and the fatigue level calculation pattern. Fatigue level calculation method.

8. Obtain measurement values of a subject who is an athlete, and subject information indicating at least any one of season information regarding the schedule on which the competition performed by the subject is held, status information regarding injuries of the subject, or practice history information of the subject. Determine a fatigue level calculation pattern, which is a calculation pattern of the fatigue level of the subject, based on the subject information. A program for causing a computer to execute a process of calculating the fatigue level of the subject based on the measurement values and the fatigue level calculation pattern.

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