Fatigue estimation device, fatigue estimation system, and fatigue estimation method

Abstract

An estimation device (100) (fatigue estimation device) comprises: a first acquisition unit (101) (position acquisition unit) that acquires information regarding the positions of the body parts of a subject (11); a posture estimation unit (105) that estimates the posture of the subject (11) on the basis of the information acquired by the position acquisition unit; a fourth acquisition unit (104) (subjective acquisition unit) that acquires a subjective degree of fatigue felt by the subject (11); and a fatigue estimation unit (108) that estimates the degree of fatigue of the subject (11) on the basis of the duration of the posture estimated by the posture estimation unit (105) using the acquired subjective degree of fatigue as a starting degree of fatigue, calculates a parameter for determining an estimation formula from the acquired subjective degree of fatigue, and estimates the degree of fatigue of the subject (11) using an estimation formula to which the calculated parameter is applied.

Description

[Technical Field]

[0001] The present disclosure relates to a fatigue estimation system for estimating a fatigue level of a subject, a fatigue estimation device used in the estimation system, and a fatigue estimation method. [Background technology]

[0002] In recent years, there have been many cases where accumulated fatigue has led to poor health, injuries, accidents, etc. In response to this, attention has been drawn to technology that can prevent poor health, injuries, accidents, etc. by estimating the level of fatigue. For example, Patent Document 1 discloses a fatigue determination device as a fatigue estimation system for estimating the level of fatigue, which determines the presence or absence of fatigue and the type of fatigue based on force measurement and bioelectrical impedance measurement. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2017-023311 Summary of the Invention [Problem to be solved by the invention]

[0004] However, the accuracy of the estimated fatigue level may not be sufficient in the conventional fatigue determination device exemplified in the above-mentioned Patent Document 1. Therefore, the present disclosure provides a fatigue estimation device that estimates the fatigue level with higher accuracy. [Means for solving the problem]

[0005] A fatigue estimation device according to one aspect of the present disclosure includes a position acquisition unit that acquires information regarding the positions of body parts of a subject, a posture estimation unit that estimates the posture of the subject based on the information acquired by the position acquisition unit, a subjective acquisition unit that acquires a subjective fatigue level felt by the subject, and a fatigue estimation unit that uses the acquired subjective fatigue level as a starting point of fatigue level and estimates the fatigue level of the subject based on the duration of the posture estimated by the posture estimation unit, wherein the fatigue estimation unit calculates parameters for determining an estimation formula from the subjective fatigue level received as input, and estimates the fatigue level of the subject using the estimation formula to which the calculated parameters have been applied.

[0006] Furthermore, a fatigue estimation system according to one embodiment of the present disclosure includes an information output device that outputs information regarding the positions of body parts of a subject, a posture estimation unit that estimates the posture of the subject based on the information output by the information output device, a first receiving unit that receives input of a subjective fatigue level felt by the subject, a fatigue estimation unit that uses the received input subjective fatigue level as a starting point of fatigue level and estimates the fatigue level of the subject based on the duration of the posture estimated by the posture estimation unit, the fatigue estimation unit calculates parameters for determining an estimation formula from the received input subjective fatigue level and estimates the fatigue level of the subject using the estimation formula to which the calculated parameters have been applied, and a presentation device that presents the estimated fatigue level of the subject.

[0007] In addition, a fatigue estimation method according to one aspect of the present disclosure acquires information regarding the position of a body part of a subject, estimates the posture of the subject based on the acquired information, accepts input of the subjective fatigue level felt by the subject, calculates parameters for determining an estimation formula from the accepted input subjective fatigue level, sets the accepted input subjective fatigue level as the starting point of fatigue level, and estimates the fatigue level of the subject using the estimation formula to which the calculated parameters have been applied based on the duration of the estimated posture. [Effects of the Invention]

[0008] A fatigue estimation device according to an aspect of the present disclosure can estimate fatigue with higher accuracy. [Brief explanation of the drawings]

[0009] [Figure 1A] FIG. 1A is a first diagram for explaining estimation of a fatigue level according to an embodiment. [Figure 1B] FIG. 1B is a second diagram for explaining estimation of a fatigue level according to the embodiment. [Figure 1C] FIG. 1C is a third diagram for explaining estimation of a fatigue level according to the embodiment. [Figure 2A] FIG. 2A is a block diagram illustrating a functional configuration of a fatigue estimation system according to an embodiment. [Figure 2B] FIG. 2B is a diagram illustrating the determination of an estimation formula according to the embodiment. [Figure 3A] FIG. 3A is a flowchart showing a method for estimating a fatigue level according to an embodiment. [Figure 3B] FIG. 3B is a sub-flowchart showing details of some steps according to an embodiment. [Figure 4A] FIG. 4A shows a subject standing still in posture A. [Figure 4B] FIG. 4B shows the subject standing still in posture B. [Figure 5A] FIG. 5A is a first diagram illustrating an estimated accumulation of a subject's fatigue level according to an embodiment. [Figure 5B] FIG. 5B is a second diagram illustrating the estimated accumulation of the subject's fatigue level according to the embodiment. [Figure 6] FIG. 6 is a first diagram showing an example of displaying an estimation result according to the embodiment. [Figure 7] FIG. 7 is a second diagram showing an example of displaying an estimation result according to the embodiment. [Figure 8] FIG. 8 is a diagram for explaining posture estimation according to the modification of the embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0010] Hereinafter, the embodiments will be described in detail with reference to the drawings. Note that the embodiments described below are all comprehensive or specific examples. The numerical values, shapes, materials, components, component placement and connection forms, steps, and step order shown in the following embodiments are merely examples and are not intended to limit the present disclosure. Furthermore, among the components in the following embodiments, components not recited in independent claims will be described as optional components.

[0011] It should be noted that the drawings are schematic diagrams and are not necessarily strict illustrations. In addition, in the drawings, substantially the same components are denoted by the same reference numerals, and overlapping descriptions may be omitted or simplified.

[0012] (Embodiment) [Fatigue estimation system] The overall configuration of a fatigue estimation system according to an embodiment will be described below. Fig. 1A is a first diagram for explaining fatigue level estimation according to an embodiment. Fig. 1B is a second diagram for explaining fatigue level estimation according to an embodiment. Fig. 1C is a third diagram for explaining fatigue level estimation according to an embodiment.

[0013] In an embodiment, a fatigue estimation system 200 (see FIG. 2A described later) in the present disclosure is a system that estimates a fatigue level of a subject 11 using an image output by capturing an image of the subject 11 using an imaging device 201. The imaging device 201 is not limited to any particular form as long as it is a camera that captures an image of the subject 11 and outputs an image, and may be a fixed camera installed on a wall or ceiling of a building or the like as shown in FIG. 1A, or may be a camera mounted on a PC, smartphone, tablet terminal, or the like operated by the subject 11.

[0014] Here, the subject is seated in a chair 12, working with a work object placed on a desk top 13a of a desk 13. The fatigue estimation system 200 according to the present disclosure estimates the fatigue level of the subject 11 based on fatigue accumulated by maintaining a fixed, stationary posture. This means that fatigue accumulated due to strain on at least one of muscles and joints and impaired blood flow (hereinafter also referred to as decreased blood flow) caused by maintaining a fixed posture is estimated. Therefore, the subject 11 is in a stationary posture, sitting, lying down, or standing, for at least a certain period of time. The certain period is the minimum period for which fatigue can be estimated by the fatigue estimation system 200, such as several tens of seconds or a few seconds. This period is determined depending on the processing capabilities of the imaging device 201 and the estimation device 100 (an example of a fatigue estimation device, see FIG. 2A described below) that constitute the fatigue estimation system 200.

[0015] Examples of subjects 11 who assume such a static posture include desk workers in an office, drivers steering a moving object, people performing strength training using loads in a static posture, residents of facilities such as hospitals, and passengers and crew members of airplanes.

[0016] An image captured by imaging device 201 and output is processed by estimation device 100, and the posture of subject 11 is estimated as shown in FIG. 1B. The estimated posture of subject 11 is output as rigid link model 11a, for example. Specifically, as shown in FIG. 1B, skeletons indicated by straight lines are connected by joints indicated by black dots, and the posture of subject 11 can be reproduced by the positional relationship between two skeletons connected by one joint. The posture is estimated by image recognition, and is output as the above-mentioned rigid link model 11a based on the positional relationship between the joints and skeletons.

[0017] By fitting the estimated rigid link model 11a to a musculoskeletal model 11c as shown in Fig. 1C, the load on at least one of the muscles and joints of each body part is calculated as an estimate in order to maintain the positional relationship corresponding to the estimated posture for each body part, including the muscles that pull the skeletons together and the joints that variably connect the skeletons. Since the estimated load on at least one of the muscles and joints of each body part accumulates as the duration of the static posture increases, the fatigue level of the subject 11 caused by maintaining the static posture is calculated by a calculation using the estimated load and the duration. In the following description, "at least one of the muscles and joints" will also be expressed as "muscles and / or joints."

[0018] Furthermore, in this embodiment, in addition to the estimated value of the load on the muscles and / or joints, it is possible to estimate the level of fatigue based on the estimated value of the blood flow of the subject 11. In the following explanation, an example in which the fatigue level of the subject 11 is estimated using the estimated values ​​of the load on the muscles and the load on the joints will be mainly described, but it is also possible to combine the estimated value of the blood flow here to more accurately estimate the level of fatigue of the subject 11. Furthermore, it is also possible to estimate the level of fatigue of the subject 11 using the estimated value of any one of the load on the muscles, the load on the joints, and the blood flow of the subject 11.

[0019] That is, the fatigue estimation system 200 estimates the posture of the subject 11, and then estimates at least one of the load on the muscles, the load on the joints, and the blood flow rate of the subject 11 based on the duration of the posture. The fatigue estimation system 200 estimates the fatigue level of the subject 11 based on the estimated value of at least one of the load on the muscles, the load on the joints, and the blood flow rate of the subject 11. Hereinafter, for simplicity, the estimated value of the load may be simply referred to as the load or the estimated value. Furthermore, when the estimated value includes the estimated value of the blood flow rate, the load amount may be read as the blood flow rate, and a high load amount may be equated with a decrease in the blood flow rate, and a low load amount may be equated with an increase in the blood flow rate.

[0020] As described above, blood flow is information for quantifying the blood flow that deteriorates when subject 11 maintains a certain posture. The lower the blood flow, the worse the blood flow of subject 11 is, and blood flow can be used as an index of fatigue caused by the deterioration of blood flow. Blood flow may be acquired as an absolute value at the time of measurement, or as a relative value between two different points in time. For example, the degree of deterioration of blood flow in subject 11 can be estimated based on the posture of subject 11 and the relative values ​​of blood flow at two points in time, the start and end of the posture. Furthermore, because there is a correlation between the posture of subject 11, the duration of the posture, and the deterioration of blood flow, the blood flow of the subject may be estimated simply from the posture of subject 11 and the duration of the posture.

[0021] In the following description, the above-described musculoskeletal model 11c is used to estimate at least one of the muscle load, joint load, and blood flow rate from the posture of the subject 11. However, in addition to the above-described musculoskeletal model 11c, a method using actual measurement data can also be applied as a method for estimating the muscle load, joint load, and blood flow rate from the posture. This actual measurement data is a database constructed by accumulating the actual measurement values ​​of the muscle load, joint load, and blood flow rate measured for each posture in association with the posture. In this case, the fatigue estimation system 200 can input the estimated posture of the subject 11 into the database and obtain the actual measurement values ​​of the muscle load, joint load, and blood flow rate for the corresponding posture as outputs.

[0022] The actual measurement data may be constructed using actual measurement values ​​for each individual, taking into account individual differences among subjects 11, or may be constructed by qualifying big data obtained from an unspecified number of subjects through analytical processing such as statistical analysis or machine learning to suit each subject 11.

[0023] Next, the functional configuration of the fatigue estimation system 200 according to the present disclosure will be described with reference to Fig. 2A. Fig. 2A is a block diagram showing the functional configuration of the fatigue estimation system according to the embodiment.

[0024] As shown in FIG. 2A, a fatigue estimation system 200 according to the present disclosure includes an estimation device 100, an imaging device 201, a timing device 202, a pressure sensor 203, a reception device 204, a display device 205, and a recovery device 206.

[0025] The estimation device 100 includes a first acquisition unit 101, a second acquisition unit 102, a third acquisition unit 103, a fourth acquisition unit 104, a posture estimation unit 105, a first calculation unit 106, a second calculation unit 107, a fatigue estimation unit 108, an instruction unit 110, and an output unit 109.

[0026] The first acquisition unit 101 is a communication module connected to the imaging device 201 and acquires an image of the subject 11 captured from the imaging device 201. In other words, the first acquisition unit 101 is an example of a position acquisition unit. The connection between the first acquisition unit 101 and the imaging device 201 is wired or wireless, and there are no particular limitations on the method of communication performed via this connection.

[0027] The second acquisition unit 102 is a communication module that is connected to the timing device 202 and acquires the time from the timing device 202. The connection between the second acquisition unit 102 and the timing device 202 is wired or wireless, and there are no particular limitations on the method of communication that is performed via this connection.

[0028] The third acquisition unit 103 is a communication module that is connected to the pressure sensor 203 and acquires the pressure distribution from the pressure sensor 203. The connection between the third acquisition unit 103 and the pressure sensor 203 is wired or wireless, and there are no particular limitations on the method of communication that is performed via this connection.

[0029] The fourth acquisition unit 104 is a communication module connected to the reception device 204 and acquires, from the reception device 204, the subjective fatigue level felt by the subject 11 and personal information of the subject 11. In other words, the fourth acquisition unit 104 has both the function of a subjective acquisition unit and the function of a personal information acquisition unit. The connection between the fourth acquisition unit 104 and the reception device 204 is made by wire or wirelessly, and there are no particular limitations on the method of communication made via this connection.

[0030] The posture estimation unit 105 is a processing unit that is realized by executing a predetermined program using a processor and a memory. Through processing by the posture estimation unit 105, the posture of the subject 11 is estimated based on the image acquired by the first acquisition unit 101 and the pressure distribution acquired by the third acquisition unit 103.

[0031] The first calculation unit 106 is a processing unit that is realized by executing a predetermined program using a processor and a memory. Through processing by the first calculation unit 106, the load amount applied to each muscle and / or joint is calculated based on the estimated posture of the subject 11 and the personal information acquired by the fourth acquisition unit.

[0032] The second calculation unit 107 is a processing unit that is realized by executing a predetermined program using a processor and a memory. Through the processing of the second calculation unit 107, the amount of recovery from fatigue in each muscle and / or joint is calculated based on the amount of change in the estimated posture change of the subject.

[0033] The fatigue estimation unit 108 is a processing unit that is realized by executing a predetermined program using a processor and a memory. The fatigue estimation unit 108 estimates the fatigue level of the subject 11 based on the duration of the estimated posture using the posture estimated by the posture estimation unit 105 and the time acquired by the second acquisition unit 102.

[0034] More specifically, the fatigue estimation unit 108 estimates the fatigue level of the subject 11 based on the duration of the posture estimated by the posture estimation unit 105. At this time, the fatigue estimation unit 108 determines an estimation formula to be used to estimate the fatigue level of the subject 11 based on the input subjective fatigue level, and estimates the fatigue level of the subject 11 according to the determined estimation formula. To determine the estimation formula, the fatigue estimation unit 108 uses the subjective fatigue level and personal information acquired by the fourth acquisition unit 104. More specifically, the fatigue estimation unit 108 uses a candidate formula selection model that has previously learned, by machine learning, the relationship between personal information and candidate formulas that are candidates for the estimation formula. The fatigue estimation unit 108 obtains an output candidate formula by inputting the acquired personal information into the candidate formula selection model. The candidate formula is, for example, a function whose coefficients are indefinite. The candidate formula is a function selected from any function, such as a linear function, quadratic function, exponential function, or logarithmic function, clustered from the personal information of the subject 11 in terms of susceptibility to fatigue, timing of increase in fatigue level, etc., or a combination of two or more functions.

[0035] The fatigue estimation unit 108 determines an estimation formula by applying parameters calculated from the acquired subjective fatigue level to the obtained candidate formula. The parameters are numerical values ​​corresponding to coefficients in an undefined state in the candidate formula. In this way, the fatigue estimation unit 108 determines an estimation formula by applying the calculated parameters as coefficients of the candidate formula. In addition to the subjective fatigue level, the current estimated fatigue level based on the estimation formula currently determined and used to estimate the fatigue level is used to calculate the parameters. The fatigue estimation unit 108 changes the parameters of the estimation formula used so that the current estimated fatigue level becomes the numerical value of the acquired subjective fatigue level. After determining the new estimation formula, the fatigue level estimation is started so that the fatigue level increases or decreases, starting from the acquired subjective fatigue level.

[0036] FIG. 2B is a diagram illustrating the determination of an estimation formula according to an embodiment. FIG. 2B shows a schematic graph of fatigue levels estimated in this embodiment along a time series. The solid line graph in the figure indicates the fatigue level values ​​actually output from the estimation device 100, and the dashed line graph indicates hypothetical fatigue level values ​​for explanation. The figure shows the progression of fatigue levels from the start of estimation at a first time point T1 to a second time point T2. For example, in the example in the figure, a candidate formula for a quadratic function is selected from the personal information of the subject 11.

[0037] From the first time point T1 until the subjective fatigue level is acquired and a new estimation formula is determined, y = ax 2 The fatigue level is estimated using the estimation formula of +bx+c. Then, at the moment when the subjective fatigue level is acquired and a new estimation formula is determined, the fatigue level has reached the white dot shown as (a). On the other hand, according to the acquired subjective fatigue level, the fatigue level of the subject 11 has reached the fatigue level of the black dot shown as (b). In this embodiment, since the subjective fatigue level is given priority in this case, the fatigue level when the subjective fatigue level is acquired and a new estimation formula is determined is the fatigue level of the black dot shown as (b). Then, a new coefficient (parameter) of the quadratic function (dashed line graph on the left side of the page) linking the fatigue level at the first time point T1 and the fatigue level of the black dot shown as (b) is calculated. The new estimation formula to which this coefficient is applied is: y=a'x 2 +b'x+c'. The fatigue level estimated during the period from when the subjective fatigue level is acquired and a new estimation formula is determined to the second time point T2 will be a fatigue level closer to the fatigue level felt by subject 11, starting from the fatigue level indicated by the black dot in (b), using the determined estimation formula. In particular, if the estimation formula differs in the progression of the fatigue level felt by subject 11, the gap is likely to widen as time passes, as shown at second time point T2, etc.

[0038] In the above example, a parameter is calculated using a subjective fatigue level and a current estimated fatigue level based on an estimation formula that is currently determined and used to estimate fatigue level. However, the parameter may also be calculated using a parameter log that includes parameters calculated in the past.

[0039] For this reason, the estimation device 100 may be provided with a device (not shown) capable of storing (accumulating) information, such as a semiconductor memory, an optical disk, or a magnetic disk, and may store previously calculated parameters as a parameter log.

[0040] For example, the fatigue estimation unit 108 may calculate the parameters for determining the estimation formula as the average value of previously calculated parameters included in the parameter log and a newly calculated value using the subjective fatigue level and the current estimated fatigue level based on the estimation formula currently determined and used to estimate the fatigue level. Also, considering that the subjective opinion of the subject 11 is fluid and may change over time, the fatigue estimation unit 108 may calculate the parameters for determining the estimation formula by multiplying and adding a weighting coefficient so that the shorter the elapsed time, the greater the weight.

[0041] For example, consider a case where there are three previously calculated parameters included in the parameter log. A newly calculated value using the subjective fatigue level and the current estimated fatigue level based on the currently determined estimation formula used to estimate the fatigue level is multiplied by a weighting factor such as 0.50, which is a relatively large weight. The most recent parameter included in the parameter log is multiplied by a weighting factor such as 0.30. The second most recent parameter included in the parameter log is multiplied by a weighting factor such as 0.15. The oldest parameter included in the parameter log is multiplied by a weighting factor such as 0.05, which is a relatively small weight. The fatigue estimation unit 108 can calculate parameters for determining a new estimation formula by adding these values ​​after multiplication by the weighting factors. The sum of the weighting factors is set to 1.00.

[0042] The instruction unit 110 is a processing unit realized by executing a predetermined program using a processor and a memory. The instruction unit 110 is connected to the display device 205 and instructs the subject 11 to input a subjective fatigue level in a first cycle. The connection between the instruction unit 110 and the display device 205 is made by wire or wirelessly, and there is no particular limitation on the method of communication made through this connection.

[0043] The instructing unit 110 generates an image including instructions such as "Please input the level of fatigue you are currently feeling" to prompt the subject 11 to input his / her subjective fatigue level, and outputs the image to the display device 205, thereby displaying the image. The subject 11 inputs his / her subjective fatigue level to the reception device 204 in accordance with the displayed image, thereby causing the fatigue estimation system 200 to accept the input of the subjective fatigue level. The estimation device 100 can then acquire the subjective fatigue level using the fourth acquisition unit 104.

[0044] The reception device 204 constantly receives input of the subjective fatigue level from the subject 11. That is, it receives input of the subjective fatigue level from the subject 11 in a cycle (second cycle) within the cycle (first cycle) of instructions from the instruction unit 110 displayed in the first cycle. Similarly, the fourth acquisition unit 104 acquires the subjective fatigue level in a second cycle within the first cycle. The more frequently the subjective fatigue level is acquired, the more accurate the fatigue level of the subject 11 estimated by the fatigue estimation unit 108 can be. Therefore, the estimation device 100 is configured to acquire at least the subjective fatigue level input in response to instructions from the instruction unit 110 in the first cycle, while also being able to acquire the subjective fatigue level input at any timing by the subject 11. If no arbitrary subjective fatigue level is input by the subject 11, the first cycle and the second cycle coincide.

[0045] In addition to instructions via the display device 205, the instruction unit 110 may also instruct the subject 11 to input his / her subjective fatigue level by generating and playing back audio including instructions such as "Please input the level of fatigue you are currently feeling" via a sound output device not shown.

[0046] The output unit 109 is a communication module that is connected to the display device 205 and the recovery device 206 and outputs content based on the fatigue level estimation result by the estimation device 100 to the display device 205 and the recovery device 206. The connection between the output unit 109 and the display device 205 or the recovery device 206 is made by wire or wirelessly, and there are no particular limitations on the method of communication made through this connection.

[0047] As described above, the imaging device 201 is a device that captures an image of the subject 11 and outputs the image, and is realized by a camera. As the imaging device 201, an existing camera such as a security camera or a fixed camera in the space where the fatigue estimation system 200 is applied may be used, or a dedicated camera may be newly installed. Such an imaging device 201 is an example of an information output device that outputs an image as information related to the positions of the body parts of the subject 11. Therefore, the output information is an image, and is information including the positional relationship of the body parts of the subject 11 projected on the imaging element.

[0048] The timing device 202 is a device that measures time, and is realized by a clock. The timing device 202 is capable of transmitting time to the connected second acquisition unit 102. Here, the time measured by the timing device 202 may be absolute time, or may be the time elapsed from a relative starting point. The timing device 202 may be realized in any form as long as it can measure the time between two points in time, that is, the point in time when the subject 11 is detected to be stationary and the point in time when the fatigue level is estimated (i.e., the duration of the stationary posture).

[0049] The pressure sensor 203 is a sensor having a detection surface, and measures the pressure applied to each of the unit detection surfaces that divide the detection surface into one or more unit detection surfaces. The pressure sensor 203 measures the pressure for each unit detection surface in this manner, and outputs the pressure distribution on the detection surface. The pressure sensor 203 is installed so that the subject 11 is positioned on the detection surface.

[0050] For example, the pressure sensor 203 is provided on the seat and backrest of a chair on which the subject 11 sits. Also, for example, the pressure sensor 203 may have a marker on its detection surface, and guide the subject 11 onto the detection surface by displaying a message such as "Please sit on the marker." Also, by guiding the subject 11 onto the detection surface of the pressure sensor 203 provided on a portion of the floor in this manner, the pressure sensor 203 may output the pressure distribution of the subject 11 on the floor. Note that the pressure distribution is used for the purpose of improving the accuracy of estimating the fatigue level, and therefore the fatigue estimation system 200 may be realized without the pressure sensor 203 if sufficient accuracy is ensured.

[0051] The reception device 204 is a user interface that accepts input of personal information of the subject 11, and is realized by an input device such as a touch panel or a keyboard. The personal information includes at least one of age, gender, height, weight, muscle mass, stress level, body fat percentage, exercise proficiency, attendance information, and vital signs. The age of the subject 11 may be a specific numerical value, may be an age band divided into 10-year age groups such as teens, twenties, and thirties, may be two age bands divided by a predetermined age such as 59 years old or younger or 60 years old or older, or may be other.

[0052] The gender of the subject 11 is selected from either male or female, whichever is appropriate for the subject 11. The height and weight are accepted as the subject's 11 height and weight values, respectively. The muscle mass is accepted as the muscle composition ratio of the subject 11 measured using a body composition scale or the like. The stress level of the subject 11 is selected by the subject 11 himself / herself from options such as high, medium, and low as the subjective level of stress felt by the subject 11.

[0053] The body fat percentage of the subject 11 is the ratio of the weight of body fat to the weight of the subject 11, and is expressed as a percentage, for example.

[0054] Furthermore, the exercise proficiency of subject 11 may be quantified by the score when subject 11 performs a predetermined exercise program, or by the status of the exercise that subject 11 usually engages in. In the former, it is quantified by, for example, the time required to perform 10 back exercises, the time required to run 50 meters, the distance of a long throw, etc. In the latter, it is quantified by, for example, how many days a week the subject exercises, how many hours the subject exercises, etc.

[0055] Furthermore, the information regarding the attendance of the subject 11 is used, for example, as the number of consecutive days of attendance since the most recent vacation (if the subject is a student, this can be interpreted as the number of consecutive days of attendance at school or kindergarten). The vital information of the subject 11 is, for example, numerical values ​​such as heart rate, respiratory rate, blood pressure, body temperature, and blood saturated oxygen concentration. Note that, since the reception of personal information (function as a personal information acquisition unit) is used for the purpose of improving the accuracy of fatigue level estimation, the fatigue estimation system 200 may be realized without including the function of the personal information acquisition unit in the reception device 204, provided that sufficient accuracy is ensured.

[0056] The display device 205 is a device for displaying content based on the fatigue level estimation result output by the output unit 109. In the present embodiment, the display device 205 is provided as an example of a presentation device. Another example of the presentation device may include a sound output device that allows the subject to hear content based on the fatigue level estimation result by audio. The presentation device may be realized by any device that can present the estimated fatigue level to the subject. The display device 205 displays an image showing content based on the fatigue level estimation result using a display panel such as a liquid crystal panel or an organic EL (Electro Luminescence) panel. The content displayed by the display device 205 will be described later. Furthermore, when the fatigue estimation system 200 is configured to only reduce the fatigue level of the subject 11 using the recovery device 206, it is sufficient to provide only the recovery device 206, and the display device 205 is not essential.

[0057] The recovery device 206 is a device that reduces the fatigue level of the subject 11 by promoting blood circulation in the subject 11. Specifically, the recovery device 206 actively changes the posture of the seated subject 11 by applying voltage, pressurizing, vibrating, or heating, or by changing the arrangement of various parts of the chair 12 using a mechanism provided in the chair 12. In this way, the recovery device 206 changes the load on at least one of the muscles and joints of the subject 11 and promotes blood circulation. From the perspective of blood flow volume, promoting blood circulation in this way reduces the impact of poor blood flow due to the subject 11 being in a stationary posture, and the subject 11 recovers from fatigue. The recovery device 206 is attached to or brought into contact with an appropriate body part of the subject 11 in advance, depending on the configuration of the device.

[0058] When promoting blood circulation in the subject 11 by heating, the entire space around the subject 11 is heated, and in such a case, it is not necessary to attach or contact an appropriate body part of the subject 11. Furthermore, when the fatigue estimation system 200 is configured only to display the estimated fatigue level to the subject 11, it is sufficient to include only the display device 205, and the recovery device 206 is not essential.

[0059] [Operation] Next, estimation of a fatigue level of a subject 11 using a fatigue estimation system 200 according to an embodiment will be described with reference to Figs. 3A to 5B. Fig. 3A is a flowchart showing a method for estimating a fatigue level according to an embodiment. Fig. 3B is a sub-flowchart showing details of some steps according to the embodiment.

[0060] The fatigue estimation system 200 first acquires personal information of the subject 11 (step S101). The personal information is acquired by the subject 11 himself / herself or an administrator who manages the fatigue level of the subject 11 by inputting it into the reception device 204. The input personal information of the subject 11 is stored in a storage device or the like (not shown), and is read out and used when estimating the fatigue level.

[0061] The fatigue estimation system 200 detects the subject 11 using the imaging device 201 (step S102). The detection of the subject 11 is performed by determining whether or not the subject 11 has entered the angle of view of the camera, which is the imaging device 201. Note that the subject 11 at this time may be a specific subject 11, or may be a person who has entered the angle of view of the camera from an unspecified number of people. When the subject 11 is selected from an unspecified number of people, input of personal information may be omitted. Furthermore, when detecting a specific subject 11, a step of identifying the subject 11 by image recognition or the like is added.

[0062] In this embodiment, an example will be described in which the subject 11 himself / herself inputs personal information, identifies the detection area of ​​the image capture device 201, and enters the detection area, thereby estimating the fatigue level. Therefore, image recognition or the like is not required, and the fatigue level is estimated taking the personal information into consideration.

[0063] If the fatigue estimation system 200 determines that the subject 11 has not been detected (No in step S102), it repeats step S102 until the subject 11 is detected. On the other hand, if the subject 11 is detected (Yes in step S102), the first acquisition unit 101 acquires an image output by the imaging device 201 (step S103, an example of an acquisition step). Here, if it is detected in the acquired image that the subject 11 is stationary (in a stationary posture) (step S104), the estimation device 100 estimates the posture of the subject 11. Specifically, first, the third acquisition unit 103 acquires a pressure distribution applied to the detection surface from the pressure sensor 203 (step S105).

[0064] The posture estimation unit 105 estimates the posture of the subject 11 based on the acquired image and pressure distribution (posture estimation step S106). For example, if biased pressure is applied, the pressure distribution is used to correct the estimated posture so that the bias is corrected. Next, the first calculation unit 106 calculates the load amount on each muscle and / or joint of the subject 11 from the posture estimation result. At this time, the load amount is corrected and calculated using personal information acquired in advance (step S107). Note that the estimation of the posture of the subject 11 has been explained using FIG. 1B, and the calculation of the load amount has been explained using FIG. 1C, so a detailed explanation will be omitted.

[0065] When correcting the load using personal information, for example, the load is decreased as the age of subject 11 approaches the peak age of muscle development, and increased as the age of subject 11 moves away from the peak age. Such peak values ​​may be based on the sex of subject 11. Furthermore, the load may be decreased if subject 11 is male, and increased if subject 11 is female. Furthermore, the load may be decreased as the height and weight of subject 11 decrease, and increased as the height and weight increase.

[0066] Furthermore, the load may be decreased as the muscle mass composition ratio of subject 11 increases, and increased as the muscle mass composition ratio decreases. Furthermore, the load may be decreased as the stress level of subject 11 decreases, and increased as the stress level increases. Furthermore, the load may be increased as the body fat percentage of subject 11 increases, and decreased as the body fat percentage decreases. Furthermore, the load may be decreased as subject 11 becomes more familiar with exercise, and increased as subject 11 becomes less familiar with exercise.

[0067] The load may be increased as the number of consecutive working days of the subject 11 increases. The load may be increased as the vital information of the subject 11 deviates from the median of the reference values.

[0068] Here, the duration of the stationary posture of the subject 11 is measured based on the time acquired by the second acquisition unit 102 (step S108). The fatigue estimation unit 108 adds the load calculated above every time the duration elapses by a unit time, and estimates the fatigue level of the subject 11 at this point in time (fatigue estimation step S109). The processes of step S108 and fatigue estimation step S109 are continued until the stationary state of the subject 11 is released. Specifically, whether the stationary state has been released is determined based on whether the posture estimated by the posture estimation unit 105 has changed from a certain stationary posture (step S110).

[0069] Here, the fatigue estimation step S109 will be described in detail with reference to FIG. 3B. When the fatigue estimation step S109 is started, first, the fatigue estimation unit 108 determines whether or not a subjective fatigue level has been acquired (S109a). When the fatigue estimation unit 108 determines that a subjective fatigue level has been acquired (Yes in S109a), the fatigue estimation unit 108 refers to the parameter log and acquires parameters calculated in the past (S109b). Then, the fatigue estimation unit 108 calculates new parameters based on the acquired subjective fatigue level and the parameters calculated in the past (S109c). Furthermore, the fatigue estimation unit 108 inputs personal information into a candidate formula selection model to output a candidate formula (S109d). The fatigue estimation unit 108 applies the newly calculated parameters to the output candidate formula to determine a new estimation formula (S109e). The fatigue estimation unit 108 estimates the fatigue level of the subject 11 using the newly determined estimation formula (S109f).

[0070] On the other hand, if the fatigue estimation unit 108 determines that the subjective fatigue level has not been acquired (No in S109a), it estimates the fatigue level of the subject 11 (S109f) without determining a new estimation formula (skipping S109b to S109e).

[0071] Returning to FIG. 3A, if it is not determined that the stationary state has been released (No in step S110), the process returns to step S108, measures the duration, and proceeds to fatigue estimation step S109, where the load is added to accumulate the fatigue level of subject 11 as long as the stationary posture continues. That is, by repeating step S108 and fatigue estimation step S109, fatigue estimation unit 108 estimates the fatigue level of subject 11 using an increasing function of fatigue level (corresponding to the above estimation formula) having a slope corresponding to the calculated load level (interpreted as the slope of the tangent if the function is a curve function) with respect to the duration. Therefore, the greater the calculated load level, the greater the increase in subject 11's fatigue level per unit time. Note that in this accumulation of fatigue level, the fatigue level of subject 11 is initialized (set to fatigue level 0) at the start timing of the stationary posture, which is the starting point.

[0072] On the other hand, if it is determined that the stationary state has been released (Yes in step S110), the posture estimation unit 105 calculates the amount of change in posture from the original stationary posture to the changed current posture. The amount of change in posture is calculated for each muscle and / or joint, similar to the load amount described above. When the posture changes in this way, the load on at least one of the muscles and joints changes, and in terms of blood flow, the worsened blood flow is temporarily alleviated, and the fatigue level of the subject 11 begins to recover. The fatigue level reduced by the recovery is related to the amount of change in posture. Accordingly, the second calculation unit 107 calculates the amount of recovery, which is the degree of recovery of the fatigue level, based on the amount of change in posture (step S111).

[0073] Based on the time acquired by the second acquisition unit 102, a change time, which is the time during which the change in posture of the subject 11 continues, is measured (step S112). The relationship between the recovery amount and the change time is the same as the relationship between the load amount and the duration, and the recovery amount of the subject 11 is accumulated as long as the posture change continues. In other words, at the timing when the posture of the subject 11 changes in this way, the fatigue estimation unit 108 estimates the fatigue level of the subject 11 by subtracting the recovery amount every time a unit time elapses (step S113). Note that, in this fatigue level recovery, as in the fatigue estimation step S109, the fatigue level may also be estimated by determining a new estimation formula based on the subjective fatigue level and personal information.

[0074] The processes of steps S111, S112, and S113 are continued until the posture of the subject 11 becomes stationary. Specifically, it is determined whether or not the posture estimated by the posture estimation unit 105 is a certain stationary posture (step S114). If the subject 11 is not detected to be stationary (No in step S114), the process returns to step S111, calculates the recovery amount, proceeds to step S112, measures the change time, and proceeds to step S113, where the recovery amount is subtracted, thereby accumulating the fatigue level of the subject 11 so that it recovers as long as the posture change continues.

[0075] That is, the fatigue estimation unit 108 repeats steps S111, S112, and S113 to estimate the fatigue level of the subject 11 using a fatigue level reduction function (corresponding to the above estimation formula) having a slope corresponding to the calculated recovery amount with respect to the change time (interpreted as the slope of the tangent if the function is a curve function). The recovery amount of the fatigue level depends on the amount of change in posture, so the greater the amount of change in posture, the greater the decrease in the fatigue level of the subject 11 per unit time.

[0076] On the other hand, if it is detected that the subject 11 is still (Yes in step S114), the process returns to step S105, and the posture and fatigue level are estimated again for the new still posture. In this way, the fatigue estimation system 200 calculates the fatigue level of the subject 11 based on the image and taking into account the duration of the still posture, so that the fatigue level of the subject 11 can be estimated with less strain on the subject 11 and with higher accuracy.

[0077] The above will be described in more detail with reference to Figures 4A to 5B. Figure 4A is a diagram showing a subject standing still in posture A. Figure 4B is a diagram showing a subject standing still in posture B.

[0078] Similar to the subject 11 shown in FIG. 1A, the subject 11 shown in FIG. 4A and FIG. 4B is in a static sitting position in a chair 12. Although a table, PC, etc. (not shown) are actually present in FIG. 4A and FIG. 4B, only the subject 11 and the chair 12 are shown here. The static posture of the subject 11 shown in FIG. 4A is posture A, which places a relatively heavy load on the shoulders. On the other hand, the static posture of the subject 11 shown in FIG. 4B is posture B, which places a relatively light load on the shoulders.

[0079] The fatigue level estimated for subject 11 standing still in posture A or posture B accumulates as shown in Figures 5A and 5B. Figure 5A is a first diagram illustrating the estimated accumulation of the subject's fatigue level according to the embodiment. Figure 5B is a second diagram illustrating the estimated accumulation of the subject's fatigue level according to the embodiment.

[0080] As shown in Figure 5A, when subject 11 remains stationary in posture A shown in Figure 4A or posture B shown in Figure 4B, the fatigue level of subject 11 is expressed by a linear function whose slope is the amount of load calculated from the posture.

[0081] As described above, posture A is a posture that imposes a greater load than posture B. Therefore, for example, in a certain muscle of subject 11 (here, a muscle related to shoulder movement), the load amount in posture A (the slope of the straight line in posture A) is greater than the load amount in posture B (the slope of the straight line in posture B). For this reason, subject 11 accumulates (accumulates) a greater degree of fatigue in a shorter period of time in posture A compared to when subject 11 is stationary in posture B.

[0082] On the other hand, as shown in Figure 5B, when the posture of subject 11 changes from posture A shown in Figure 4A to posture B shown in Figure 4B, the fatigue level of subject 11 is expressed by a function that combines a linear function whose slope is the amount of load calculated from the posture and a linear function whose slope is the amount of change in posture.

[0083] Therefore, for example, while subject 11 remains stationary in posture A, the fatigue level of a certain muscle of subject 11 is estimated as an accumulation (addition) of fatigue level using an increasing function with a positive slope corresponding to the load of posture A, as in FIG. 5A , and the accumulation (addition) turns to recovery (decrease) at the change point when subject 11 begins to change posture. Subject 11's fatigue level recovers (decreases) by an amount shown as the change width in the figure during the period when the posture change continues, shown as the change time in the figure, using a decreasing function with a negative slope corresponding to the amount of posture change. After the change point when subject 11 remains stationary again in posture B, subject 11's fatigue level is estimated as an accumulation (addition) of fatigue level using an increasing function with a positive slope corresponding to the load of posture B.

[0084] In this way, the fatigue estimation system 200 in this embodiment estimates the fatigue level of the subject 11 that reflects accumulation and recovery in accordance with the subject 11's stillness and change in posture.

[0085] Next, an example of the output of output unit 109 based on the estimated fatigue level will be described. Fig. 6 is a first diagram showing an example of a display of the estimation result according to the embodiment. Fig. 7 is a second diagram showing an example of a display of the estimation result according to the embodiment.

[0086] As shown in FIGS. 6 and 7, the fatigue estimation system 200 can display and feed back the estimation result of the fatigue level of the subject 11 using a display device 205. Specifically, as shown in FIG. 6, by visualizing the fatigue level of the subject 11, it is possible to visually grasp how tired the subject 11 is. In the figure, a doll resembling the subject 11 and the fatigue levels of the subject 11's shoulders, back, and essential parts are displayed together on the display device 205. To make it easier for the subject 11 to intuitively grasp the fatigue level, the fatigue level of the shoulders is displayed as a "stiff shoulder level," the fatigue level of the back is displayed as a "back pain level," and the fatigue level of the lower back is displayed as a "lower back pain level."

[0087] Here, in the display in the figure, fatigue levels of three parts of the subject 11 are displayed all at once, but the fatigue levels of these three parts are estimated from images captured at the same time. That is, the estimation device 100 estimates fatigue levels for the muscles and / or joints in each of multiple body parts including a first part (e.g., shoulders), a second part (e.g., back), and a third part (e.g., waist) of the subject 11 from one posture of the subject 11. Therefore, even if the posture of the subject 11 is constant, the fatigue levels accumulated in the muscles and / or joints for each body part will differ, but the fatigue estimation system 200 can simultaneously and individually estimate such different fatigue levels.

[0088] 1C, in this embodiment, the load is calculated for each muscle and / or joint of subject 11, and therefore, if there are no limitations on processing resources, it is possible to estimate the fatigue level of each muscle and / or joint. Therefore, there is no limit to the number of body parts whose fatigue levels are estimated from images captured at one time, and it may be one, two, four or more.

[0089] The estimation device 100 calculates the load amount for each of multiple body parts, and for one posture of the subject 11, it can estimate the fatigue level of a first part (the above-mentioned shoulder stiffness level) based on the load amount calculated for the first part, the fatigue level of a second part (the above-mentioned back pain level) based on the load amount calculated for the second part, and the fatigue level of a third part (the above-mentioned lower back pain level) based on the load amount calculated for the third part.

[0090] In this way, since the fatigue level of each body part of the subject 11 can be estimated, the fourth acquisition unit 104 acquires, as the subjective fatigue level, input information including the subjective fatigue level of each body part of the subject 11. In other words, applying this to the above example, the subjective fatigue level includes the fatigue level of a first body part of the subject 11, the fatigue level of a second body part different from the first body part, and the fatigue level of a third body part different from the first body part and the second body part.

[0091] The fatigue estimation unit 108 then calculates a first parameter for determining a first estimation formula, which is an estimation formula for the first part, from the fatigue level of the first part included in the received subjective fatigue level, and estimates the fatigue level of the first part using the first estimation formula to which the calculated first parameter is applied. Similarly, the fatigue estimation unit 108 calculates a second parameter for determining a second estimation formula, which is an estimation formula for the second part, from the fatigue level of the second part included in the received subjective fatigue level, and estimates the fatigue level of the second part using the second estimation formula to which the calculated second parameter is applied. Similarly, the fatigue estimation unit 108 calculates a third parameter for determining a third estimation formula, which is an estimation formula for the third part, from the fatigue level of the third part included in the received subjective fatigue level, and estimates the fatigue level of the third part using the third estimation formula to which the calculated third parameter is applied.

[0092] In the example shown in the figure, the degree of stiff shoulders is estimated from the load on the trapezius muscle, the degree of back pain is estimated from the fatigue level of the latissimus dorsi muscle, and the degree of lower back pain is estimated from the load on the lumbar paraspinal muscles. In this way, a single fatigue level may be estimated from the load on a single muscle and / or joint, but a single fatigue level may also be estimated from the combined load on multiple muscles and / or joints. For example, the degree of stiff shoulders (i.e., a single fatigue level in the shoulder region) may be estimated from the average value of the loads on the trapezius muscle, levator scapulae muscle, rhomboid muscle, and deltoid muscle. Furthermore, in estimating the fatigue level, a more realistic estimate of the fatigue level may be made by weighting the load on the muscle and / or joint that has a particularly large impact on the fatigue level of that body part, rather than simply using an average value.

[0093] The fatigue levels estimated in this way may be shown as relative positions on a reference meter with a minimum value of 0 and a maximum value of 100, as shown in the figure. Here, a reference value is set at a predetermined position on the reference meter. Such a reference value is set to a relative position (or around that position, etc.) of the fatigue level that may cause subjective symptoms such as pain in a typical subject 11, which has been quantified in advance through an epidemiological survey or the like. Therefore, different reference values ​​may be set depending on the fatigue level of each body part.

[0094] Furthermore, when the estimated fatigue level of the subject 11 reaches a reference value, the display device 205 may display a warning to the subject 11 as an estimation result. The reference value here is an example of a first threshold value. In the figure, an example of such a warning is displayed at the bottom of the display device 205 as "Your shoulder stiffness level has exceeded the reference value." In addition, in connection with such a warning, the display device 205 may also display a specific method of dealing with the situation, such as "We recommend you take a break," as also shown in the figure.

[0095] 7, when the estimated fatigue level of the subject 11 reaches a reference value, the display device 205 may display to the subject 11 a recommended posture that places less strain on the body part that has reached the reference value than the currently estimated posture of the subject 11. The reference value here is an example of a second threshold value, and may be the same as or different from the first threshold value. The recommended posture that is displayed may be accompanied by specific cautionary points such as "lean your weight on the back of the chair" and "sit deep in the seat" along with a doll assuming that posture.

[0096] In addition to the configuration described above in which the estimation result is displayed to the subject 11 to encourage the subject 11 to deal with the accumulated fatigue level, a configuration in which the fatigue estimation system 200 actively recovers the fatigue level of the subject 11 is also conceivable. Specifically, the fatigue level of the subject 11 is recovered by the operation of the recovery device 206 shown in FIG. 2A. The specific configuration of the recovery device 206 is as described above and will not be described again. However, when the estimated fatigue level of the subject 11 reaches a reference value, the recovery device 206 operates to change the load on at least one of the muscles and joints of the subject 11 and promote blood circulation, thereby reducing the subject's fatigue level. The reference value here is an example of a third threshold value and may be the same as or different from either the first threshold value or the second threshold value.

[0097] [Effects, etc.] As described above, the fatigue estimation system 200 in this embodiment includes an imaging device 201 (information output device) that outputs information regarding the positions of body parts of the subject 11, a posture estimation unit 105 that estimates the posture of the subject 11 based on the information output by the information output device, a fourth acquisition unit 104 (subjective acquisition unit) that acquires the subjective fatigue level felt by the subject 11, a fatigue estimation unit 108 that uses the acquired subjective fatigue level as the starting fatigue level and estimates the fatigue level of the subject 11 based on the duration of the posture estimated by the posture estimation unit 105, calculates parameters for determining an estimation formula from the acquired subjective fatigue level, and estimates the fatigue level of the subject 11 using the estimation formula to which the calculated parameters are applied, and a display device 205 (presentation device) that presents the estimated fatigue level of the subject 11.

[0098] Such a fatigue estimation system 200 can achieve the same effects as the estimation device 100 described below.

[0099] Further, for example, the device may include an information output device (e.g., an imaging device 201) that outputs information regarding the positions of body parts of the subject 11, and an estimation device 100 that estimates the posture of the subject 11 based on the information (e.g., an image) output by the information output device, and estimates the fatigue level of the subject 11 based on the estimated posture and the duration of the posture.

[0100] Also, for example, the information output device may be an imaging device 201 that captures an image of the subject 11 and outputs the image as information regarding the position of body parts, and the estimation device 100 may estimate the posture of the subject 11 based on the image output by the imaging device 201.

[0101] Such a fatigue estimation system 200 can estimate the fatigue level of the subject 11 using an image output by the imaging device 201. The posture of the subject 11 estimated from the output image is used to estimate the fatigue level of the subject 11. Specifically, the fatigue level is quantified based on the duration of time that the subject 11 has remained in a stationary posture, and includes the amount of strain on the muscles, the amount of strain on the joints, and the accumulation of fatigue due to deterioration of blood flow caused by maintaining a certain stationary posture. In this way, the fatigue estimation system 200 calculates the fatigue level of the subject 11 based on the image and taking into account the duration of time that the subject 11 has remained in a stationary posture, thereby making it possible to estimate the fatigue level of the subject 11 in a stationary posture with less strain on the subject 11 and with higher accuracy.

[0102] Furthermore, for example, the estimation device 100 may use the musculoskeletal model 11c to calculate the amount of load on at least one of the muscles and joints of the subject 11 used to maintain the estimated posture, and estimate the degree of fatigue using an increasing function of the degree of fatigue with respect to the duration, such that the increasing function used to estimate the degree of fatigue may increase the degree of fatigue per unit time as the calculated amount of load increases.

[0103] According to this, the load amount for at least one of each muscle and each joint is calculated using the musculoskeletal model 11c. The fatigue level of the subject 11 can be easily estimated by an increasing function with the slope of the load amount calculated in this way. Therefore, the fatigue level of the subject 11 can be easily estimated with higher accuracy.

[0104] Furthermore, for example, the estimation device 100 may calculate the load on at least one of the muscles and joints in each of two or more body parts of the subject 11, including a first part and a second part, and, in one posture of the subject 11, estimate at least a first fatigue level of the first part based on the load calculated in the first part and a second fatigue level of the second part based on the load calculated in the second part.

[0105] This allows the fatigue level of two or more body parts of the subject 11 to be calculated with a single image capture. There is no need to perform measurements or the like for estimating the fatigue level for each body part, and fatigue levels can be estimated quickly and approximately simultaneously for multiple body parts. Furthermore, fatigue levels estimated approximately simultaneously can easily identify body parts of the subject 11 that are prone to fatigue, which is effective when taking measures to recover from fatigue. Therefore, a quick and effective estimation of the fatigue level of the subject 11 can be performed.

[0106] Furthermore, for example, when posture is changed, the estimation device 100 may estimate the fatigue level using a decrease function of the fatigue level over time, and the decrease function used to estimate the fatigue level may be such that the greater the change in posture, the greater the decrease in fatigue level per unit time.

[0107] This allows the estimated fatigue level to reflect the change in the load on at least one of the muscles and joints and the recovery from fatigue due to improved blood flow as a result of changes in the posture of the subject 11. Therefore, it is possible to more accurately estimate the fatigue level based on the still posture of the subject 11 and the position of the subject's body parts.

[0108] Furthermore, for example, the fatigue estimation system 200 may further include a display device 205 that displays a warning to the subject 11 as an estimation result when the fatigue level of the subject 11 estimated by the estimation device 100 reaches a first threshold value.

[0109] According to this, the subject 11 or the like can know that the fatigue level of the subject 11 has reached the first threshold value by the warning displayed on the display device 205. By dealing with the accumulated fatigue level in accordance with the displayed warning, the subject 11 can reduce the possibility of fatigue-related illness, such as poor physical condition, injury, or accident. Therefore, the fatigue level estimated with higher accuracy is used to reduce illness caused by fatigue in the subject 11.

[0110] Furthermore, for example, the fatigue estimation system 200 may further include a display device 205 that displays to the subject 11 a recommended posture that places less strain on the subject 11 than the posture when the fatigue level of the subject 11 estimated by the estimation device 100 reaches a second threshold.

[0111] This allows the subject 11, etc. to deal with the subject 11's fatigue level that has reached the second threshold by using the recommended posture displayed on the display device 205. By changing to the recommended posture, the subject 11's fatigue level is expected to recover, so the subject 11 can suppress the accumulation of fatigue without being particularly conscious of it. Therefore, using the fatigue level estimated with higher accuracy, the subject 11's discomfort caused by fatigue is suppressed.

[0112] Furthermore, for example, the fatigue estimation system 200 may further include a recovery device 206 that reduces the fatigue level of the subject 11 by promoting blood circulation in the subject 11 when the fatigue level of the subject 11 estimated by the estimation device 100 reaches a third threshold.

[0113] According to this, since the recovery device 206 is expected to recover the fatigue level of the subject 11, the subject 11 can suppress the accumulation of fatigue without being particularly conscious of it. Therefore, using the fatigue level estimated with higher accuracy, the subject 11's discomfort caused by fatigue can be suppressed.

[0114] Furthermore, for example, the fatigue estimation system 200 may further include a pressure sensor 203 that outputs a pressure distribution indicating the distribution of pressure applied on the detection surface, and the estimation device 100 may correct the estimated posture of the subject 11 based on the pressure distribution output by the pressure sensor 203 and calculate the amount of load required to maintain the corrected posture.

[0115] According to this, the pressure distribution output by the pressure sensor 203 can be used to estimate the posture of the subject 11. Therefore, the posture of the subject 11 can be estimated with high accuracy by correction using the pressure distribution. Therefore, the fatigue level of the subject 11 can be estimated with higher accuracy.

[0116] Furthermore, for example, the fatigue estimation system 200 may further include a reception device 204 that receives input of personal information including at least one of the subject's 11 age, sex, height, weight, muscle mass, stress level, body fat percentage, and exercise proficiency, and the estimation device 100 may correct the load amount based on the personal information received as input by the reception device 204 when calculating the load amount required to maintain the estimated posture.

[0117] According to this, the personal information accepted by the accepting device 204 can be used to calculate the load amount. Therefore, the load amount in the stationary posture can be calculated with high accuracy by correction with the personal information. Therefore, the fatigue level of the subject 11 can be estimated with high accuracy.

[0118] In addition, the estimation device 100 (fatigue estimation device) in this embodiment includes a first acquisition unit 101 (position acquisition unit) that acquires information regarding the position of the body parts of the subject 11, a posture estimation unit 105 that estimates the posture of the subject 11 based on the information acquired by the position acquisition unit, a fourth acquisition unit 104 (subjective acquisition unit) that acquires the subjective fatigue level felt by the subject 11, and a fatigue estimation unit 108 that uses the acquired subjective fatigue level as the starting fatigue level and estimates the fatigue level of the subject 11 based on the duration of the posture estimated by the posture estimation unit 105, calculates parameters for determining an estimation formula from the acquired subjective fatigue level, and estimates the fatigue level of the subject 11 using the estimation formula to which the calculated parameters are applied.

[0119] Such an estimation device 100 can acquire a subjective fatigue level and apply parameters calculated based on the acquired subjective fatigue level to an estimation formula used to estimate the fatigue level. The determined estimation formula applies parameters that bring the estimation result closer to the subjective fatigue level, making it possible to estimate and output an estimation result that is closer to the fatigue level felt by the subject 11. Therefore, it is possible to estimate the fatigue level with higher accuracy from the perspective of being closer to the fatigue level felt by the subject 11.

[0120] Furthermore, for example, the fatigue estimation unit 108 may accumulate a parameter log including parameters calculated in the past to determine an estimation formula from a previously acquired subjective fatigue level, and calculate parameters for determining an estimation formula from the accumulated parameter log and a newly acquired subjective fatigue level.

[0121] This allows new parameters to be calculated taking into account previously calculated parameters. The degree of fatigue indicated by the subjective fatigue level is likely to experience transient increases and decreases, so it is effective to take into account previously calculated parameters in order to reduce the influence of such transient increases and decreases and to reflect the subject 11's unique tendency to become fatigued in the estimation formula.

[0122] Furthermore, for example, the fatigue estimation unit 108 may calculate a parameter for determining an estimation formula from a newly acquired subjective fatigue level as an average value of previously calculated parameters included in the parameter log.

[0123] This allows the average value of previously calculated parameters, that is, the average value of previously calculated parameters and a tentative parameter calculated from a newly acquired subjective fatigue level, to be calculated as a new parameter.

[0124] Furthermore, for example, the fatigue estimation unit 108 may calculate parameters for determining an estimation formula from a newly acquired subjective fatigue level by multiplying each of previously calculated parameters included in the parameter log by a weighting coefficient such that the weight is greater the shorter the elapsed time, and then adding the results.

[0125] This allows a new parameter to be calculated by multiplying each previously calculated parameter by a weighting coefficient so that the shorter the elapsed time, the greater the weight. In other words, the weight of parameters calculated a longer time ago can be reduced, and the weight of parameters calculated more recently can be increased. Since the subjective tendency of subject 11, i.e., the tendency to become fatigued, changes from moment to moment, it is possible to take previously calculated parameters into consideration while reducing the influence of parameters calculated a longer time ago on the tendency, and to reflect the tendency to become fatigued relatively recently in the estimation formula.

[0126] Furthermore, for example, the fourth acquisition unit 104 may further have another function (function of the personal information acquisition unit) of acquiring personal information including at least one of the subject 11's age, sex, height, weight, muscle mass, stress level, body fat percentage, exercise proficiency, attendance information, and vital information, and the estimation formula may be determined by applying parameters calculated in the fatigue estimation unit 108 to a candidate formula output by inputting the acquired personal information of the subject 11 into a candidate formula selection model.

[0127] According to this, it is possible to select candidate estimation formulas from the personal information of the subject 11. The personal information includes information closely related to the tendency of fatigue susceptibility. By inputting the personal information as a candidate formula selection model into a trained model that has undergone machine learning or the like to determine the correlation between the personal information and the tendency of fatigue susceptibility (here, as a candidate formula for the estimation formula), it becomes possible to select candidate formulas for determining an appropriate estimation formula based on the personal information.

[0128] Furthermore, for example, the device may further include an instruction unit 110 that instructs the subject 11 to input a subjective fatigue level in a first cycle, and the subjective fatigue level acquisition unit may acquire the subjective fatigue level in a second cycle within the first cycle.

[0129] This allows the subject 11 to be prompted to input their subjective fatigue level in the first cycle in order to estimate the minimum appropriate level of fatigue guaranteed by the estimation device, and further allows for the acceptance and acquisition of high-frequency input of subjective fatigue levels, which can enable a more highly appropriate estimation of fatigue levels.

[0130] Furthermore, for example, the subjective fatigue level may include the fatigue level of a first body part of the subject 11 and the fatigue level of a second body part different from the first body part, and the fatigue estimation unit 108 may calculate a first parameter for determining a first estimation formula, which is an estimation formula for the first body part, from the fatigue level of the first body part included in the acquired subjective fatigue level, estimate the fatigue level of the first body part using the first estimation formula to which the calculated first parameter is applied, calculate a second parameter for determining a second estimation formula, which is an estimation formula for the second body part, from the fatigue level of a second body part included in the acquired subjective fatigue level, and estimate the fatigue level of the second body part using the second estimation formula to which the calculated second parameter is applied.

[0131] According to this, a subjective fatigue level is acquired, and a first parameter calculated based on the fatigue level of the first region can be applied to a first estimation formula used to estimate the fatigue level of the first region. The determined first estimation formula applies a first parameter that makes the estimation result closer to the subjective fatigue level of the first region, making it possible to estimate and output an estimation result that is closer to the fatigue level felt by subject 11 in the first region. At the same time, a second parameter calculated based on the fatigue level of the second region can be applied to a second estimation formula used to estimate the fatigue level of the second region. The determined second estimation formula applies a second parameter that makes the estimation result closer to the subjective fatigue level of the second region, making it possible to estimate and output an estimation result that is closer to the fatigue level felt by subject 11 in the second region. In other words, it becomes possible to estimate the fatigue level of two or more different regions of subject 11 (including the first region and the second region) with higher accuracy and in a region-specific manner, respectively, according to the subject's tendency to become fatigued.

[0132] Further, for example, the device may include a first acquisition unit 101 that acquires information regarding the positions of body parts of the subject 11, a posture estimation unit 105 that estimates the posture of the subject 11 based on the information acquired by the first acquisition unit 101, and a fatigue estimation unit 108 that estimates the degree of fatigue based on the duration of the posture estimated by the posture estimation unit 105.

[0133] Such an estimation device 100 can estimate the fatigue level of the subject 11 using information such as acquired images. The fatigue level of the subject 11 is estimated using the posture of the subject 11 estimated from the acquired images. Specifically, the fatigue level is quantified based on the duration of time that the subject 11 remains in a stationary posture, and includes the load on at least one of the muscles and joints due to maintaining a certain stationary posture, as well as the accumulated fatigue due to the deterioration of blood flow. In this way, the estimation device 100 calculates the fatigue level of the subject 11 taking into account the duration of time that the subject 11 remains in a stationary posture, thereby making it possible to estimate the fatigue level of the subject 11 in a stationary posture with higher accuracy.

[0134] In addition, the fatigue estimation method in this embodiment acquires information regarding the position of the body parts of subject 11, estimates the posture of subject 11 based on the acquired information, acquires the subjective fatigue level felt by subject 11, calculates parameters for determining an estimation formula from the acquired subjective fatigue level, uses the acquired subjective fatigue level as the starting point of fatigue level, and estimates the fatigue level of subject 11 using an estimation formula to which the calculated parameters are applied based on the duration of the estimated posture.

[0135] It may also include, for example, an acquisition step (such as step S103) for acquiring information regarding the positions of body parts of the subject 11, a posture estimation step S106 for estimating the posture of the subject 11 based on the information acquired in the acquisition step, and a fatigue estimation step S109 for estimating the degree of fatigue based on the duration of the posture estimated in the posture estimation step S106.

[0136] Such a fatigue estimation method provides the same effects as the above-described estimation device 100.

[0137] (Other embodiments) Although the embodiments have been described above, the present disclosure is not limited to the above-described embodiments.

[0138] For example, in the above embodiment, a process executed by a specific processing unit may be executed by another processing unit, the order of multiple processes may be changed, or multiple processes may be executed in parallel.

[0139] Furthermore, the fatigue estimation system or estimation device of the present disclosure may be realized by multiple devices each having some of the multiple components, or by a single device having all of the multiple components. Furthermore, some of the functions of a component may be realized as the functions of another component, and the functions may be distributed in any manner among the components. Any configuration that includes all of the functions that can substantially realize the fatigue estimation system or estimation device of the present disclosure is included in the present disclosure.

[0140] In the above-described embodiments, each component may be realized by executing a software program suitable for that component, or by a program execution unit such as a CPU or processor reading and executing a software program recorded on a recording medium such as a hard disk or semiconductor memory.

[0141] Furthermore, each component may be realized by hardware. For example, each component may be a circuit (or integrated circuit). These circuits may form a single circuit as a whole, or each may be a separate circuit. Furthermore, each of these circuits may be a general-purpose circuit or a dedicated circuit.

[0142] Furthermore, the general or specific aspects of the present disclosure may be realized as a system, an apparatus, a method, an integrated circuit, a computer program, or a computer-readable recording medium such as a CD-ROM, or may be realized as any combination of a system, an apparatus, a method, an integrated circuit, a computer program, and a recording medium.

[0143] In addition, in the above embodiment, the posture of the subject is estimated from an image using a rigid link model generated by image recognition, the load amount is calculated from the estimated posture of the subject, and the fatigue level of the subject is estimated based on the load amount and duration, but the method of estimating the fatigue level is not limited to this. Any existing method may be used as a method of estimating the posture of the subject from an image, and any existing method may be used as a method of estimating the load amount from the posture of the subject.

[0144] Furthermore, as a method for estimating the posture of a subject, the present disclosure can also be realized by a configuration using a position sensor, in addition to a configuration using an imaging device. A specific description will be given using FIG. 8. FIG. 8 is a diagram illustrating posture estimation according to a modified example of the embodiment. As shown in FIG. 8, in this modified example, the posture of the subject 11 is estimated using a sensor module 207 including a position sensor 207a and a potential sensor 207b. Here, multiple sensor modules 207 are attached to the subject 11, but there is no particular limitation on the number of sensor modules 207 attached to the subject 11. Only one sensor module 207 may be attached to the subject 11.

[0145] There are also no particular limitations on the manner in which the sensor module 207 is worn, and any manner may be used as long as it is possible to measure the position of a predetermined body part of the subject 11. As an example, in Fig. 8, the subject 11 wears an outfit to which a plurality of sensor modules 207 are attached, and the plurality of sensor modules 207 are attached to the subject 11.

[0146] The sensor module 207 is a device that is attached to a predetermined body part of the subject 11 and outputs information indicating the results of detection or measurement in conjunction with the predetermined body part. Specifically, the sensor module 207 has a position sensor 207a that outputs position information related to the spatial position of the predetermined body part of the subject 11, and an electric potential sensor 207b that outputs electric potential information indicating the electric potential at the predetermined body part of the subject 11. Although the figure shows the sensor module 207 having both the position sensor 207a and the electric potential sensor 207b, the electric potential sensor 207b is not essential as long as the sensor module 207 has the position sensor 207a.

[0147] The position sensor 207a in the sensor module 207 is an example of an information output device that outputs position information as information relating to the position of a body part of the subject 11. Therefore, the output information is position information, and is information including the relative or absolute position of a predetermined body part of the subject 11. The output information may also include, for example, potential information. The potential information is information including the value of the potential measured at a predetermined body part of the subject 11. The position information and potential information will be described in detail below together with the position sensor 207a and the potential sensor 207b.

[0148] The position sensor 207a is a detector that detects the relative or absolute spatial position of a predetermined body part of the subject 11 wearing the sensor module 207, and outputs information related to the spatial position of the predetermined body part as a result of the detection. The information related to the spatial position includes information that can identify the position of the body part in space as described above, and information that can identify a change in the position of the body part due to body movement. Specifically, the information related to the spatial position includes information indicating the positions of the joints and skeleton in space and the change in the positions.

[0149] The position sensor 207a is configured by combining various sensors such as an acceleration sensor, an angular velocity sensor, a geomagnetic sensor, a distance measurement sensor, etc. The position information output by the position sensor 207a can approximate the spatial position of a predetermined body part of the subject 11, and therefore the posture of the subject 11 can be estimated from the spatial position of the predetermined body part.

[0150] The potential sensor 207b is a detector that measures the potential at a predetermined body part of the subject 11 wearing the sensor module 207, and outputs information indicating the measurement result, which indicates the potential of the predetermined body part. The potential sensor 207b is a measuring instrument that has multiple electrodes and measures the potential generated between the multiple electrodes using an electrometer. The potential information output by the potential sensor indicates the potential generated at the predetermined body part of the subject 11, and since the potential corresponds to the action potential of the muscle in the predetermined body part, it is possible to improve the accuracy of estimating the posture of the subject 11 from the action potential of the predetermined body part.

[0151] The fatigue estimation system in this modification estimates the fatigue level of the subject 11 using the posture of the subject 11 estimated as described above. Note that the processing after estimating the posture of the subject 11 is the same as in the above embodiment, and therefore description thereof will be omitted.

[0152] As described above, in the fatigue estimation system of this modified example, the information output device is a position sensor 207a that is attached to a specific body part of the subject 11 and outputs position information regarding the spatial position of the specific body part as information regarding the position of the body part of the subject 11, and the estimation device 100 estimates the posture of the subject 11 based on the position information output by the position sensor 207a.

[0153] According to this, the fatigue level of the subject 11 can be estimated using the position information output by the position sensor 207a. The posture of the subject 11 estimated from the output information is used to estimate the fatigue level of the subject 11. Specifically, the fatigue level is quantified based on the duration of time that the subject 11 remains in a stationary posture, and the accumulated fatigue caused by maintaining a certain stationary posture is quantified as the fatigue level. In this way, the fatigue estimation system calculates the fatigue level of the subject 11 based on the results of detection and measurement by the sensor module 207, taking into account the duration of time in a stationary posture, so that the fatigue level of the subject 11 in a stationary posture can be estimated with less strain on the subject 11 and with higher accuracy.

[0154] In the above embodiment, the increasing function and the decreasing function are described as linear functions, but this is not limiting. The increasing function may be a curved function as long as the fatigue level increases over time. The decreasing function may be a curved function as long as the fatigue level decreases over time.

[0155] Furthermore, the above-described estimation device has been described as estimating the fatigue level of a subject using estimated values ​​of muscle load, joint load, and blood flow rate estimated from the posture of the subject, but it is also possible to achieve more accurate estimation of fatigue level by correcting the estimated values ​​with values ​​measured using a measurement device. Specifically, the estimation device acquires measurement values ​​that correspond to the estimated values ​​and are based on the measurement results of measuring the subject using a measurement device.

[0156] The detection device may be, for example, an electromyograph, a muscle hardness meter, a pressure meter, or a near-infrared spectrometer, and may obtain measurements related to muscle load, joint load, and blood flow through measurements. For example, an electromyograph can estimate muscle movement corresponding to an electric potential measured by potential measurement. That is, an estimated value of muscle movement can be obtained as a measurement value. The estimated value of muscle movement can be converted into muscle load, and therefore the estimated value of muscle load can be corrected using the measurement value. The correction here may involve, for example, averaging the estimated value and the measurement value, selecting either the estimated value or the measurement value, or substituting the estimated value into a correlation function between the estimated value and the measurement value.

[0157] The muscle hardness meter can estimate muscle hardness based on the stress generated when pressure is applied to the muscle. The estimated muscle hardness can be converted into the amount of load on the muscle, which can be used to correct the estimated value in the same way as above.

[0158] A pressure gauge can measure the pressure applied to a subject's body part. These pressure parameters can be input into a musculoskeletal model. Inputting additional parameters such as pressure improves the estimation accuracy of the musculoskeletal model, allowing for more accurate correction of the estimated values ​​obtained using the musculoskeletal model.

[0159] The near-infrared spectrometer can obtain a measurement value obtained by spectroscopically measuring the blood flow rate of a subject. As in the above embodiment, when the estimated value does not include the blood flow rate, the estimated value may be corrected by combining the blood flow rate measured by the infrared spectrometer. Even when the estimated value includes the blood flow rate, the measured blood flow rate may be used when the reliability of the estimated blood flow rate is low.

[0160] In this way, by using measurement values ​​corresponding to estimated values ​​obtained from a different perspective and making corrections to make the estimated values ​​more accurate, it is possible to more accurately estimate the subject's fatigue level.

[0161] Furthermore, the fatigue estimation system described in the above embodiment may be used to configure a fatigue factor identification system that identifies the factors behind a subject's fatigue. Conventional devices or systems that estimate the degree of fatigue as "shoulder stiffness" and "lower back pain" have had difficulty identifying the way muscles and joints are used (i.e., the posture that causes the "shoulder stiffness" and "lower back pain"). Therefore, the fatigue estimation system disclosed herein can be used to address the above-mentioned issues.

[0162] That is, the fatigue factor identification system of the present disclosure identifies, as fatigue-causing parts, body parts that are prone to accumulate fatigue in a static posture taken by a subject (body parts with a large estimated amount of promoting various types of fatigue). Furthermore, the fatigue factor identification system may simply identify the fatigue-causing parts in one static posture taken by the subject, or may identify the fatigue-causing posture with the largest estimated amount of fatigue-causing parts from among multiple static postures taken by the subject. Furthermore, the fatigue factor identification system may present a recommended posture to replace the identified fatigue-causing posture, or may perform a fatigue recovery operation using a recovery device on the fatigue-causing parts in the fatigue-causing posture.

[0163] The fatigue factor identification system includes the fatigue estimation system described in the above embodiment and a storage device for storing information related to the estimated fatigue level. Such a storage device may be realized using, for example, a semiconductor memory, and may be one of the main storage units constituting the fatigue estimation system, or a new storage device may be provided that is communicatively connected to the estimation device.

[0164] The present disclosure may also be realized as a fatigue estimation method executed by a fatigue estimation system or an estimation device, as a program for causing a computer to execute such a fatigue estimation method, or as a computer-readable non-transitory recording medium on which such a program is recorded.

[0165] In addition, this disclosure also includes forms obtained by applying various modifications to the embodiments that a person skilled in the art would conceive, or forms realized by arbitrarily combining the components and functions of each embodiment within the scope that does not deviate from the intent of this disclosure. [Explanation of symbols]

[0166] 11. Target Audience 11a Rigid link model 11c Musculoskeletal Model 12 chairs 13 desk 13a Desk surface 100 Estimation device (fatigue estimation device) 101 First acquisition part (position acquisition part) 102 Second acquisition part 103 Third acquisition part 104 4th Acquisition Department (Subjective Acquisition Department, Personal Information Acquisition Department) 105 Posture estimation section 106 First Calculation Unit 107 Second Calculation Unit 108 Fatigue Estimation Unit 109 Output section 110 Instruction section 200 Fatigue Estimation System 201 Imaging device (information output device) 202 Timing device 203 Pressure Sensor 204 Reception Device 205 Display device (presentation device) 206 Recovery Device 207 Sensor Module 207a Position Sensor 207b Potential sensor

Claims

1. a position acquisition unit that acquires information about the position of a body part of a subject; a posture estimation unit that estimates a posture of the subject based on the information acquired by the position acquisition unit; a subjective fatigue level acquisition unit that acquires a subjective fatigue level felt by the subject; a fatigue estimation unit that estimates a fatigue level of the subject based on the duration of the posture estimated by the posture estimation unit, using the acquired subjective fatigue level as a fatigue level at a starting point on a time series, and calculates parameters for determining an estimation formula that estimates a fatigue level of the subject such that the longer the duration is, the greater the fatigue level of the subject, from the subjective fatigue level acquired at the time of acquisition of the subjective fatigue level, and estimates a fatigue level of the subject after the starting point on the time series using the estimation formula to which the calculated parameters are applied. Fatigue estimation device.

2. The fatigue estimation unit accumulating a parameter log including parameters calculated in the past to determine the estimation formula from the subjective fatigue level previously acquired; Calculating parameters for determining the estimation formula from the accumulated parameter log and the newly acquired subjective fatigue level. The fatigue estimation device according to claim 1 .

3. The fatigue estimation unit calculates a parameter for determining the estimation formula from the newly acquired subjective fatigue level as an average value of the previously calculated parameter included in the parameter log. The fatigue estimation device according to claim 2 .

4. The fatigue estimation unit calculates parameters for determining the estimation formula from the newly acquired subjective fatigue level by multiplying each of the previously calculated parameters included in the parameter log by a weighting coefficient such that the weight increases as the elapsed time decreases and then adding the results. The fatigue estimation device according to claim 2 .

5. further comprising an instruction unit that instructs the subject to input the subjective fatigue level in a first cycle, The subjective fatigue level acquisition unit acquires the subjective fatigue level in a second period within the first period. The fatigue estimation device according to any one of claims 1 to 4.

6. the subjective fatigue level includes a fatigue level of a first body part of the subject and a fatigue level of a second body part different from the first body part, The fatigue estimation unit calculating a first parameter for determining a first estimation formula, which is the estimation formula related to the first body part, from the fatigue level of the first body part included in the acquired subjective fatigue level; estimating a fatigue level of the first part using the first estimation formula to which the calculated first parameter is applied; calculating a second parameter for determining a second estimation formula, which is the estimation formula for the second body part, from the fatigue level of the second body part included in the acquired subjective fatigue level; The fatigue level of the second part is estimated using the second estimation formula to which the calculated second parameter is applied. The fatigue estimation device according to any one of claims 1 to 4.

7. an information output device that outputs information regarding the position of a body part of a subject; a posture estimation unit that estimates a posture of the subject based on the information output by the information output device; a subjective fatigue level acquisition unit that acquires a subjective fatigue level felt by the subject; a fatigue estimation unit that estimates a fatigue level of the subject based on the duration of the posture estimated by the posture estimation unit, using the acquired subjective fatigue level as a fatigue level at a starting point on a time series, and calculates parameters for determining an estimation formula that estimates a fatigue level of the subject such that the longer the duration is, the greater the fatigue level of the subject, from the subjective fatigue level acquired at the time of acquisition of the subjective fatigue level, and estimates a fatigue level of the subject after the starting point on the time series using the estimation formula to which the calculated parameters are applied. Fatigue estimation system.

8. obtaining information about the location of a body part of the subject; Estimating a posture of the subject based on the acquired information; Obtaining the subjective fatigue level felt by the subject; calculating parameters for determining an estimation formula that estimates the subject's fatigue level so that the longer the duration of the estimated posture is, the greater the subject's fatigue level will be, based on the subjective fatigue level acquired at the time of acquiring the subjective fatigue level; The acquired subjective fatigue level is set as the fatigue level at the starting point on the time series, and the fatigue level of the subject after the starting point on the time series is estimated based on the duration using the estimation formula to which the calculated parameters are applied. Fatigue estimation methods.

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