Recovery Prediction System

Abstract

To provide a recovery prediction system capable of precisely predicting the recovery state after work.SOLUTION: A recovery prediction system S1 includes: an extraction unit 10 that extracts the duration of a posture taken by a person while working or operating one or more physical conditions by analyzing the work process of the person; a data acquisition unit 11 that acquires the physical burden of the person related to the one or more physical conditions; a fatigue prediction unit 31 that predicts at least one of the person's fatigue level or remaining physical strength when the work is completed based on the duration of the one or more physical conditions extracted by the extraction unit 10 and the burden acquired by data acquisition unit 11; and a recovery prediction section 32 that predicts at least one of the degrees of decrease in fatigue level or degree of increase in remaining physical strength from the value predicted by fatigue prediction unit 31 after completing the work.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a recovery prediction system. [Background technology]

[0002] When humans continue physical activity, fatigue (muscle fatigue) occurs in the muscles involved in the activity. This leads to a decline in activity performance and a decrease in work efficiency. To date, various studies have been conducted to analyze the phenomenon of muscle fatigue.

[0003] For example, Non-Patent Document 1 defines a state transition model for muscle fatigue, activation, and standby based on motor units. [Prior art documents] [Non-patent literature]

[0004] [Non-Patent Document 1] Ting Xia, Laura A. Frey Law, "A theoretical approach for modeling peripheral muscle fatigue and recovery", Journal of Biomechanics, 41(2008), pp.3046-3052 Summary of the Invention [Problem to be solved by the invention]

[0005] When business operators consider or improve work plans at construction sites, etc., it is important from the perspectives of both worker health and efficient planning to formulate plans that take into account fatigue and recovery caused by worker work. However, the model described in Non-Patent Document 1 simply defines the transition of muscle state, including fatigue, and does not take into account the specific work content. As a result, there is a risk that it will not be possible to accurately predict worker fatigue and recovery from it.

[0006] The present invention is intended to solve such problems, and provides a recovery prediction system that can accurately predict the recovery state after work. [Means for solving the problem]

[0007] A recovery prediction system according to an exemplary embodiment of the present invention includes an extraction unit that analyzes the steps of a first task performed by a person to extract the duration of one or more physical states, such as postures or movements, assumed by the person during the first task; an acquisition unit that acquires the person's physical stress associated with the one or more physical states; a fatigue prediction unit that predicts at least one of the person's fatigue level or remaining physical strength upon completion of the task based on the duration of the one or more physical states extracted by the extraction unit and the stress acquired by the acquisition unit; and a recovery prediction unit that predicts at least one of a decrease in the fatigue level or an increase in the remaining physical strength from the value predicted by the fatigue prediction unit after completion of the task. This recovery prediction system predicts recovery by predicting the fatigue level or remaining physical strength based on the stress associated with the person's postures or movements during the task, the stress, and the duration of the task, thereby enabling recovery predictions to be tailored to the specific task content. This enables accurate prediction of a person's recovery state after the task. It should be noted that this recovery prediction system can further improve the prediction accuracy by, for example, using a machine learning technique (for example, a technique of updating a learning model in the system). [Effects of the Invention]

[0008] The present invention provides a recovery prediction system that can accurately predict the recovery state after work. [Brief explanation of the drawings]

[0009] [Figure 1] 1 is a block diagram showing an example of a recovery prediction system according to a first embodiment. [Figure 2] FIG. 2 is a schematic diagram showing an example of a fatigue model according to the first embodiment. [Figure 3] 3 shows an example of a posture during work in the first embodiment. [Figure 4] 4 is an example of a graph showing a transition of a fatigue level of a worker according to the first embodiment. [Figure 5] 10 is an example of a graph showing a transition of a worker's remaining physical strength according to the first embodiment. [Figure 6] FIG. 2 is a block diagram showing an example of a hardware configuration of the recovery prediction system. DETAILED DESCRIPTION OF THE INVENTION

[0010] Embodiment 1 DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of the present invention will be described with reference to the drawings. Note that the following description and drawings have been omitted or simplified as appropriate for clarity of explanation.

[0011] Fig. 1 is a diagram for explaining a recovery prediction system according to an embodiment. As shown in Fig. 1, the recovery prediction system S1 according to this embodiment includes an extraction unit 10, a data acquisition unit 11, a prediction unit 12, a storage unit 13, and a display unit 14. Each unit of the recovery prediction system S1 will be described below.

[0012] The extraction unit 10 analyzes a certain work process of a person to extract at least the duration of one or more physical states of posture or movement that the person takes during the work. The work process to be analyzed is stored as log data in the storage unit 13, and the extraction unit 10 performs the analysis and extraction process using the log data. Furthermore, the extraction unit 10 may analyze the work process to extract the duration of one or more physical states during the work and the timing at which the duration occurs.

[0013] Here, "posture" refers to the physical posture of a person, and "movement" refers to a series of multiple postures taken by a person. Examples of movements include carrying a load, drilling a hole in a pillar, handing over a jig, and pulling out a nail. The task to be analyzed may include multiple sections in which one or more physical states continue. This applies to cases where a person maintains the same posture for a predetermined period of time, then stops maintaining that posture, and then continues the same posture again for a predetermined period of time, or cases where a subject maintains a certain posture for a predetermined period of time, and then maintains a different posture for a predetermined period of time.

[0014] The data acquisition unit 11 acquires data on a person's load related to one or more physical states that the person assumes while working. The one or more physical states to be acquired are the same as the physical states to be extracted by the extraction unit 10. In detail, the data acquisition unit 11 has a sensor unit 21, a posture estimation unit 22, a posture duration determination unit 23, and a load calculation unit 24.

[0015] The sensor unit 21 includes a plurality of inertial sensors attached to various parts of the subject's body (for example, at least one of the upper limbs, lower limbs, trunk, and head). Each inertial sensor acquires data related to the inertial motion of each part when the subject is in a posture (a posture taken during work) for which load data is to be acquired. Data from the sensor unit 21 is acquired until the subject finishes his or her work.

[0016] The posture estimation unit 22 estimates the position and orientation of each part of the subject's body at each time during the task, i.e., the body posture, by integrating and analyzing the data of each inertial sensor obtained from the sensor unit 21. In other words, the posture estimation unit 22 converts the data of each inertial sensor into posture data. Furthermore, the posture duration determination unit 23 determines, for each posture, how long one or more postures estimated by the posture estimation unit 22 were continued during the duration of the task to be measured.

[0017] The load calculation unit 24 identifies one or more postures determined by the posture duration determination unit 23 as having a duration equal to or longer than a predetermined threshold time or a predetermined percentage of the measurement time. Then, the load calculation unit 24 calculates the muscle load on each part of the subject's body in the identified one or more postures by applying the posture data to a calculation model previously stored in the storage unit 13. The calculation model calculates data on the magnitude and direction of external forces acting on each part of the body (e.g., each joint) in the identified posture, thereby calculating the muscle load on each part. The muscle load on each part is calculated, for example, as a numerical value of %MVC, which is a ratio to the MVC (Maximum Voluntary Contraction), but may also be calculated as other types of numerical values. Furthermore, this muscle load may change depending on the time. The muscle load for each posture is calculated in this manner.

[0018] The sensor unit 21 may include a plurality of sensors such as displacement sensors or bending sensors instead of the inertial sensor. Even in this case, the posture estimation unit 22 can estimate the posture of the subject based on the data obtained from the sensor unit 21.

[0019] Furthermore, instead of the sensor unit 21, a camera that captures still or video images of the posture of the subject may be provided. The posture estimation unit 22 estimates the posture of the subject by analyzing the video captured by the camera using image recognition technology. The posture estimation unit 22 may determine the posture of the subject using a machine learning technique, or the system may update the posture estimation model. In this way, any existing motion capture technology can be used for measuring data related to the posture of the subject in the data acquisition unit 11 and estimating the posture based on the acquired data.

[0020] Furthermore, the data acquiring unit 11 may use a computer simulation to assume a posture of the subject and calculate the load data for that posture, rather than calculating the load data by acquiring actual data related to the posture of the subject as described above. Furthermore, the data acquiring unit 11 may be an interface that simply acquires load data related to the physical condition measured or predicted by a device other than the recovery prediction system S1 from the other device.

[0021] Next, the prediction unit 12 will be described. The prediction unit 12 has a fatigue prediction unit 31 and a recovery prediction unit 32. The prediction unit 12 acquires data on the duration of one or more physical states extracted by the extraction unit 10, and muscle force load data of one or more postures applied to each part of the subject calculated by the load calculation unit 24.

[0022] The fatigue prediction unit 31 calculates the cumulative fatigue level for each posture over the duration of that posture. This allows the fatigue prediction unit 31 to predict the person's fatigue level when a task requiring one or more consecutive postures is completed. Note that the fatigue level may be predicted not only after the task is completed, but also during the task. Furthermore, the recovery prediction unit 32 predicts the degree of decrease in fatigue level from the value predicted by the fatigue prediction unit 31 after the task is completed.

[0023] An example of a model used by the prediction unit 12 to predict a person's fatigue level will be described with reference to FIG. 2. The model described in this example is a model described in Non-Patent Document 1 and is stored in the storage unit 13 in advance. This model assumes that a certain motor unit (motor unit) exists within the muscle of a certain part of the body, and makes it possible to determine the state of muscle fatigue in a specific part by calculating the state transition of each motor unit over time. Each motor unit is in one of three states: standby, active, or fatigued. The number of motor units in the standby state is defined as Muc, the number of motor units in the active state as MA, and the number of motor units in the fatigued state as MF. If the total number of motor units is M0 (= Muc + MA + MF), the ratio of the number of motor units in the standby or active state ((Muc + MA) / M0) is defined as remaining physical strength, and the ratio of the number of motor units in the fatigued state (MF / M0) is defined as fatigue level.

[0024] Furthermore, each motor unit can transition from a standby state to an active state, and conversely, from an active state to a standby state, from an active state to a fatigued state, and from a fatigued state to a standby state. An activation intensity parameter C is defined for the degree of transition from the standby state to the active state or the reverse transition, a fatigue intensity parameter F is defined for the degree of transition from the active state to the fatigued state, and a recovery intensity parameter R is defined for the degree of transition from the fatigued state to the standby state. These parameters define the processes of muscle activation, fatigue, and recovery.

[0025] In this model, the following equations are defined as the time-dependent changes in Muc, MA, and MF.

number

number

number

number

[0026] The fatigue prediction unit 31 calculates at least one of the values ​​of MF or Muc+MA at the time a certain task is completed by applying the muscle force load of each part in the identified posture and the duration of that posture during work as parameters to the equations (1)-(4) of the model shown above.

[0027] Furthermore, the recovery prediction unit 32 calculates at least one of MF and Muc+MA at the time when the recovery time has elapsed by applying the recovery time from the end of work (a period when no or very little muscle load is applied, such as a rest period) as a parameter to equations (1)-(4). However, the prediction methods of the fatigue prediction unit 31 and the recovery prediction unit 32 are not limited to this specific example.

[0028] If there are multiple sections during work in which the specified posture continues, the fatigue prediction unit 31 can calculate the MF accumulated over the duration of each section. This allows the fatigue prediction unit 31 to predict at least one of the subject's fatigue level (MF / M0) or remaining physical strength ((Muc+MA) / M0) at the end of the work. Even if there are multiple recovery time sections during work, the recovery prediction unit 32 can predict at least one of the subject's fatigue level or remaining physical strength at the end of each recovery time.

[0029] As described above, the storage unit 13 stores the log data of the work process used by the extraction unit 10, and the models used by the data acquisition unit 11 and the prediction unit 12. The display unit 14 is an interface such as a display that displays the prediction result calculated by the prediction unit 12 and presents it to the user. Note that the display unit 14 may include a speaker or the like as an interface that notifies the user of the prediction result or some kind of notification or suggestion.

[0030] Next, an example of the prediction result calculated by the prediction unit 12 will be shown.

[0031] 3 shows examples of multiple postures that are maintained for a predetermined threshold time or longer during the task being measured. Postures 1-3 show the state in which person P is carrying loads B1-B3 during tasks 1-3, respectively. In this case, the weights of the loads increase in the order of loads B2, B1, and B3, so the muscle load L applied to a specific part of the subject (e.g., arm or waist) increases in the order of L2, L1, and L3.

[0032] The extraction unit 10 analyzes a predetermined work process to extract the duration of each of tasks 1-3 during the work. The data acquisition unit 11 acquires L1-L3, which are the muscle loads L for each of tasks 1-3. The prediction unit 12 predicts the person's fatigue level during and at the end of the work based on the extracted duration of each task and the muscle loads L1-L3, as shown in the graph in Figure 4.

[0033] FIG. 4 is an example of a graph showing the transition of the fatigue level (MF / M0) [%] of an operator (subject) calculated by the prediction unit 12. This graph shows a situation where Work 1 continues for a period t1, then after a break time (Break 1) of period (t2 - t1), Work 2 continues for a period (t3 - t2), and then after a break time (Break 2) of period (t4 - t3), Work 3 continues for a period (t5 - t4). At time 0, the fatigue level was 0, whereas the fatigue prediction unit 31 calculates the fatigue level at time t1 when Work 1 ends as F1. However, the recovery prediction unit 32 calculates the fatigue level at time t2 when Break 1 ends as F2 (<F1). The fatigue prediction unit 31 calculates the fatigue level at time t3 when Work 2 after Break 1 ends as F3. However, the recovery prediction unit 32 calculates the fatigue level as F4 (<F3) at time t4 when Break 2 after Work 2 ends. Then, the fatigue prediction unit 31 calculates the fatigue level at time t5 when Work 5 after Break 2 ends as F5. As described above, since the muscle strength load L increases in the order of L2, L1, L3, the degree of increase in the fatigue level increases in the order of Work 2, Work 1, Work 3. The prediction unit 12 causes this calculated graph to be displayed on the display unit 14. Thereby, the user can know the transition of the fatigue level during work and the fatigue level after work ends.

[0034] FIG. 5 is an example of a graph showing the transition of the remaining physical strength ((Muc + MA) / M0) [%] of an operator calculated by the prediction unit 12 in the same situation as FIG. 4. At time 0, the remaining physical strength was 100, whereas at time t1 when Work 1 ends, the remaining physical strength becomes A_{1}(= 100 - F_{1}), at time t2 when Break 1 ends, the remaining physical strength becomes A_{2}(= 100 - F_{2}), at time t3 when Work 2 ends, the remaining physical strength becomes A_{3}(= 100 - F_{3}), at time t4 when Break 2 ends, the remaining physical strength becomes A_{4}(= 100 - F_{4}), and at time t5 when Work 3 ends, the remaining physical strength becomes A_{5}(= 100 - F_{5}). Thus, the prediction unit 12 may calculate the transition state of the remaining physical strength and present it to the user by the display unit 14.

[0035] In the example described above, the prediction unit 12 predicted the degree of fatigue or remaining physical strength during work and at the end of work using the muscle load of each part for one or more postures and the duration of each posture during work. However, even for movements involving multiple postures, it is possible to predict the degree of fatigue or remaining physical strength during work and at the end of work using the muscle load of each part for one or more movements and the duration of each movement during work. Furthermore, similar predictions are possible even when a period during work involves a mixture of periods during which a posture continues and periods during which a movement continues. The details of the prediction process are the same as those described above, so a description thereof will be omitted.

[0036] As described above, the recovery prediction system S1 predicts fatigue or remaining physical strength based on the load of a person's posture or movements during work and the duration of work extracted by analyzing the work process, so recovery predictions can be tailored to the specific work content. This makes it possible to accurately predict the recovery state after work. For example, the present invention is useful for predicting recovery state at construction sites where the work content can change.

[0037] Furthermore, even if a task includes multiple sections in which one or more physical conditions persist, the prediction unit 12 can predict at least one of the fatigue level or remaining physical strength at the end of the task by accumulating the fatigue level calculated for each section. This makes it possible to accurately predict the recovery state after a task that involves various postures or movements.

[0038] Furthermore, the present invention can also be varied as follows.

[0039] The recovery prediction system S1 may further include a suggestion unit capable of appropriately suggesting changes to the work process when multiple work processes are involved in the construction work. The storage unit 13 stores the types of work required for the construction work, the work time allocated to each work, the fatigue level or remaining physical strength threshold at which the worker can perform each work, information on the order in which each work should be performed, and the initially set construction schedule information (e.g., information that work 1 should be continued for time T1, followed by work 2 for time T2). For example, if the construction work includes work 1-3, the information on the order in which each work should be performed indicates that work 1 can be performed either before or after work 2, but work 3 must be performed after work 1 and 2. The suggestion unit refers to the information as needed to suggest changes to the work process to the user. The suggestion is notified to the user via the display unit 14 or the like.

[0040] For example, if the recovery prediction unit 32 predicts that the time it takes for the fatigue level to decrease to the first threshold after the completion of a first task is equal to or longer than a predetermined threshold time (e.g., a predetermined rest period), the suggestion unit determines whether a second task exists that can be performed with a fatigue level of a second threshold greater than the first threshold. If such a second task exists, the suggestion unit suggests that the second task be performed after the first task. Note that the suggestion unit may also suggest that the second task be performed after the first task if the time it takes for the remaining physical strength to increase to a third threshold after the completion of the first task is predicted to be equal to or longer than a predetermined time and a second task exists that can be performed with a remaining physical strength of a fourth threshold less than the third threshold. This allows the suggestion unit to propose a work schedule that reduces the burden on workers while shortening the construction period.

[0041] Here, the suggestion unit may refer to information on the order in which the first and second tasks should be performed, and if it is confirmed that the second task can be performed after the first task, it may suggest that the second task be performed after the first task, but if the second task cannot be performed after the first task, it may not make that suggestion. This allows the suggestion unit to make suggestions that reduce problems such as rework caused by changing the task procedure while shortening the construction period.

[0042] In the above variation, the first threshold or the third threshold may be a threshold for the fatigue level required for a preset third task. In this case, the suggestion unit suggests changing the task to be performed after the first task from the third task to the second task. This makes it possible to reduce the workload on the workers and shorten the construction period as much as possible.

[0043] In particular, the first to third tasks may be defined in advance in the storage unit 13 as tasks included in a single construction process. By referencing this definition, the proposal unit confirms that the task that can be performed after the first task can be either the second task or the third task, and then proposes changing the task to be performed after the first task from the third task to the second task. This allows the proposal unit to propose completing a single construction process early rather than proceeding with tasks in different construction processes in parallel, thereby making it possible to progress the construction efficiently.

[0044] Furthermore, the prediction unit 12 can predict the fatigue level or remaining physical strength of not only one body part of the subject, but also multiple body parts. Details of this prediction method are as described above. This allows visualization of which body parts of the worker are prone to fatigue or recovery, thereby enabling the development of efficient work plans that reduce the overall burden on the worker. In this case, if the prediction unit predicts that the time it takes for the fatigue level value of at least one body part to decrease to the first threshold after the completion of a first work task is equal to or longer than a predetermined threshold time, the suggestion unit can also determine whether a second work task exists that can be performed with a fatigue level of a second threshold value greater than the first threshold value. If such a second work task exists, the suggestion unit suggests that the second work task be performed after the first work task. Furthermore, the suggestion unit can suggest that the second work task be performed after the first work task by performing a similar determination for multiple body parts, but with respect to the remaining physical strength rather than the fatigue level. The detailed method for suggesting the second work task can also be similar to that described above.

[0045] In addition, if the recovery prediction unit 32 predicts that the time it takes for the fatigue level value to decrease to the first threshold value or the time it takes for the remaining physical strength value to increase to the third threshold value after the completion of the first task is equal to or longer than a predetermined threshold time, the suggestion unit can also suggest changing the work time or work content of the first task.

[0046] The recovery prediction unit 32 can also predict the sense of strain that the worker feels subjectively. Here, the recovery prediction unit 32 uses data on other tasks that are different from the task to be predicted, stored in the storage unit 13. The data on other tasks includes prediction results of at least one of the worker's fatigue level or remaining physical strength in the other tasks, and at least one of the subjective fatigue level or subjective remaining physical strength that the worker feels subjectively in the other tasks. Here, prediction of the fatigue level or remaining physical strength in the other tasks is realized using the same method as the prediction for the task to be predicted, as described above. This prediction may be performed by the prediction unit 12 or by a prediction system separate from the recovery prediction system S1.

[0047] Subjective fatigue level or subjective remaining physical strength is subjective information indicating the worker's own fatigue level or remaining physical strength, answered by the worker at a certain timing during work (for example, immediately after work is completed or a predetermined time has elapsed since work was completed), and may be quantitative (quantified) information or qualitative information. Even for the same work content, different workers may answer differently regarding subjective fatigue level or subjective remaining physical strength. An example of quantitative information is a scale of fatigue level (or remaining physical strength) from 5 to 1 in descending order. An example of qualitative information is an expression indicating the magnitude of fatigue level (or remaining physical strength), such as "tired," "slightly tired," "not very tired," or "not tired." However, the quantitative or qualitative information that can be used is not limited to these.

[0048] The predicted results of fatigue level or remaining physical strength included in the data for other tasks, and the subjective fatigue level or subjective remaining physical strength corresponding to the predicted results, are values ​​and answers at the same (or corresponding) timing in the other tasks, such as immediately after the other tasks are completed or a predetermined time has elapsed since the other tasks are completed. This timing may include one or more arbitrary timings in the other tasks. Furthermore, the data for other tasks may be stored not for one task, but for multiple tasks, including the predicted results for each task and at least one of the subjective fatigue level or subjective remaining physical strength.

[0049] The recovery prediction unit 32 further predicts at least one of the subjective fatigue level or subjective remaining physical strength felt by the person after the completion of the work by referring to the stored data of other work and at least one of the degree of decrease in fatigue level or the degree of increase in remaining physical strength predicted by the recovery prediction unit 32. Note that, when making this prediction, the recovery prediction unit 32 may further use at least one of the fatigue level or remaining physical strength at the time of completion of the work predicted by the fatigue prediction unit 31. The timing to be predicted is, for example, the timing when a predetermined time (rest time) has elapsed after the completion of the work. However, predictions may be made for any multiple timings after the completion of the work. The recovery prediction unit 32 may perform this prediction using a predetermined algorithm or a model using AI.

[0050] If the predicted quantitative subjective fatigue level at the time of prediction is equal to or greater than a predetermined threshold, or if the predicted quantitative subjective remaining stamina is less than a predetermined threshold, the suggestion unit may notify the user of this. Alternatively, the suggestion unit may suggest at least one of shortening the time or changing the content of a scheduled task, or shortening the time or changing the content of a subsequent task after fatigue recovery. Even if the predicted qualitative subjective fatigue level or subjective remaining stamina indicates specific information, the suggestion unit may provide a similar notification or suggestion to the user. For example, if the predicted quantitative subjective fatigue level is "5" or "4" or if the predicted qualitative subjective fatigue level is "tired" or "slightly tired," the recovery prediction unit 32 may provide the user with the above-mentioned notification or suggestion.

[0051] When proposing a change in the content of a subsequent task, the suggestion unit may refer to information about each task stored in the storage unit 13, identify tasks that can be performed based on the predicted quantitative or qualitative subjective fatigue level or subjective remaining physical strength, and suggest that the identified task be performed as the subsequent task. Here, as described above, the suggestion unit may also refer to information about the order in which the tasks should be performed and confirm that a task (second task) identified as executable based on the prediction result can be performed after the task (first task) that is to be performed first. If the second task can be performed after the first task, the suggestion unit suggests that the second task be performed after the first task, and if the second task cannot be performed after the first task, the suggestion unit does not implement the suggestion.

[0052] Furthermore, the threshold value of the quantitative subjective fatigue level or subjective remaining physical strength at the predicted timing after the completion of the first task, which the suggestion unit uses to determine whether to execute the proposal for the subsequent task, may be a threshold value for the execution of a third task that is preset to be executed subsequently. For example, if the quantitative subjective fatigue level at the predicted timing is "4" and the quantitative subjective fatigue level required to execute the third task is "3" or less, the threshold value may be used to determine that the third task cannot be executed because the burden on the worker is too great. Therefore, the suggestion unit proposes changing the task to be executed after the first task from the third task to the second task, which requires a quantitative subjective fatigue level of "4" or less to execute the task. Furthermore, the first to third tasks may be predefined in the storage unit 13 as tasks included in a single construction process. The suggestion unit refers to the definition and confirms that the work that can be performed after the first work can be either the second work or the third work, and then suggests changing the work to be performed after the first work from the third work to the second work.

[0053] In this determination process, the suggestion unit can also use information on qualitative subjective fatigue level or subjective remaining physical strength instead of the threshold value of quantitative subjective fatigue level or subjective remaining physical strength. For example, if the qualitative subjective fatigue level at the time to be predicted is "slightly tired" and the qualitative subjective fatigue level at which the third task cannot be performed is "tired" or "slightly tired," the threshold value is used to determine that the third task cannot be performed because the burden on the worker is too great. Therefore, the suggestion unit suggests changing the task to be performed after the first task from the third task to a second task that requires a qualitative subjective fatigue level of "slightly tired" or lower to perform the task.

[0054] Furthermore, even when a worker actually performs a task and acquires the worker's fatigue level or remaining physical strength in real time, the recovery prediction unit 32 can also predict the worker's subjective fatigue level or subjective remaining physical strength after the task is completed in real time by referencing the acquired values ​​and the other task data described above. Here, the data acquisition unit 11 and the prediction unit 12 acquire the worker's task data to acquire the duration of one or more physical states (postures or movements) and the associated loads, and use the acquired data to derive at least one of the worker's fatigue level or remaining physical strength in real time. Alternatively, real-time data on the fatigue level or remaining physical strength may be acquired by a different device (e.g., a wearable device attached to the worker). At a predetermined timing after the task is completed, if the predicted quantitative subjective fatigue level is equal to or greater than a predetermined threshold, the predicted quantitative subjective remaining physical strength is less than a predetermined threshold, or the predicted qualitative subjective fatigue level or subjective remaining physical strength indicates at least one of specific information, the recovery prediction unit 32 detects the state. The recovery prediction unit 32 can use this as a trigger to notify the worker that the worker's workload is excessive. Alternatively, the prediction unit 12 can make a proposal to ease the burden on the worker, such as canceling the scheduled subsequent work or changing the subsequent work.

[0055] The variations described above can also be implemented as a determination of the fatigue level or remaining physical strength of at least one of multiple body parts of a worker. For example, assume that at a predetermined timing after work is completed, the quantitative subjective fatigue level of at least one body part is equal to or greater than a predetermined threshold, or the quantitative subjective remaining physical strength is less than a predetermined threshold. In this case, the suggestion unit can provide the above-described notification or a suggestion regarding the subsequent work (at least one of shortening the time or changing the content). Note that similar determination processing and notification or suggestion processing can be performed using qualitative information instead of a quantitative threshold.

[0056] The prediction model used by the prediction unit 12 is not limited to the one described above. The user may also input feedback indicating the accuracy of the prediction result displayed by the display unit 14 to the recovery prediction system S1. Based on the feedback, the recovery prediction system S1 can update the prediction model using AI. This can further improve the prediction accuracy.

[0057] In the above-described embodiment, the present invention has been described as a hardware configuration. However, the present invention can also be realized by having a processor in a computer execute a computer program to perform the processes (steps) of the recovery prediction system S1 described in the above-described embodiment.

[0058] 6 is a block diagram showing an example of the hardware configuration of an information processing device 90 that executes the processes of the above-described embodiments. Referring to FIG. 6, the information processing device 90 includes a signal processing circuit 91, a processor 92, and a memory 93.

[0059] The signal processing circuit 91 is a circuit for processing signals in accordance with the control of the processor 92. The signal processing circuit 91 may include a communication circuit for receiving signals from a transmitting device.

[0060] The processor 92 is connected (coupled) to the memory 93, and performs the processing of the device described in the above embodiment by reading and executing software (computer programs) from the memory 93. As an example of the processor 92, one of a CPU (Central Processing Unit), an MPU (Micro Processing Unit), an FPGA (Field-Programmable Gate Array), a DSP (Demand-Side Platform), and an ASIC (Application Specific Integrated Circuit) may be used, or a plurality of these may be used in parallel.

[0061] The memory 93 is configured with a volatile memory, a nonvolatile memory, or a combination thereof. The memory 93 is not limited to one, and multiple memories may be provided. The volatile memory may be, for example, a RAM (Random Access Memory) such as a DRAM (Dynamic Random Access Memory) or an SRAM (Static Random Access Memory). The nonvolatile memory may be, for example, a ROM (Read Only Memory) such as a PROM (Programmable Random Only Memory) or an EPROM (Erasable Programmable Read Only Memory), a flash memory, or an SSD (Solid State Drive).

[0062] The memory 93 is used to store one or more instructions. Here, the one or more instructions are stored in the memory 93 as a group of software modules. The processor 92 can perform the processes described in the above-mentioned embodiments by reading and executing the group of software modules from the memory 93. The memory 93 can be located anywhere.

[0063] As described above, one or more processors included in each device in the above-described embodiments execute one or more programs including instructions for causing a computer to execute the algorithms described using the drawings. This processing enables the fatigue prediction method described in each embodiment to be realized.

[0064] The program includes instructions (or software code) that, when loaded into a computer, cause the computer to perform one or more functions described in the embodiments. The program may be stored in a non-transitory computer-readable medium or a tangible storage medium. By way of example and not limitation, computer-readable media or tangible storage media include random-access memory (RAM), read-only memory (ROM), flash memory, solid-state drive (SSD) or other memory technologies, CD-ROM, digital versatile disk (DVD), Blu-ray® disk or other optical disk storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage device. The program may also be transmitted on a transitory computer-readable medium or communication medium. By way of example and not limitation, transitory computer-readable media or communication media include electrical, optical, acoustic, or other forms of propagated signals.

[0065] A part or all of the above-described embodiments can be described as, but not limited to, the following supplementary notes. (Appendix 1) an extraction unit that extracts durations of one or more physical states of postures or movements taken by the person during a first task by analyzing a step of the first task of the person; an acquisition unit that acquires the physical load of the person related to the one or more physical conditions; a fatigue prediction unit that predicts at least one of a fatigue level or a remaining physical strength of the person when the first work is completed, based on the duration of the one or more physical conditions extracted by the extraction unit and the load acquired by the acquisition unit; and a recovery prediction unit that predicts, after the first task is completed, at least one of a degree of decrease in the fatigue level from the value predicted by the fatigue prediction unit or a degree of increase in the remaining physical strength. Recovery prediction system. (Appendix 2) and a suggestion unit that, when the recovery prediction unit predicts that the time it will take for the value of the fatigue level to decrease to the first threshold after the end of the first task will be equal to or longer than a predetermined time, determines whether or not there is a second task that can be performed with a fatigue level of a second threshold that is greater than the first threshold, and suggests that the second task be performed after the first task if the second task exists; or, when the recovery prediction unit predicts that the time it will take for the value of the remaining physical strength to increase to a third threshold after the end of the first task will be equal to or longer than a predetermined time, determines whether or not there is a second task that can be performed with a remaining physical strength of a fourth threshold that is smaller than the third threshold, and suggests that the second task be performed after the first task if the second task exists. 2. The recovery prediction system of claim 1. (Appendix 3) the suggestion unit suggests that the second task be performed after the first task when it is confirmed that the second task can be performed after the first task based on information about the order in which the first and second tasks should be performed. 2. The recovery prediction system of claim 1. (Appendix 4) the acquisition unit acquires loads on a plurality of body parts of the person with respect to the one or more physical conditions; the fatigue prediction unit predicts at least one of a fatigue level or a remaining physical strength of the plurality of body parts of the person when the first work is completed, based on the duration of the one or more physical conditions extracted by the extraction unit and the loads on the plurality of body parts acquired by the acquisition unit; and the recovery prediction unit predicts, after completion of the first task, at least one of a degree of decrease in the fatigue level or a degree of increase in the remaining physical strength from a value predicted by the fatigue prediction unit, for the plurality of body parts. 2. The recovery prediction system of claim 1. (Appendix 5) and a suggestion unit that, when the recovery prediction unit predicts that the time it takes for the fatigue level value to decrease to the first threshold in one or more of the body parts after the end of the first task is equal to or longer than a predetermined time, determines whether or not there is a second task that can be performed in the one or more body parts with a fatigue level of a second threshold value that is greater than the first threshold value, and suggests that the second task be performed after the first task if the second task is present; or, when the recovery prediction unit predicts that the time it takes for the remaining physical strength value to increase to a third threshold in one or more of the body parts after the end of the first task is equal to or longer than a predetermined time, determines whether or not there is a second task that can be performed with a remaining physical strength of a fourth threshold value that is smaller than the third threshold value, and suggests that the second task be performed after the first task if the second task is present. 5. The recovery prediction system of claim 4. (Appendix 6) the recovery prediction unit further predicts at least one of the subjective fatigue level or the subjective remaining physical strength that the person will subjectively feel after the end of the first task by referring to data on the other task including a prediction result of at least one of the person's fatigue level or the remaining physical strength in the other task, and at least one of the subjective fatigue level or the subjective remaining physical strength that the person subjectively felt in the other task, and at least one of the degree of decrease in the fatigue level or the degree of increase in the remaining physical strength predicted by the recovery prediction unit. A recovery prediction system according to any one of appendices 1 to 5. (Appendix 7) the first threshold or the third threshold is a threshold of a fatigue level required for a preset third task, and the suggestion unit suggests changing the task to be performed after the first task from the third task to the second task. 4. The recovery prediction system according to claim 2 or 3. (Appendix 8) The first to third operations are defined in advance as operations included in a single process, the suggestion unit suggests changing the task to be performed after the first task from the third task to the second task by referring to the definition of the process. 8. The recovery prediction system of claim 7.

[0066] Although the present invention has been described above with reference to the embodiments, the present invention is not limited to the above. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of the present invention within the scope of the disclosure. [Explanation of symbols]

[0067] S1 Recovery Prediction System 10 Extraction Unit 11 Data acquisition section 12 Prediction section 13 Storage section 14 Display section 21 Sensor unit 22 Attitude estimation unit 23 Posture duration determination unit 24 Load calculation unit 31 Fatigue prediction section 32 Recovery prediction section

Claims

1. an extraction unit that extracts durations of one or more physical states of postures or movements taken by the person during a first task by analyzing a process of the first task of the person; an acquisition unit that acquires a physical load of the person related to the one or more physical conditions; a fatigue prediction unit that predicts at least one of a fatigue level or a remaining physical strength of the person when the first work is completed, based on the duration of the one or more physical conditions extracted by the extraction unit and the load acquired by the acquisition unit; and a recovery prediction unit that, when the fatigue prediction unit predicts the fatigue level, predicts a degree of decrease in the fatigue level after the completion of the first work from the predicted value of the fatigue level, and, when the fatigue prediction unit predicts the remaining physical strength, predicts a degree of increase in the remaining physical strength after the completion of the first work from the predicted value of the remaining physical strength; a suggestion unit that, when the recovery prediction unit predicts that the time it takes for the value of the fatigue level to decrease to the first threshold after the end of the first task is a predetermined time or more, determines whether or not a second task that can be performed with a fatigue level of a second threshold that is greater than the first threshold, and suggests performing the second task after the first task if the second task exists; and, when the recovery prediction unit predicts that the time it takes for the value of the remaining physical strength to increase to a third threshold after the end of the first task is a predetermined time or more, determines whether or not a second task that can be performed with a remaining physical strength of a fourth threshold that is less than the third threshold, and suggests performing the second task after the first task if the second task exists. Recovery prediction system.

2. the suggestion unit suggests that the second task be performed after the first task when it is confirmed that the second task can be performed after the first task based on information about the order in which the first and second tasks should be performed. The recovery prediction system of claim 1 .

3. the recovery prediction unit further predicts the subjective fatigue level that the person will feel subjectively after the end of the first task by referring to data on the other task including a prediction result of the person's fatigue level in the other task and a subjective fatigue level that the person subjectively felt in the other task, and a degree of decrease in the fatigue level predicted by the recovery prediction unit; or further predicts the subjective remaining physical strength that the person will feel subjectively after the end of the first task by referring to data on the other task including a prediction result of the person's remaining physical strength in the other task and the subjective remaining physical strength that the person subjectively felt in the other task, and a degree of increase in the remaining physical strength predicted by the recovery prediction unit. The recovery prediction system according to claim 1 or 2.

4. an extraction unit that extracts durations of one or more physical states of postures or movements taken by the person during a first task by analyzing a process of the first task of the person; an acquisition unit that acquires loads at a plurality of body parts of the person regarding the one or more physical conditions; a fatigue prediction unit that predicts at least one of fatigue levels or remaining physical strength of the person at the plurality of body parts when the first work is completed, based on the duration of the one or more physical conditions extracted by the extraction unit and the loads on the plurality of body parts acquired by the acquisition unit; a recovery prediction unit that, when the fatigue prediction unit has predicted the fatigue level for the plurality of body parts, predicts a degree of decrease in the fatigue level after completion of the first work from the predicted value of the fatigue level, and, when the fatigue prediction unit has predicted the remaining physical strength, predicts a degree of increase in the remaining physical strength after completion of the first work from the predicted value of the remaining physical strength; a suggestion unit that, when the recovery prediction unit predicts that after the end of the first task, it will take a predetermined time or more for the value of the fatigue level to decrease to the first threshold in one or more of the body parts, determines whether or not there is a second task that can be performed in the one or more body parts with a fatigue level of a second threshold that is greater than the first threshold, and suggests that the second task be performed after the first task, if the second task is present; and, when the recovery prediction unit predicts that after the end of the first task, it will take a predetermined time or more for the value of the remaining physical strength to increase to a third threshold in one or more of the body parts, determines whether or not there is a second task that can be performed in the one or more body parts with a remaining physical strength of a fourth threshold that is smaller than the third threshold, and suggests that the second task be performed after the first task, if the second task is present. Recovery prediction system.

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