Worker workload determination device and worker workload determination method

Abstract

[Problem] There has been the problem that a worker's burden of stress or the like felt in the brain could not be determined in a prior safety and sanitary management monitor device. [Solution] In a working environment in which a worker P repetitively performs the same work, a worker's burden determination device 10 according to the present invention measures a plurality of times an eardrum temperature that reflects a hypothalamus temperature, uses a calculated eardrum temperature fluctuation coefficient CV and determines a worker's burden of work. In addition, a manager can figure out the personal ability of the worker P as the worker's burden is visualized, and promote improvement in the work environment and increase work productivity by putting the right workers in the right places.

Description

Technical Field

[0001] The present invention relates to an operator burden determination device and an operator burden determination method.

Background Art

[0002] Patent Document 1 discloses a conventional safety and health management monitor device. The safety and health management monitor device is a device that manages the safety and health of workers at a work site, and uses data such as the body temperature, blood pressure, and pulse of workers obtained via a wearable computer. And in the feature extraction unit of the safety and health management monitor device, for example, based on physical load information such as the load during work from a load sensor worn by the worker and psychological load information such as the heart rate of the worker obtained via the wearable computer, it has a function of extracting the physical characteristics of the worker for each work content. Note that the physical characteristics refer to, for example, a mutual correlation value that reflects individual differences by adding a physiological load to a general load state obtained from the work content and the physical load.

[0003] The feature extraction unit includes an arithmetic unit that calculates a mutual correlation function, and obtains a mutual correlation function between the physical load and the physiological load of the worker for each work content. And in the safety and health management unit of the safety and health management monitor device, a threshold value is generated from a plurality of physical characteristics of each worker, the threshold value is compared with the mutual correlation function, and a warning is issued according to the result of the comparison operation.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] As described above, the conventional safety and health management monitor device makes workers wear work clothes with a built-in wearable computer, and continuously acquires the health status of the workers through various sensors attached to the body. Then, the safety and health management monitor device grasps stress and the like with respect to the work load taking into account individual differences, and reflects it in the work plan and the like.

[0006] However, the physical load information handled by the above-described safety and health management monitor device is information such as the load during work, and is targeted at so-called heavy labor using the whole body at a civil engineering site or the like, and is not targeted at labor that repeatedly performs the same work using the upper limbs of workers at an assembly site in a factory or the like. That is, the conventional safety and health management monitor device has a problem that it is impossible to determine the burden on the worker with respect to stress and the like felt by the brain when the worker thinks about the work content and the like.

[0007] In particular, in order to detect stress and the like felt by the worker's brain, it is desirable to measure the deep body temperature and the like in a region close to the brain. And in the conventional device, since there is no device that measures the hypothalamic temperature, which is an example of the deep body temperature, or the deep body temperature reflecting the hypothalamic temperature, and determines the worker burden, there is often a problem that the worker cannot be arranged in the appropriate material process while taking into account the working environment of the worker. And because the worker cannot be arranged in the appropriate material process, there is a risk of deterioration of the working environment due to the worker burden such as stress for the worker, and it is difficult for the company to improve labor productivity, and there are also problems such as deterioration of the worker turnover rate and difficulty in securing human resources in the first place.

[0008] The present invention has been made in view of the above circumstances, measures the tympanic membrane temperature reflecting the hypothalamic temperature that is easy to detect the burden status on the worker's brain, and uses it for the determination of the worker burden, thereby improving the working environment of the worker and improving labor productivity. Provided are a worker burden determination device and a worker burden determination method.

Means for Solving the Problems

[0009] In the worker burden determination device of the present invention, when a worker repeats the same work, it is a worker burden determination device that determines the worker burden of the worker for the same work. A tympanic membrane temperature measurement unit that measures the tympanic membrane temperature, which reflects the temperature of the hypothalamus of the worker, a plurality of times while the worker performs the same work once, and acquires temperature data; and using the temperature data transmitted from the tympanic membrane temperature measurement unit, an arithmetic control unit that calculates a coefficient of variation of tympanic membrane temperature CV for each number of work repetitions of the same work. The arithmetic control unit calculates a correlation coefficient r1 for the transition of the coefficient of variation of tympanic membrane temperature CV with respect to the number of work repetitions of the same work, compares the correlation coefficient r1 with a preset first threshold value, and when the correlation coefficient r1 is less than or equal to the first threshold value, makes a first determination that the worker burden due to the same work is small, and when the correlation coefficient r1 is greater than the first threshold value, makes a second determination that the worker burden due to the same work is large.

[0010] In the method for determining the operator's burden according to the present invention, when an operator repeats the same operation, an operator burden determination device determines the operator's burden for the same operation. The tympanic membrane temperature measurement unit of the operator burden determination device measures the tympanic membrane temperature reflecting the temperature of the hypothalamus of the operator a plurality of times while the operator performs the same operation once, and obtains the temperature data of the operator. A data acquisition step; a first calculation step in which an arithmetic control unit of the operator burden determination device calculates a tympanic membrane temperature variation coefficient CV from the temperature data for each number of operations of the same operation; a second calculation step in which the arithmetic control unit of the operator burden determination device calculates a correlation coefficient r1 for the transition of the tympanic membrane temperature variation coefficient CV with the number of operations of the same operation; and a determination step in which the arithmetic control unit of the operator burden determination device determines the operator's burden for the same operation using the tympanic membrane temperature variation coefficient CV and the correlation coefficient r1. In the determination step, the correlation coefficient r is compared with a preset first threshold value. When the correlation coefficient r1 is less than or equal to the first threshold value, a first determination is made that the operator's burden due to the same operation is small. When the correlation coefficient r1 is greater than the first threshold value, a second determination is made that the operator's burden due to the same operation is large.

Effect of the Invention

[0011] The operator burden determination device according to the present invention includes a tympanic membrane temperature measurement unit that measures the tympanic membrane temperature reflecting the hypothalamus temperature a plurality of times in a working environment where an operator is subjected to a working load of repeating the same operation, and an arithmetic control unit that calculates a tympanic membrane temperature variation coefficient CV using the temperature data input from the tympanic membrane temperature measurement unit. Then, the arithmetic control unit determines the operator's burden for the operation using the tympanic membrane temperature variation coefficient CV. With this structure, the operator's burden is visualized using the operator burden determination device, so that the manager can grasp the individual abilities of the operators, arrange the operators in the right places, improve the working environment of the operators, and increase labor productivity.

[0012] The method for determining the operator's burden according to the present invention includes a data acquisition step of acquiring temperature data of the tympanic membrane temperature reflected by the hypothalamic temperature during the operator's work in a working environment where the operator is subjected to a working load of repeating the same work, a first calculation step of calculating the coefficient of variation CV of the tympanic membrane temperature from the temperature data, and a determination step of determining the operator's burden from the coefficient of variation CV of the tympanic membrane temperature. By this method for determining the operator's burden, the operator's burden is visualized as a numerical value, so that the manager can grasp the individual ability of the operator, arrange the operator in the right place, improve the working environment of the operator, and increase the labor productivity.

Brief Description of the Drawings

[0013]

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Embodiments for Carrying Out the Invention

[0014] First, the operator burden determination device 10 according to an embodiment of the present invention will be described in detail with reference to the drawings. In the description of this embodiment, the same reference numerals are generally used for the same members, and repeated descriptions are omitted.

[0015] FIGS. 1A and 1B are schematic diagrams for explaining the configuration of the operator burden determination device 10 of this embodiment. FIGS. 2A to 2C are schematic diagrams for explaining the element technologies used in the operator burden determination method of the operator burden determination device 10 of this embodiment. FIG. 3 is a schematic diagram for explaining the stored data such as the measurement data and calculated data of the operator burden determination device 10 of this embodiment.

[0016] The operator burden determination device 10 of this embodiment is, for example, a device that visualizes the operator burden imposed on the operator P due to the influence of vocational preparedness and work difficulty level for the purpose of the establishment and early combat power formation of a new operator P on a factory production line. And in a company, by using the operator burden determination device 10 to grasp the individual abilities of individual operators P before arranging the operator P on the work line, the labor environment of the operator P is improved and the improvement of labor productivity on the production line is realized.

[0017] As shown in FIG. 1A, the operator burden determination device 10 mainly includes a eardrum temperature measurement unit 11 that measures the eardrum temperature of the operator P, a work time measurement unit 12 that measures the required work time T for each identical operation of the operator P, an arithmetic control unit 13 that determines the operator burden using the above measurement data, and a display unit 14 that displays the determination result determined by the arithmetic control unit 13. Note that the above measurement data is transmitted from each measurement unit 11, 12 to the arithmetic control unit 13 via the communication network 15 (see FIG. 1B). The communication network 15 is a network system constructed using a wireless LAN installed at a work site such as a factory, a private PHS, or a wired connection such as a LAN cable, and transmits various data.

[0018] FIG. 1B shows an example of constructing the operator burden determination device 10 on a production line. As the eardrum temperature measurement unit 11, an eardrum thermometer 16 that can be worn on the operator P's ear during work is used. As the work time measurement unit 12, a touch panel 17 installed on the workbench is used. As the arithmetic control unit 13, a personal computer 13A and a server 13B are used. Then, the eardrum thermometer 16 and the touch panel 17 are connected to be able to communicate with the personal computer 13A and the server 13B via the communication network 15. Note that the arithmetic control unit 13 may be composed only of the personal computer 13A, and the above measurement data etc. may be stored in the memory of the personal computer 13A.

[0019] As the eardrum temperature measurement unit 11, for example, an eardrum thermometer 16 with the product number BL100 manufactured by Technonext Co., Ltd. is used. The eardrum thermometer 16 is a measuring device that measures the eardrum temperature, which is the deep body temperature, using a non-contact infrared sensor. Then, the eardrum temperature measurement unit 11 is connected to the arithmetic control unit 13 via the communication network 15, and transmits the measurement data acquired from each operator P to the arithmetic control unit 13 in real time. Note that the above measurement data may be stored in the internal memory of the eardrum temperature measurement unit 11 and transferred to the arithmetic control unit 13 after the work is completed.

[0020] Also, the eardrum temperature measurement unit 11 measures the eardrum temperature of the operator P multiple times from the start to the end of one identical operation. Then, in the arithmetic control unit 13, as the number of times of measuring the eardrum temperature increases, the calculation accuracy of the correlation coefficient r1 with respect to the transition of the eardrum temperature variation coefficient CV associated with the number of operation times of the identical operation described later improves. Therefore, in the present embodiment, considering the capacity of the rechargeable battery of the actually used eardrum thermometer 16, the timeliness of measurement data, etc., data collection is performed once every 2 seconds. However, for example, any design change is possible with the interval of the measurement time for each measurement being 5 seconds or less.

[0021] As the working time measurement unit 12, for example, a touch panel 17 with a timer function set is used. The working time measurement unit 12 is arranged near the workbench. When the operator P starts one identical operation, the measurement of the required working time T is started by touching the start button of the touch panel 17. Similarly, after the above operation is completed, the operator P touches the stop button of the touch panel 17 to end the measurement of the required working time T. Then, the working time measurement unit 12 measures the required working time T for each identical operation of each operator P, and transmits the measurement data to the arithmetic control unit 13 in real time via the communication network 15.

[0022] Note that an image processing device that photographs the working state of the workbench may be used as the working time measurement unit 12. In this case, the start time and end time of one identical operation of the operator P are recognized from the image data of the image processing device, and after calculating the required working time T, the measurement data of the required working time T is transmitted to the arithmetic control unit 13 via the communication network 15. Also, the above measurement data may be stored in the internal memory of the working time measurement unit 12 and transferred to the arithmetic control unit 13 after the operation is completed.

[0023] The arithmetic control unit 13 is composed of a CPU (CENTRAL PROCESSING UNIT), a ROM (READ ONLY MEMORY), a RAM (RANDOM ACCESS MEMORY), etc. As described above, as the arithmetic control unit 13, the CPU of the personal computer 13A or the like is used. Then, using the known power approximation formula, linear approximation formula, and coefficient of variation calculation formula of the Excel function, the correlation coefficient r1 for the transition of the coefficient of variation of tympanic membrane temperature CV with respect to the number of work repetitions of the same work, the correlation coefficient r2 for the transition of the time required for one work T with respect to the number of work repetitions of the same work, etc. are calculated. Note that the arithmetic control unit 13 is not limited to using the known power approximation formula of the Excel function. As the approximation formula, for example, a logarithmic approximation formula or the like, which is a known approximation formula used when the transition of the CV value of the coefficient of variation of tympanic membrane temperature CV tends to converge, can also be used.

[0024] The arithmetic control unit 13 can determine and visualize the workload on the operator P for work training and the like by comparing the calculated correlation coefficients r1, r2, etc. with a first threshold value or the like as a preset determination criterion.

[0025] Further, the arithmetic control unit 13 has a storage unit (not shown), and the storage unit is composed of, for example, a non-volatile memory such as an EEPROM (Electrically Erasable Programmable Read-only Memory). Note that, as described above, the server 13B may be used as the storage unit. Then, the arithmetic control unit 13 stores the above measurement data, the calculated correlation coefficients r1, r2, the coefficient of variation of tympanic membrane temperature CV, the significance level p1, and the first to third threshold values set in advance.

[0026] As the monitor 18, for example, a display or the like is used. The monitor 18 displays the determination result calculated by the arithmetic control unit 13 via the communication network 15, and can also display the operator burden such as the work proficiency of the operator P in real time. Then, during the work, the operator P can check the determination result and improve the work proficiency by enhancing the concentration on the work. On the other hand, the operator P can also check the determination result, recognize his / her own stress state, and strive to relieve stress such as taking a deep breath consciously during breaks in the work.

[0027] In FIG. 2A, the drawing described in "IE Method 7 Tools 4 Motion Analysis (Work Analysis) What is Servic Analysis? Visualizing Operations with Symbols Edited by Fumio Nishi (https: / / seizo-bu.com / ie)" is cited and a part of it is processed and shown.

[0028] Servic analysis is one of the analysis methods belonging to the motion study of the IE (work study) method. And Servic analysis is a method devised by F.B. Gilbreth, which decomposes the basic motions common to all works into 18 types of therbligs (servics) for analysis.

[0029] The first classification refers to the motions mainly performed by the upper limbs and necessary for the work. Specifically, they are 8 motions: extend, grasp, carry, release, position, use, assemble, and disassemble. And the second classification refers to the motions performed by the sensory organs and the brain that delay the work. Specifically, they are 5 motions: search, select, examine, think, and prepare. Incidentally, the third classification refers to the motions unnecessary for the work, which are 5 motions: find, continue to grasp, inevitable delay, avoidable delay, and rest.

[0030] In the worker burden determination device 10 of the present embodiment, in the case of acceptance education, as an example for determining the worker burden including the work proficiency of the worker P as a numerical value, the following two types of work trainings are set. Specifically, as work trainings, a screwing work training mainly composed of the operations of the first classification and a harness wiring work training which is a series of operations in which the second classification is attached to the first classification are prepared. Note that as work trainings, those with various levels of difficulty can be prepared by combining the operations of the first classification and the second classification of the serviceability analysis.

[0031] In the above acceptance education, before arranging the worker P in the production line at the time of launching a new model or the like, in the determination of the applicability when arranging the worker P, while taking into account the degree of occupational preparedness of each worker P, the purpose is to determine whether the individual ability of each worker P can adapt to the second classification. And when the second classification is included in the elemental work of the same work and its appearance rate is high, the difficulty of the work increases, which may cause a worker burden and may also be an inhibiting factor for work proficiency.

[0032] In FIG. 2B, the drawing described in the Subaru Mental Clinic (http: / / ashiya-subaru.org / index.php) is cited and a part of it is processed and shown.

[0033] Although it is also described in the paper "Research on the Measurement Method of Human Deep Body Temperature (1998)" by Manabu Shibazaki (Doctoral Thesis of the Graduate School of Natural Science, Kobe University), it is known that various information of the human can be obtained by measuring the deep body temperature of the human. For example, it is known that the tympanic membrane temperature, which is one of the indices of deep body temperature, best reflects the temperature of the hypothalamus, which is the body temperature regulation center. And the tympanic membrane temperature is considered to be an index for grasping the burden status on the brain by reflecting the brain temperature from the internal carotid artery blood temperature flowing to the hypothalamus and measuring its temperature change.

[0034] As shown in the figure, the hypothalamus is the center of the autonomic nervous system and is close in distance to the limbic system of the brain, which is the center of emotions (such as anxiety, irritation, and tension). It is considered that the functions of emotions and the autonomic nervous system are closely related. Therefore, it is considered effective to measure the hypothalamic temperature close to the neural network of the limbic system of the brain in order to know the physical excitation state due to emotional excitement during the process of work proficiency. And the eardrum is considered to best reflect the temperature change of the internal carotid artery flowing directly below the eardrum. In the operator burden determination device 10 of the present embodiment, since the hypothalamic temperature cannot be directly measured, the eardrum temperature that most accurately reflects the hypothalamic temperature is measured and used as data for determining the operator burden. Note that in the present embodiment, with the current device technology, it is difficult to directly measure the hypothalamic temperature, and it is dealt with by measuring the eardrum temperature, but it is not limited to this case. For example, when it becomes possible to directly measure the hypothalamic temperature in the future, it can also be dealt with by that device.

[0035] In FIG. 2C, the drawing described in "Health Promotion, 1st Edition, written by Masashi Wada" is cited and a part of it is processed and shown.

[0036] As shown in the figure, for example, when a load due to an operation with a high difficulty in the second classification of the service analysis is applied to the operator P, during the warning reaction period in a state of physical excitement, the brain temperature changes drastically. And it is considered that the eardrum temperature reflects the above change via the internal carotid artery flowing to the hypothalamus.

[0037] As described above, the validity verification of the operator burden determination method using the operator burden determination device 10 of the present embodiment is to apply the function of the hypothalamus, which appears as an influence of the load during work on the brain, to this investigation according to the findings of the immediate change in the eardrum temperature obtained in the preliminary investigation, enabling visualization and quantification of the operator burden during the process of work proficiency.

[0038] FIG. 3 shows an example of the data storage status in the arithmetic control unit 13 of the operator burden determination device 10. As shown in the figure, using the Excel function, on the first sheet, for the same operation repeatedly performed multiple times, for each single operation, the operation start time, the operation required time T, the eardrum temperature fluctuation coefficient CV, the target time, etc. are stored in association with each other.

[0039] Also, in order to calculate the eardrum temperature fluctuation coefficient CV, on the second sheet, for each single operation, the measurement data of the eardrum temperature measured at a rate of once every 2 seconds, for example, from the operation start time is stored. Then, after the end of a single operation, the arithmetic control unit 13 calculates the standard deviation and the average value by using the Excel function with the above measurement data, calculates the eardrum temperature fluctuation coefficient CV of a single operation, and stores it in the first sheet. Incidentally, the arithmetic control unit 13 of the present embodiment corresponds to the first calculation means of the present invention. Further, when a workload is applied to the brain, minute fluctuations occur in the eardrum temperature of the operator P, but these minute fluctuations in the eardrum temperature attenuate within just a few seconds. Therefore, if the measurement interval of the eardrum temperature becomes longer than 5 seconds, it becomes difficult to detect the influence of the workload on the brain. Therefore, in the present embodiment, the measurement interval by the eardrum temperature measurement unit 11 can be arbitrarily changed to 5 seconds or less, and by performing more detailed temperature measurement, the determination accuracy of the operator burden determination device 10 can be improved.

[0040] Next, with reference to FIGS. 4A to 6, an operator burden determination method regarding the screwing work training by the operator burden determination device 10 at the above-mentioned acceptance education site will be described. FIGS. 4A and 4B are graphs for explaining the change in the eardrum temperature of the operator P in the screwing work training using the operator burden determination device 10 of the present embodiment. FIG. 5 is a graph for explaining the correlation coefficients r1 and r2 between the number of operations for the screwing work training, the eardrum temperature fluctuation coefficient CV, and the operation required time T using the operator burden determination device 10 of the present embodiment. FIG. 6 is a graph for explaining the correlation between the operation required time T and the eardrum temperature fluctuation coefficient CV using the operator burden determination device 10 of the present embodiment.

[0041] First, screw-driving training involves, for example, the task of fastening the housing of an electronic device or the like with screws. In the screw-driving training, worker P mainly aligns the front and back sides of the housing, inserts screws into screw insertion holes provided in the housing, and then uses a tool such as an electric screwdriver to fasten the screws. In other words, the actions included in the screw-driving training are mainly composed of actions that belong to the first category of the above-mentioned Serblick analysis.

[0042] Figure 4A shows the change in eardrum temperature during the initial stage of screw-driving training. As shown in the figure, small fluctuations in eardrum temperature were observed within a range of approximately 36.4°C to 36.8°C, but no large fluctuations were observed, and it was confirmed that the temperature remained stable. Furthermore, for worker P, a slightly large temperature fluctuation in eardrum temperature was observed immediately after the start of the work.

[0043] Figure 4B shows the change in eardrum temperature during the final stage of the screw-driving training. As shown in the figure, small fluctuations in eardrum temperature were observed within the range of approximately 36.5°C to 36.7°C, but no major changes were observed, and it was confirmed that the temperature remained stable.

[0044] In Figure 5, the horizontal axis represents the number of tasks performed in screw tightening training, and the vertical axis represents the coefficient of variation of eardrum temperature CV and the required time for the task T. This graph was created using a power approximation formula in Excel. In Figure 5, the white circles represent the coefficient of variation of eardrum temperature CV according to the number of tasks performed, and the black circles represent the required time for the task T according to the number of tasks performed.

[0045] As shown in the figure, the power approximation formula for the number of operations and the eardrum temperature variation coefficient CV is Y1 = 0.0016X1 -0.214 The correlation coefficient r1 was calculated as -0.6218. On the other hand, the power approximation formula for the number of tasks and the required time for the task T is Y2=74.878X2 -0.056Due to the correlation relationship, a correlation coefficient r2 of -0.5925 was calculated. Note that the arithmetic control unit 13 of the present embodiment corresponds to the second calculation means of the present invention.

[0046] Here, in the operator burden determination device 10 of the present embodiment, with reference to the following general correlation determination criteria, a first threshold value for determining the operator burden from the above correlation coefficients r1 and r2 is set to -0.5. Note that the set value of the first threshold value is not limited to the case of setting it to -0.5, and it can be arbitrarily set in consideration of individual work contents such as the difficulty level of work training. Also, in setting the first threshold value, for example, when determining the correlation based on the correlation coefficient r1 of the transition of the tympanic membrane temperature variation coefficient CV accompanying the number of operations, or when determining the correlation between a plurality of data of the correlation coefficient r1 and a certain variable, it also becomes the object of setting the first threshold value.

[0047] Generally, when determining a correlation relationship, in the case of a negative correlation, the following relationship is known. -1.0 ≦ r ≦ -0.7: There is a high correlation -0.7 ≦ r ≦ -0.5: There is a quite high correlation -0.5 ≦ r ≦ -0.4: There is a medium correlation -0.4 ≦ r ≦ -0.3: There is a certain degree of correlation -0.3 ≦ r ≦ -0.2: There is a weak correlation -0.2 ≦ r ≦ -0.1: There is almost no correlation

[0048] Therefore, in the arithmetic control unit 13 of the operator burden determination device 10, when r1 ≦ -0.5, it is determined that there is a high correlation between the number of operations and the tympanic membrane temperature variation coefficient CV, the operator burden is small, and a first determination is made that "Operator P is suitable for the target work training". On the other hand, when r1 > -0.5, it is determined that there is a low correlation between the number of operations and the tympanic membrane temperature variation coefficient CV, the operator burden is large, and a second determination is made that "Operator P is not suitable for the target work training".

[0049] Similarly, in the arithmetic control unit 13 of the worker burden determination device 10, when r2 ≤ -0.5, it is determined that there is a high correlation between the number of operations and the operation required time T, the work proficiency of the worker P is high, the worker burden is small, and the above first determination is made. On the other hand, when r2 > -0.5, it is determined that there is a weak correlation between the number of operations and the operation required time T, the work proficiency of the worker P is low, the worker burden is large, and the above second determination is made.

[0050] As described above, in the case of the worker P for whom the results shown in FIG. 5 are obtained, the arithmetic control unit 13 makes a first determination that the worker P is suitable for the screw tightening work training by determining that the correlation coefficient r1 ≤ -0.5 and the correlation coefficient r2 ≤ -0.5.

[0051] Furthermore, in the arithmetic control unit 13, in order to improve the accuracy of the determination result of the worker P, even when determining whether or not the CV value of the eardrum temperature fluctuation coefficient CV converges to be equal to or less than a second threshold value in the correlation between the number of operations shown in FIG. 5 and the eardrum temperature fluctuation coefficient CV, it may be the case.

[0052] In the arithmetic control unit 13, 0.001 is set as the above second threshold value as a determination criterion. As described above, as the eardrum temperature measurement unit 11, an eardrum thermometer 16 with a product number BL100 manufactured by Technonext Co., Ltd. is used. And the measurement variation rate of the eardrum temperature by the above eardrum thermometer 16 is 0.1% according to the manufacturer's specifications. Therefore, it can be considered that the worker burden is alleviated when the eardrum temperature fluctuation coefficient CV shown in FIG. 5 falls within this 0.1%.

[0053] In addition, as a result of the interview with the worker P, from the worker P in whom the CV value of the eardrum temperature fluctuation coefficient CV converges to be equal to or less than the second threshold value with respect to the number of operations, impressions that the worker does not feel a burden on the work training as a work load are obtained. Also, the set value of the second threshold value is not limited to the case of setting it to 0.001, and it can be arbitrarily set according to the measurement variation rate of the eardrum temperature by the eardrum thermometer 16 to be used.

[0054] As described above, in the case of worker P who obtained the results shown in FIG. 5, since the CV value of the eardrum temperature fluctuation coefficient CV converges to 0.001 or less, which is the second threshold value with respect to the number of work operations, similar to the determination result from the above-described correlation coefficients r1 and r2, worker P makes a first determination that he / she is suitable for the screw tightening work training.

[0055] Furthermore, as shown in FIG. 6, the arithmetic control unit 13 may calculate a significance level p1 from the correlation relationship between the work required time T and the eardrum temperature fluctuation coefficient CV to improve the accuracy of the determination result of worker P. Note that the arithmetic control unit 13 of the present embodiment corresponds to the third calculation means of the present invention.

[0056] In FIG. 6, the work required time T is set on the horizontal axis, the eardrum temperature fluctuation coefficient CV is set on the vertical axis, and a graph created using the linear approximation formula of the Excel function is shown. As shown in the figure, in the correlation relationship based on the linear approximation formula between the work required time T and the eardrum temperature fluctuation coefficient CV, 0.032689 is calculated as the significance level p1.

[0057] Here, the arithmetic control unit 13 sets 0.05 as the third threshold value for determining the worker burden from the above significance level p1. Note that the set value of the third threshold value is not limited to 0.05, and it can be arbitrarily set in consideration of individual work contents such as the difficulty level of the work training.

[0058] As described above, in the case of worker P who obtained the results shown in FIG. 5, the arithmetic control unit 13 determines that p1 ≤ 0.05, and determines that the correlation coefficients r1, r2, and CV values used in the above work determination are significant numerical values, and maintains the above determination result.

[0059] As described above with reference to FIGS. 4A to 6, the screw tightening work training is constituted by the operation of the first classification of the serviceability analysis, and does not require the operator P himself to think, but is a simple operation. Therefore, it is considered that no significant displacement was observed in the eardrum temperature even when the same operation was repeated. That is, as described with reference to FIG. 2C, the screw tightening work training is considered to impose a small work load on the operator P, make it difficult for the brain to feel stress during the warning reaction period, and cause the displacement of the brain temperature to change within a small range. And the work constituted by the first classification of the serviceability analysis, such as the screw tightening work training, is considered to have a low work load and be an operation that is easy to adapt to new operators P and the like.

[0060] Next, with reference to FIGS. 7A to 9, a method for determining the operator burden regarding the harness wiring work training by the operator burden determination device 10 at the site of the above-mentioned acceptance education will be described. FIGS. 7A and 7B are graphs for explaining the change in the eardrum temperature of the operator P in the harness wiring work training using the operator burden determination device 10 of the present embodiment. FIG. 8 is a graph for explaining the correlation between the number of operations for the harness wiring work training, the coefficient of variation CV of the eardrum temperature, and the required work time T using the operator burden determination device 10 of the present embodiment. FIG. 9 is a graph for explaining the correlation between the required work time T and the coefficient of variation CV of the eardrum temperature using the operator burden determination device 10 of the present embodiment.

[0061] First, the harness wiring work training is, for example, an operation of electrically connecting electronic components inside a housing of an electronic device or the like with a harness. In the harness wiring work training, the operator P mainly prepares a housing on the back side where an electronic board or the like is fixed and a harness to be wired, searches for a connection point of the harness and a point to fix the harness, and uses a tool to fix the harness to the electronic board or the electronic component. That is, the operations included in the harness wiring work training are configured as a series of operations in which about 30% of the second classification is attached to the first classification of the serviceability analysis. As a result, the harness wiring work training has a higher difficulty level of the work content than the above-mentioned screw tightening work training, and the work load on the operator P also increases.

[0062] In FIG. 7A, the change in eardrum temperature at the initial stage of the number of work operations of the harness wiring work training is shown. As shown in the figure, within the range of approximately 36.0 degrees to 36.8 degrees, the eardrum temperature generally changes with fine displacements, but there are some significant shifts in the eardrum temperature here and there. From the above, in a series of operations where the second classification is attached to the first classification of the service analysis, a workload is applied to the brain and the eardrum temperature of the operator P is displaced. However, when the above series of operations can be performed without hesitation, it was confirmed that the eardrum temperature of the operator P is changing in a stable state. Note that the above series of operations means that, for example, when wiring the harness, operations such as "while paying attention (while checking) so that the harness does not come off from the claw portion of the housing" as the second classification and "determining the position of the harness" as the first classification are performed continuously without being separated.

[0063] In FIG. 7B, the change in eardrum temperature at the final stage of the number of work operations of the harness wiring work training is shown. As shown in the figure, within the range of approximately 36.4 degrees to 36.8 degrees, the eardrum temperature generally changes with fine displacements, but as shown by the solid circle 21 and its frame 24, there is a difference in the change in the eardrum temperature towards the end of the work training. From the above, similar to the view in FIG. 7A, it was confirmed that a workload is applied to the brain during the above series of operations of the service analysis, and some stress such as psychological stress is applied to the brain of the operator P towards the end of the work training.

[0064] In FIG. 8, the number of work operations for the harness wiring work training is set on the horizontal axis, and the coefficient of variation of eardrum temperature CV and the required work time T are set on the vertical axis respectively, and a graph created using the power approximation formula of the Excel function is shown. Note that in FIG. 8, the white solid circles indicate the coefficient of variation of eardrum temperature CV corresponding to the above number of work operations, and the black solid circles indicate the required work time T corresponding to the above number of work operations.

[0065] As shown in the figure, in the power approximation formula between the above number of work operations and the coefficient of variation of eardrum temperature CV, Y1 = 0.002X1 -0.243Based on the correlation relationship, a correlation coefficient r1 of -0.3651 was calculated. On the other hand, in the power approximation formula between the number of operations and the operation time T, Y2 = 49.662X2 -0.343 Based on the correlation relationship, a correlation coefficient r2 of -0.8284 was calculated.

[0066] As described above, in the case of operator P who obtained the results shown in FIG. 8, the arithmetic control unit 13 determines that the correlation coefficient r1 > -0.5 and the correlation coefficient r2 ≤ -0.5, and makes the above-mentioned second determination that operator P is not suitable for the harness wiring work training.

[0067] Furthermore, in the arithmetic control unit 13, in order to improve the accuracy of the determination result of operator P, in the correlation relationship between the number of operations shown in FIG. 8 and the eardrum temperature fluctuation coefficient CV, it may also be determined whether the CV value of the eardrum temperature fluctuation coefficient CV converges to a value equal to or less than a second threshold with respect to the number of operations.

[0068] As described above, in the case of operator P who obtained the results shown in FIG. 8, the arithmetic control unit 13 determines that most of the CV values of the eardrum temperature fluctuation coefficient CV converge to 0.001 or less, which is the second threshold. However, as indicated by the circled marks 22, 23, some of the CV values increase significantly with respect to the second threshold, and makes the second determination that operator P is not suitable for the harness wiring work training.

[0069] Furthermore, in the arithmetic control unit 13, as shown in FIG. 9, a significance level p1 may be calculated from the correlation relationship between the operation time T and the eardrum temperature fluctuation coefficient CV to improve the accuracy of the determination result of operator P.

[0070] As shown in Fig. 9, in the correlation relationship based on the linear approximation formula between the required working time T and the tympanic membrane temperature fluctuation coefficient CV, a significance level p1 of 0.11577 was calculated. As described above, in the case of the worker P for whom the results shown in Fig. 9 were obtained, the arithmetic control unit 13 calculates that the significance level p1 > 0.05, and determines that the correlation coefficients r1, r2, and CV values used in the above-described work determination are not significant values. Thereafter, the manager, who is the supervisor of the worker P, conducts an interview with the worker P to investigate the cause while looking at the visualized determination result. If the cause cannot be traced as a result of the interview, it may be possible to recommend harness wiring work training to the worker P again. On the other hand, if the cause is identified between the worker P and the worker P is convinced, the work training may be terminated as it is.

[0071] As described above with reference to FIGS. 7A to 9, the harness wiring work training is configured as a series of operations in which about 30% of the second classification is accompanied by the first classification of the service analysis. And in the above series of operations, since thinking by the worker P himself is required, it is considered that a large displacement of the tympanic membrane temperature was seen especially in the initial stage of the number of work times when the worker is not used to the work while repeating the same work. That is, as described with reference to Fig. 2C, it is considered that the harness wiring work training has a large work load, the brain is likely to feel stress during the warning reaction period, and the displacement of the brain temperature also changes greatly. And in work including a series of operations of service analysis such as harness wiring work training, it is easy for the worker P to appear whether he / she is suitable or not. As a result, when assigning the worker P to a process, the manager can investigate the cause and consider countermeasures by conducting an interview with the worker P while looking at the visualized determination result.

[0072] In the case of worker P described above with reference to Figures 7A to 9, a post-work training interview revealed that six months had passed since worker P's return to work and that worker P had continued working six hours a day. The final stage of the work training measurement, indicated by circle 21 and box 24 (see Figure 7B), was measured after the six-hour shift, suggesting that changes in living environment had had an impact. Worker P also reported that he began to feel a decline in his ability to concentrate around the time he exceeded the six-hour shift. In other words, by visualizing worker burden using the worker burden assessment device 10, interviews between the manager and worker P facilitate an understanding of worker P's mental and physical burden, which in turn facilitates improving the working environment by optimizing personnel allocation on the production line.

[0073] Next, a worker workload determination method for harness wiring work training using the worker workload determination device 10 at the site of the above-mentioned accepted training will be described with reference to Figures 10A to 12. Figures 10A and 10B are graphs illustrating the change in eardrum temperature of worker P during harness wiring work training, using the worker workload determination device 10 of this embodiment. Figure 11 is a graph illustrating the correlation between the number of tasks for harness wiring work training and the eardrum temperature variation coefficient CV and the required work time T, using the worker workload determination device 10 of this embodiment. Figure 12 is a graph illustrating the correlation between the required work time T and the eardrum temperature variation coefficient CV, using the worker workload determination device 10 of this embodiment.

[0074] The graphs shown in Figures 10A to 12 were created based on data measured by the same worker P described using Figures 7A to 9, with his / her consent, after the normal working hours had elapsed. The work training shown in Figures 10A to 12 has the same content as the work training shown in Figures 7A to 9, and the data measurement methods and data calculation methods are also the same. When describing Figures 10A to 12, the descriptions of Figures 7A to 9 will be referenced as appropriate. The work training was conducted immediately after the work training shown in Figures 7A to 9.

[0075] As shown in FIGS. 10A and 10B, although the operator P should be expected to become accustomed to the above work training according to the number of times of work, significant shifts in eardrum temperature frequently occur even at the initial and final stages of the number of times of work in the harness wiring work training, and furthermore, the fluctuation range is expanding. Also, from the start to the end of the work training, an overall increase in eardrum temperature is observed, but since it is a gradual increase, it is considered to be due to the circadian rhythm. Note that since this upward transition is different from the minute fluctuations such as the above-mentioned transition of eardrum temperature, it is considered not to deteriorate the CV value of the eardrum temperature variation coefficient CV.

[0076] As shown in FIG. 11, in the power approximation formula between the above number of work times and the eardrum temperature variation coefficient CV, Y1 = 0.0013X1 0.1101 Based on the correlation relationship, a correlation coefficient r1 of 0.3127 was calculated. On the other hand, in the power approximation formula between the above number of work times and the required work time T, Y2 = 37.563X2 -0.167 Based on the correlation relationship, a correlation coefficient r2 of -0.8052 was calculated.

[0077] As described above, the arithmetic control unit 13 makes the above-mentioned second determination that the operator P is not suitable for the harness wiring work training by determining that the correlation coefficient r1 > -0.5 and the correlation coefficient r2 ≤ -0.5.

[0078] As shown in FIG. 12, in the correlation relationship based on the linear approximation formula between the required work time T and the eardrum temperature variation coefficient CV, a significance level p1 of 0.293968 was calculated. As described above, the arithmetic control unit 13 determines that the correlation coefficients r1, r2, and CV values used in the above-mentioned work determination are not significant numerical values by calculating that the significance level p1 > 0.05.

[0079] As described above, the work training was carried out with the consent of the worker P. In particular, in the case of the correlation coefficient r1, a negative correlation was expected under normal circumstances, but a positive correlation was obtained, and a result significantly deviated from the first threshold value was obtained. Furthermore, most of the CV values of the eardrum temperature fluctuation coefficient CV did not converge to 0.001 or less, which is the second threshold value, and a result significantly increased with respect to the first threshold value was obtained.

[0080] On the other hand, the correlation coefficient r2 satisfies the condition of -0.5 or less, and the required work time T is shortened each time the number of times is repeated, and a result of generally clearing the target time is obtained. From this judgment result, it is considered that the worker burden does not necessarily decrease with the work proficiency time, so it is difficult to judge the work training effect only by the work proficiency time, and it is considered necessary to consider the occupational preparedness for the application of the process arrangement of the production line.

[0081] Incidentally, the occupational preparedness means a state in which the conditions necessary for the worker P to start (including resuming) his / her professional life are prepared. And in the case of the worker P this time, rather than being incompatible with the above work training, although the consent of the worker P was obtained, the implementation of the work training outside the working hours desired by the worker P did not prepare the occupational preparedness of the worker P, and it is considered that the decrease in concentration and the like had an impact.

[0082] Next, with reference to FIG. 13, a method for calculating the predicted number of work times X1 and X2 until the target time for work training by the work load determination device 10 is achieved at the site of the above-mentioned acceptance education will be described. FIG. 13 is a graph for explaining the correlation between the number of work times for work training, the eardrum temperature fluctuation coefficient CV, and the required work time T using the work load determination device 10 of the present embodiment. Incidentally, as the work training in FIG. 13, those with different contents from the above-mentioned screw tightening work training and harness wiring work training are used.

[0083] As shown in FIG. 13, in the arithmetic control unit 13, when using the correlation based on the eardrum temperature fluctuation coefficient CV, since the eardrum temperature measuring device 16 is used, the target eardrum temperature fluctuation coefficient is set to Y1 = 0.001, and X1 = (0.001 / 0.0043) (1 / -0.274) From this, X1 is calculated as 205 times. Incidentally, for this operator P, Y1 = 0.0043X1 -0.274 Based on the correlation relationship, the correlation coefficient r1 is calculated as -0.5714.

[0084] On the other hand, in the arithmetic control unit 13, when using the correlation based on the required work time T, the target time is set to Y2 = 413 seconds, and X2 = (413 / 930.21) (1 / -0.156) From this, X2 is calculated as 182 times. Incidentally, for this operator P, Y2 = 930.21X2 -0.156 Based on the correlation relationship, the correlation coefficient r2 is calculated as -0.8979.

[0085] From the above, when the administrator has an interview with the operator P, by explaining that the first determination by the operator burden determination device 10 has been made, and that there is a possibility of clearing the target time by repeating the same work about 182 to 205 times as the predicted number of work times, the motivation of the operator P can also be enhanced.

[0086] Next, using FIG. 14, the personal ability of the operator P is determined using the above-described operator burden determination method, and the process to appropriate personnel allocation to the production line is explained. In this explanation, the explanations using FIGS. 1 to 13 are appropriately referred to, and repeated explanations are omitted. FIG. 14 is a flowchart for explaining appropriate personnel allocation to the production line using the above-described operator burden determination method.

[0087] Therefore, in this embodiment, for the purpose of the administrator appropriately allocating the operator P to the production line for starting a new model, for example, the administrator presets a plurality of work trainings along each work process for starting a new model and assigns them to the operator P, so that the personal ability of the operator P can be grasped before the process allocation.

[0088] As shown in FIG. 14, in step S11, the administrator explains to the operator P the contents of a plurality of preset work trainings. As an example of the work training, there are the screw tightening work training and the harness wiring work training described with reference to FIGS. 4A to 12.

[0089] In step S12, the operator P starts the above work training. The operator P aims to complete one same work within the set target time, and repeats the above work training multiple times.

[0090] In step S13, in the working time measurement unit 12 of the operator burden determination device 10, the working time T required for each same work of the operator P is measured, and the measurement data is transmitted to the arithmetic control unit 13 via the communication network 15. Then, in the arithmetic control unit 13, as described with reference to FIG. 3, the measurement data of the working time T is stored in association with various data for each number of works.

[0091] In step S14, in the eardrum temperature measurement unit 11 of the operator burden determination device 10, the eardrum temperature of the operator P is measured multiple times from the start to the end of one same work. Then, the eardrum temperature measurement unit 11 transmits the measured data to the arithmetic control unit 13 via the communication network 15. Then, in the arithmetic control unit 13, as described with reference to FIG. 3, the measurement data of the eardrum temperature is stored for each number of works.

[0092] In step S15, the arithmetic control unit 13 calculates the eardrum temperature variation coefficient CV for each number of works from the above measurement data stored in step S14. Then, the arithmetic control unit 13 stores the working time T and the eardrum temperature variation coefficient CV in association with each other for each same work.

[0093] In step S16, the arithmetic control unit 13 calculates the correlation coefficient r1 for the transition of the eardrum temperature variation coefficient CV with respect to the number of work operations of the same repeated work using the power approximation formula of the Excel function, the correlation coefficient r2 for the transition of the working time per operation with respect to the number of work operations of the same work, and the like.

[0094] In step S17, the arithmetic control unit 13 compares the correlation coefficients r1 and r2 calculated in step S16 with the first threshold value to determine the operator burden such as the work proficiency of the operator P. As described above, when both values of the correlation coefficients r1 and r2 are -0.5 or less, a first determination is made that "operator P is suitable for the target work training". On the other hand, when at least one of the correlation coefficients r1 and r2 is greater than -0.5, a second determination is made that "operator P is not suitable for the target work training".

[0095] Note that in the arithmetic control unit 13 in step S17, as described above, the significance level p1 may be further calculated, and the CV value and the significance level p1 may be compared with the second threshold value and the third threshold value, respectively, to improve the determination accuracy.

[0096] In the YES of step S17, when the arithmetic control unit 13 makes the above first determination, the process proceeds to step S18. Then, in step S18, the administrator conducts an interview with operator P regarding the determination result while referring to the visualized first determination, the graph of the power approximation formula, and the like.

[0097] In step S19, the administrator explains to operator P the arrangement of the operator P in one process of the production line for the startup of the new model during the above interview, and after confirming that the work is also suitable for the feeling of the operator P, the process in which the operator P is arranged is determined.

[0098] On the other hand, when the operation control unit 13 makes the above second determination at NO in step S17, the process proceeds to step S20. Then, in step S20, the administrator has an interview with the worker P about the determination result while referring to the visualized second determination, the graph of the power approximation formula, etc.

[0099] In step S20, during the above interview, the administrator, together with the worker P, traces the cause of the second determination and discusses an improvement plan for the cause. For example, as described with reference to FIGS. 7A to 12, when it is concluded that the worker P is suitable for the work content but the second determination is made due to differences in the living environment, it can be confirmed by both parties that corresponding measures can be taken by improving the working hours, etc.

[0100] If, in step S20, a YES, an improvement plan can be confirmed between the administrator and the worker P regarding the content of the work training, the process proceeds to step S19. Then, in step S19, the administrator explains to the worker P the assignment to one process of the production line for the new model launch, and after confirming that the work is also suitable for the worker P's perception, determines the process to which the worker P will be assigned.

[0101] On the other hand, when the determination in step S20 is NO, the administrator, together with the worker P, traces the cause of the second determination and discusses an improvement plan for the cause during the above interview. For example, there may be cases where the personal ability of the worker P does not match the content of the work training, such as the number of times the required work time T cannot reach the above target time even at the final stage of the work training, or the correlation coefficient r1 is greater than -0.5 and is a value far from -0.5.

[0102] In this case, it is difficult for the manager to find an improvement plan with the worker P, and the process proceeds to step S11. Then, in step S11, the manager explains the content of the different work training to the worker P and tries to find a work that suits the worker P. Thereafter, the manager and the worker P grasp the individual ability by changing the content of the work training in the calculation control unit 13 until the first judgment is made for the worker P, or even if the second judgment is made, an improvement plan is found.

[0103] 14 illustrates a case where a worker P is given work training before process allocation, and the individual capabilities of the worker P are grasped and then process allocation is performed, but the present invention is not limited to this case. For example, even after process allocation to a production line for launching a new model, the worker workload determination device 10 may periodically or irregularly determine the worker workload using actual process work. As the worker P becomes accustomed to the process work or as a result of subsequent changes in the living environment, the worker P may feel burdened by the current work content. In such a case, the work environment can be improved by reassigning the process that the worker P is responsible for.

[0104] In the above-described embodiment, when a worker P repeatedly performs the same task, the worker burden is determined using the correlation coefficient r1 for the change in the tympanic membrane temperature variation coefficient CV relative to the number of tasks performed by the worker P and the correlation coefficient r2 for the change in the task time T relative to the number of tasks performed by the worker P, but this is not limited to this case.

[0105] In this embodiment, as an example of the same task, the case where, for example, screwing operations or harness wiring operations are repeatedly performed within working hours on a production line in a factory has been described, but the case is not limited thereto. As the same task, for example, in a delivery truck, a taxi, a bus, a train, etc., the behavior of a vehicle driver repeatedly performing article delivery or passenger delivery is included, and in this case, the above driver is also targeted as the worker P. Further, as the same task, for example, in a cram school or an online class, etc., the behavior of a learner such as a child performing thinking operations such as arithmetic calculations or reading comprehension in Japanese during class time is included, and in this case, the above learner is also targeted as the worker P.

[0106] In this case, as the number of operations in this embodiment, for example, it is handled by dividing the total driving time or the total learning time into units of 5 seconds to 30 seconds. At least the tympanic membrane temperature fluctuation coefficient CV is measured for each number of operations, and the correlation coefficient r1 is calculated using a power approximation formula. Then, by comparing the correlation coefficient r1 with the first threshold value, the worker burden can be determined. When the worker P is a driver, the stress state during driving is determined as the worker burden. For example, on a mountain road or a highway where curves are continuous, in a state where the driver's tension increases, the tympanic membrane temperature tends to rise. The worker burden determination device 10 detects the deterioration of the correlation coefficient r1 and performs real-time feedback to the driver, thereby achieving thorough safe driving. Further, when the worker P is a driver or a learner, for example, in a state of being attacked by drowsiness, the tympanic membrane temperature tends to drop. The worker burden determination device 10 detects the deterioration of the correlation coefficient r1 and performs real-time feedback to the driver or the learner, thereby preventing drowsy driving and preventing distracted attention during learning.

[0107] In addition, in the worker burden determination device 10 of this embodiment, in operations where a load is applied to the upper limb of the worker P, such as during the above-described work training, during vehicle driving, and during learning, various design changes can be made without departing from the gist of the present invention when it is expected to reduce the burden on the worker P by improving those environments at regular intervals with the passage of the number of operations.

Explanation of Reference Numerals

[0108] 10. Worker workload determination device 11 Eardrum temperature measurement section 12 Work time measurement unit 13 Calculation control unit 13A Personal Computer 13B Server 14 Display section 15. Communication Network 16 Eardrum temperature measuring device 17 Touch Panel 18 monitors P worker CV Coefficient of variation of eardrum temperature T Time required r1,r2 correlation coefficient p1 significance level

Claims

1. An operator burden determination device that determines the operator burden on the same operation of the operator when the operator repeats the same operation, comprising: a working time measurement unit that measures the working time T required for one operation while the operator performs the same operation once and obtains working time data, and an arithmetic control unit; The arithmetic control unit: calculates a correlation coefficient r2 for the transition of the working time T required for one operation with respect to the number of repetitions of the same operation; compares the correlation coefficient r2 with a preset first threshold value, and when the correlation coefficient r2 is less than or equal to the first threshold value, makes a first determination that the operator burden due to the same operation is small, and when the correlation coefficient r2 is greater than the first threshold value, makes a second determination that the operator burden due to the same operation is large. An operator burden determination device characterized by performing the above.

2. The arithmetic control unit: calculates the correlation coefficient r2 using a power approximation formula, a logarithmic approximation formula, or a linear approximation formula in predicting the number of operations until the target time of the working time T is achieved. The operator burden determination device according to claim 1.

3. A tympanic membrane temperature measurement unit that measures the tympanic membrane temperature, which reflects the temperature of the hypothalamus of the operator, a plurality of times while the operator performs the same operation once and obtains temperature data, and an arithmetic control unit that calculates a tympanic membrane temperature variation coefficient CV for each number of operations of the same operation using the temperature data transmitted from the tympanic membrane temperature measurement unit. Further comprising: The arithmetic control unit: calculates a correlation coefficient r1 for the transition of the tympanic membrane temperature variation coefficient CV with respect to the number of operations of the same operation; calculates the correlation coefficient r1 using the power approximation formula, the logarithmic approximation formula, or the linear approximation formula in predicting the number of operations until the target tympanic membrane temperature variation coefficient of the tympanic membrane temperature variation coefficient CV is achieved; compares the correlation coefficient r1 with the first threshold value, and when both the correlation coefficient r1 and the correlation coefficient r2 are less than or equal to the first threshold value, makes the first determination, and when at least one of the correlation coefficient r1 and the correlation coefficient r2 is greater than the first threshold value, makes the second determination. The operator burden determination device according to claim 2, characterized by performing the above.

4. The arithmetic control unit: When the CV value of the tympanic membrane temperature variation coefficient CV does not converge to a value equal to or less than a second threshold value preset for the number of work operations, the second determination is performed regardless of the first determination. The worker burden determination device according to claim 3, characterized in that.

5. The arithmetic control unit Calculates a significance level p1 for the transition of the tympanic membrane temperature variation coefficient CV associated with the work required time T with respect to the number of work operations of the same work, When the significance level p1 is greater than a third threshold value preset, the second determination is performed regardless of the first determination. The worker burden determination device according to claim 4, characterized in that.

6. A worker burden determination method in which, when a worker repeatedly performs the same work, a worker burden determination device determines the worker burden on the same work of the worker. A data acquisition step in which a work required time measurement unit of the worker burden determination device measures a single work required time T while the worker performs the same work once and acquires work required time data, Calculating a correlation coefficient r2 for the transition of the single work required time T with respect to the number of work operations of the same work, The correlation coefficient r2 is compared with a first threshold value preset. When the correlation coefficient r2 is equal to or less than the first threshold value, a first determination is made that the worker burden due to the same work is small. When the correlation coefficient r2 is greater than the first threshold value, a second determination is made that the worker burden due to the same work is large. A worker burden determination method characterized by that.

7. The arithmetic control unit of the worker burden determination device In predicting the number of work operations until the target time of the work required time T is achieved, the correlation coefficient r2 is calculated using a power approximation formula, a logarithmic approximation formula, or a linear approximation formula. The worker burden determination method according to claim 6, characterized in that.

8. In the data acquisition step, in addition to the work required time data, a tympanic membrane temperature measurement unit of the worker burden determination device measures a tympanic membrane temperature reflecting the temperature of the hypothalamus of the worker a plurality of times while the worker performs the same work once, and acquires temperature data of the worker. A data acquisition step, A step in which an arithmetic control unit of the worker burden determination device calculates a tympanic membrane temperature variation coefficient CV from the temperature data for each number of work operations of the same work, a step in which the arithmetic control unit of the operator burden determination device calculates a correlation coefficient r1 for the transition of the tympanic membrane temperature variation coefficient CV with respect to the number of operations of the same operation; a determination step in which the arithmetic control unit of the operator burden determination device determines the operator burden for the same operation using the tympanic membrane temperature variation coefficient CV and the correlation coefficient r1; and In predicting the number of operations until the target tympanic membrane temperature variation coefficient of the tympanic membrane temperature variation coefficient CV is achieved, the correlation coefficient r1 is calculated using the power approximation formula, the logarithmic approximation formula, or the linear approximation formula. The correlation coefficient r1 is compared with the first threshold value. When the correlation coefficient r1 and the correlation coefficient r2 are less than or equal to the first threshold value, the first determination is made. When at least one of the correlation coefficient r1 and the correlation coefficient r2 is greater than the first threshold value, the second determination is made. The operator burden determination method according to claim 7, characterized in that.

9. The arithmetic control unit of the operator burden determination device has a complementary determination step for complementing the first determination by the correlation coefficient r1. In the complementary determination step, when the CV value of the tympanic membrane temperature variation coefficient CV does not converge to a value equal to or less than a second threshold value set in advance with respect to the number of operations, the second determination is made regardless of the first determination. The operator burden determination method according to claim 8, characterized in that.

10. The arithmetic control unit of the operator burden determination device has a third calculation step of calculating a significance level p1 for the transition of the tympanic membrane temperature variation coefficient CV with respect to the operation time T for the number of operations of the same operation. In the complementary determination step, when the significance level p1 is greater than a third threshold value set in advance, the second determination is made regardless of the first determination. The operator burden determination method according to claim 9, characterized in that.

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