Skill Evaluation Device

Abstract

To provide a skill evaluation method and a skill evaluation apparatus configured to properly evaluate a skill of a person to be evaluated.SOLUTION: A skill evaluation method includes: acquiring data on multiple measurement items which are different from each other and related to mental conditions of a model person and a person to be evaluated that the model person and the person to be evaluated have during a predetermined work; generating unit spatial data based on the acquired data of the model person in a unit space of Mahalanobis-Taguchi system defined with the measurement items; determining Mahalanobis distance for the generated unit spatial data, on the basis of the acquired data of the person to be evaluated; and evaluating a skill of the person to be evaluated on the basis of the determined Mahalanobis distance.SELECTED DRAWING: Figure 5

Description

Technical Field

[0001] The present invention relates to an ability evaluation device for evaluating the skill related to a predetermined operation of handling a predetermined object for an evaluation target person. Tech

Background Art

[0002] Since skills are acquired through repeated actual operations and experience, they are difficult to verbalize, document, and illustrate. Therefore, skills are difficult to convey to others and difficult to quantify. As technologies related to such skills, for example, there are the technologies disclosed in Patent Document 1.

[0003] The skill acquisition support system disclosed in Patent Document 1 is a skill acquisition support system that detects the difference between the operation of the user and the operation of the modeler. In the bodies of the modeler and the user, it is attached to a predetermined monitoring site, and a sensor that detects data related to the respective operations of the modeler and the user, an operation data acquisition means that acquires data related to the operation detected by the sensor, a storage means that records the data related to the operation acquired by the operation data acquisition means, graphing means that graphs the data related to the operation of the modeler recorded in the storage means in time series to form and display a first graph, graphs the data related to the operation of the user in time series to form and display a second graph, a work point matching means that adjusts so that a predetermined work point in the first graph and the predetermined work point in the second graph coincide on the time axis, an allowable range forming means that forms a data allowable range for the user in time series in the first graph, and a difference detecting means that detects a section that deviates from the data allowable range formed by the allowable range forming means in the second graph.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] By the way, the skill acquisition support system disclosed in Patent Document 1 adjusts so that each work point of the modeler and the user coincides on the time axis. Therefore, even if there is a deviation in the operation speed between the modeler and the user, the time deviation is not accumulated, and the operation can be appropriately evaluated. However, there is room for improvement in evaluating the skills of the person to be evaluated.

[0006] The present invention has been made in view of the above circumstances, and an object thereof is to provide a skill evaluation device that can more appropriately evaluate the skills of a person to be evaluated. Tech

Means for Solving the Problems

[0007] As a result of various studies, the present inventor has found that the above object is achieved by the following present invention. That is, a skill evaluation method according to an aspect of the present invention is a method for evaluating a skill related to a predetermined operation of handling a predetermined object for an evaluation target person, and includes a data acquisition step of acquiring data of a plurality of different measurement items related to each mental state of the modeler and the evaluation target person when each of the modeler and the evaluation target person performs the predetermined operation, a unit space generation step of generating unit space data based on the data of the modeler acquired in the data acquisition step in a unit space of the Mahalanobis distance system defined by the plurality of measurement items, a distance calculation step of obtaining a Mahalanobis distance with respect to the unit space data generated in the unit space generation step based on the data of the evaluation target person acquired in the data acquisition step, and an evaluation step of evaluating the skill of the evaluation target person based on the Mahalanobis distance obtained in the distance calculation step. Preferably, in the above skill evaluation method, in the evaluation step, as an evaluation of the skill of the evaluation target person, the Mahalanobis distance obtained in the distance calculation step is displayed on a display unit.

[0008] ​ It is presumed that the quality of the work is related to the mental state of the skilled person who performs the work. According to the inventor's experiments, findings were obtained that there are significant differences in the mental state of the skilled person who is not the model person compared to the mental state of the model person. Therefore, by comparing the mental state of the model person with the mental state of the person to be evaluated, the skills of the person to be evaluated can be more appropriately evaluated. The above skill evaluation method obtains the Mahalanobis distance of the person to be evaluated with respect to the unit space data generated based on the data of the model person in the unit space of the Mahalanobis system defined by a plurality of different measurement items related to the mental state when performing a predetermined work, and evaluates the skills of the person to be evaluated. Therefore, the skills of the person to be evaluated can be more appropriately evaluated.

[0009] In another aspect, in the above skill evaluation method, when the model person classifies the skilled person who performs the predetermined work into a plurality of classes based on the accuracy of the work and the work time from the start to the end of the work, the model person is the skilled person classified into the highest class among the plurality of classes.

[0010] Such a skill evaluation method can set a model person more appropriately because when classifying skilled persons into a plurality of classes, the skilled person classified into the highest class is used as the model person.

[0011] In another aspect, in the above skill evaluation method, the person to be evaluated is the skilled person classified into the class next to the highest class among the plurality of classes.

[0012] According to this, a skill evaluation method for evaluating the skills of a skilled person classified into the class next to the highest class can be provided.

[0013] In another aspect, in the above skill evaluation method, the unit space generation step generates unit space data based on the data of the model person in which the Mahalanobis distance converges.

[0014] Such a skill evaluation method can generate unit space data more appropriately because it generates unit space data based on the data of exemplary individuals for which the Mahalanobis distance converges.

[0015] In another aspect, in these above-described skill evaluation methods, the plurality of measurement items include at least one of the β wave of the electroencephalogram and the δ wave of the electroencephalogram.

[0016] According to the inventor's experiments, it was found that the Mahalanobis distance is particularly affected by the β wave and the δ wave of the electroencephalogram. Since the β wave is generally related to wakefulness and the δ wave is related to slow-wave sleep, there is a relationship in which the δ wave decreases as the β wave increases between the β wave and the δ wave. Therefore, it is sufficient to observe at least one of the β wave and the δ wave. Since the above-described skill evaluation method includes at least one of the β wave of the electroencephalogram and the δ wave of the electroencephalogram among the plurality of measurement items, it is possible to obtain the Mahalanobis distance reflecting the mental state related to the skill, and thus the skill of the evaluation target person can be evaluated more appropriately.

[0017] In another aspect, in these above-described skill evaluation methods, the predetermined operation is an operation of using a tool.

[0018] Such a skill evaluation method can evaluate the skill of an evaluation target person who performs an operation of using a tool.

[0019] In another aspect, in these above-described skill evaluation methods, the predetermined operation is an operation of grinding or polishing a mold.

[0020] Such a skill evaluation method can evaluate the skill of an evaluation target person who performs an operation of grinding or polishing a mold.

[0021] In another aspect, in these above-described skill evaluation methods, the mold is a mold for molding a member used in a vehicle, which is a mold for molding a vehicle member.

[0022] Such a skill evaluation method can evaluate the skills of an evaluation subject who performs grinding or polishing work on a mold for vehicle member molding.

[0023] A skill evaluation method according to another aspect of the present invention is a method for evaluating the skills of an evaluation subject related to a predetermined operation of handling a predetermined object, the method comprising: a data acquisition step of acquiring data of a plurality of different measurement items related to the mental state of the evaluation subject when the evaluation subject performs the predetermined operation; a distance calculation step of obtaining a Mahalanobis distance with respect to unit space data generated based on the data of a model person in the plurality of measurement items in a unit space of a Mahalanobis taguchi system defined by the plurality of measurement items, based on the data of the evaluation subject acquired in the data acquisition step; and an evaluation step of evaluating the skills of the evaluation subject based on the Mahalanobis distance obtained in the distance calculation step.

[0024] A skill evaluation apparatus according to another aspect of the present invention is an apparatus for evaluating the skills of an evaluation subject related to a predetermined operation of handling a predetermined object, the apparatus comprising: a data acquisition unit that acquires data of a plurality of different measurement items related to the respective mental states of the model person and the evaluation subject when the model person and the evaluation subject each perform the predetermined operation; a unit space generation unit that generates unit space data based on the data of the model person acquired by the data acquisition unit in a unit space of a Mahalanobis taguchi system defined by the plurality of measurement items; a distance calculation unit that obtains a Mahalanobis distance with respect to the unit space data generated by the unit space generation unit based on the data of the evaluation subject acquired by the data acquisition unit; and an evaluation unit that evaluates the skills of the evaluation subject based on the Mahalanobis distance obtained by the distance calculation unit.

[0025] A skill evaluation device according to another aspect of the present invention is a device that evaluates the skill of an evaluation subject related to a predetermined task of handling a predetermined object, and includes a data acquisition unit that acquires data of a plurality of different measurement items related to the mental state of the evaluation subject when the evaluation subject performs the predetermined task, and a distance calculation unit that obtains the Mahalanobis distance with respect to the unit space data generated based on the data of the model person in the plurality of measurement items in the unit space defined by the plurality of measurement items, based on the data of the evaluation subject acquired by the data acquisition unit, and an evaluation unit that evaluates the skill of the evaluation subject based on the Mahalanobis distance obtained by the distance calculation unit.

[0026] These skill evaluation methods and skill evaluation devices can more appropriately evaluate the skills of the evaluation subject.

Advantages of the Invention

[0027] According to the present invention Tech The skill evaluation device can more appropriately evaluate the skills of the evaluation subject.

Brief Description of the Drawings

[0028]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8

Figure 9

Figure 10

Embodiments for Carrying Out the Invention

[0029] Hereinafter, one or more embodiments of the present invention will be described with reference to the drawings. However, the scope of the invention is not limited to the disclosed embodiments. In each figure, components denoted by the same reference numerals are the same components, and the description thereof will be omitted as appropriate. In this specification, when referring generically, reference numerals without subscripts are used, and when referring to individual components, reference numerals with subscripts are used.

[0030] The skill evaluation method in the embodiment is a method for evaluating the skill related to a predetermined operation of handling a predetermined object for an evaluation target person, including a data acquisition step of acquiring data of a plurality of different measurement items related to the respective mental states of the model person and the evaluation target person when each of the model person and the evaluation target person performs the predetermined operation, a unit space generation step of generating unit space data based on the data of the model person acquired in the data acquisition step in the unit space of the Mahalanobis distance system defined by the plurality of measurement items, a distance calculation step of obtaining the Mahalanobis distance with respect to the unit space data generated in the unit space generation step based on the data of the evaluation target person acquired in the data acquisition step, and an evaluation step of evaluating the skill of the evaluation target person based on the Mahalanobis distance obtained in the distance calculation step. Hereinafter, a skill evaluation apparatus implementing such a skill evaluation method will be described more specifically as an example.

[0031] FIG. 1 is a block diagram showing the configuration of a skill evaluation apparatus according to an embodiment. FIG. 2 is a diagram showing, as an example, the measurement state of electrocardiogram data and electrocardiogram data. FIG. 2A shows a state in which electrodes of an electrocardiograph are attached to a skilled person to measure electrocardiogram data, and FIG. 2B shows an example of electrocardiogram data. The horizontal axis in FIG. 2B is the elapsed time (Time), and the vertical axis is the electric potential. FIG. 3 is a diagram showing, as an example, the measurement state of electroencephalogram data (electroencephalic potential data) and electroencephalogram data. FIG. 3A shows a state in which electrodes of an electroencephalograph are attached to a skilled person to measure electroencephalogram data, FIG. 3B shows an example of electroencephalogram data, and FIG. 3C shows alpha waves, beta waves, theta waves, and delta waves obtained from the electroencephalogram data. The horizontal axis in FIG. 3B is the elapsed time (Time), and the vertical axis is the electric potential. The horizontal axis in FIG. 3C is the elapsed time (Time), and the vertical axis is the content rate. FIG. 4 is a diagram showing, as an example, an eye tracker for measuring gaze data.

[0032] In the embodiment, the skill evaluation apparatus A includes, for example, as shown in FIG. 1, a data acquisition unit 1, a control processing unit 2, an input unit 3, an output unit 4, an interface unit (IF unit) 5, and a storage unit 6.

[0033] The data acquisition unit 1 is connected to the control processing unit 2 and is a device that acquires data of a plurality of different measurement items related to the respective mental states of the model person and the evaluation target person when the model person and the evaluation target person each perform a predetermined task according to the control of the control processing unit 2.

[0034] The above-mentioned work may be any work as long as it is a work performed by the movement of the body. From the perspective of skill inheritance, it may include not only work such as manufacturing work of articles and transporting work of articles, but also arts, sports, etc. In the present embodiment, the work is preferably a work using a tool, and more specifically, it is a work of grinding or polishing a mold for molding a member used in a vehicle, which is a vehicle member molding die. In this case, the tool is, for example, a hand grinder in one example. Of course, it is not limited thereto. For example, the work is a welding work, and the tool is a welding machine such as a torch, or, for example, the work is a painting work, and the tool is a painting tool such as a spray gun. Preferably, the work is a work related to the manufacture of a vehicle, and the tool is a tool used in the work related to the manufacture of the vehicle.

[0035] The modeler is a person who has acquired the skills that serve as a model for the above-mentioned work. Generally, the modeler is proficient in the above-mentioned work and can perform the work relatively stably. For example, usually, it is considered that the more work experience one has, the higher the stability of the work. Therefore, a person with a certain amount of work experience may be regarded as a modeler. For example, the modeler may be set according to the working hours or the number of workpieces. Or, for example, usually, it is considered that the higher the accuracy of the work (for example, the dimensional accuracy of the work product, etc.), the higher the stability of the work. Therefore, a person who can perform the work with high accuracy may be regarded as a modeler. In the present embodiment, when classifying the plurality of skilled workers into a plurality of classes based on the accuracy of the work and the working time from the start to the end of the work, the skilled worker classified into the highest class among the plurality of classes is regarded as the modeler. Such a classification method of skilled workers is disclosed, for example, in Japanese Patent Laid-Open No. 2021-015225.

[0036] The plurality of different measurement items may be arbitrary as long as they are items related to the mental state of skilled persons (models and evaluation targets). For example, the plurality of measurement items are measurement items obtained from electrocardiogram data, electroencephalogram data, and gaze data. More specifically, the plurality of measurement items include the heart rate (heart beat), autonomic nerve activity index (indicator of activity of the autonomic nerve), parasympathetic nerve activity index (indicator of activity of the parasympathetic nerve), and sympathetic nerve activity index (indicator of activity of the sympathetic nerve) obtained from electrocardiogram data. The electrocardiogram of a healthy person due to one heartbeat generally consists of a P wave, a QRS wave, and a T wave. The heart rate is the number of peaks R of the QRS wave observed in one minute. As the autonomic nerve activity index, so-called RRSD, which is the coefficient of variation of the intervals between continuously adjacent peaks R, is used. As the parasympathetic nerve activity index, so-called RMSSD, which is the square root of the average value of the squares of the differences between continuously adjacent peaks R, is used. As the exchange nerve activity index, so-called LF / HF, which is the ratio of the power spectrum (LF) in the low frequency band (0.04 to 0.15 [Hz]) to the power spectrum (HF) in the high frequency band (0.15 to 0.4 [Hz]) when the heartbeats are frequency-analyzed, is used. The plurality of measurement items are alpha waves (8 to 13 [Hz]), beta waves (13 to [Hz] (above 13 Hz)), theta waves (4 to 8 [Hz]), and delta waves (~4 [Hz] (below 4 Hz)) obtained from electroencephalogram data. The alpha wave, beta wave, theta wave, and delta wave are each represented by its content rate ([%]). The plurality of measurement items include the gaze data itself obtained from the gaze data. The gaze data is represented by the dispersion of the viewpoints.

[0037] The data acquisition unit 1 may be a measuring instrument that measures the measurement items. For example, the data acquisition unit 1 may be an electrocardiograph that measures electrocardiogram data, an electroencephalograph that measures electroencephalogram, and a gaze meter that measures gaze data. In one example, the electrocardiograph and the electroencephalograph use the organized measurement device intercross-413 manufactured by Intercross Co., Ltd. Two electrodes Pb1 and Pb2 for the electrocardiograph are attached to the chest around the heart of the skilled person Ob at intervals as shown in FIG. 2A, and the electrocardiogram data shown in FIG. 2B is acquired. A headset BM having three electrodes for the electroencephalograph is attached to the head HD of the skilled person Ob as shown in FIG. 3A, and the electroencephalogram data shown in FIG. 3B is acquired. By performing a fast Fourier transform on the electroencephalogram data shown in FIG. 3B, the content rate [%] of each wave is obtained, and thereby, the alpha wave, beta wave, theta wave, and delta wave shown in FIG. 3C are obtained. In one example, the Tobii Pro Glass 2 (EM) manufactured by Tobii shown in FIG. 4 is used for the gaze meter, and the variance is obtained from the coordinate values of the viewpoints (the fixation points of the skilled person Ob) obtained by converting the measured gaze data into the XYZ orthogonal coordinate system. In the measurement of these electrocardiogram data, electroencephalogram data, and gaze data, each data is measured by sampling at a predetermined sampling interval from the start to the end of the operation. In the present embodiment, the average value during the operation time is finally used as the data of the measurement item.

[0038] Alternatively, for example, the data acquisition unit 1 may be an input unit that inputs each data of each measurement item obtained from each measurement result or each measurement result measured by such a measuring instrument. In this case, the data acquisition unit 1 may be used in combination with the input unit 3 described later.

[0039] Alternatively, for example, the data acquisition unit 1 may be an interface circuit that inputs and outputs data to and from an external device. In this case, the external device is a storage medium that stores data for each measurement item obtained from each measurement result or each measurement outcome. The storage medium is, for example, a USB (Universal Serial Bus) memory, an SD card (registered trademark), or the like. Alternatively, for example, the data acquisition unit 1 may be a drive device that reads data from a recording medium that records data for each measurement item obtained from each measurement result or each measurement outcome. In this case, the recording medium is, for example, a CD-ROM (Compact Disc Read Only Memory), a CD-R (Compact Disc Recordable), a DVD-ROM (Digital Versatile Disc Read Only Memory), a DVD-R (Digital Versatile Disc Recordable), or the like. Alternatively, for example, the data acquisition unit 1 may be a communication interface circuit that transmits and receives communication signals to and from an external device. In this case, the external device is a server device that is connected to the communication interface circuit via a network (such as a WAN (Wide Area Network, including a public communication network) or a LAN (Local Area Network)) and manages data for each measurement item obtained from each measurement result or each measurement outcome. When the data acquisition unit 1 is the above-described interface circuit or communication interface circuit, it may be shared with the IF unit 5 described later.

[0040] The input unit 3 is connected to the control processing unit 2 and is a device that inputs various commands such as commands instructing the start of skill evaluation and various data necessary for operating the skill evaluation device A such as the name of the person to be evaluated into the skill evaluation device A. For example, it is a plurality of input switches assigned with predetermined functions, a keyboard, a mouse, etc. The output unit 4 is connected to the control processing unit 2 and is a device that outputs commands and data input from the input unit 3, evaluations of the person to be evaluated, etc. according to the control of the control processing unit 2. For example, it is a display device such as a CRT display, a liquid crystal display, and an organic EL display, a printing device such as a printer, etc.

[0041] Note that a so-called touch panel may be formed from the input unit 3 and the output unit 4. When configuring such a touch panel, the input unit 3 is a position input device that detects and inputs an operation position, such as a resistive film method or a capacitance method. The output unit 4 is a display device. In this touch panel, the position input device is provided on the display surface of the display device, and one or a plurality of input content candidates that can be input to the display device are displayed. When the user touches the display position where the input content to be input is displayed, the position is detected by the position input device, and the display content displayed at the detected position is input to the skill evaluation device A as the user's operation input content. In such a touch panel, since the user can easily understand the input operation intuitively, a skill evaluation device A that is easy for the user to handle is provided.

[0042] The IF unit 5 is a circuit connected to the control processing unit 2 and performs data input / output with an external device according to the control of the control processing unit 2. For example, it is an interface circuit of RS-232C in a serial communication system, an interface circuit using the Bluetooth (registered trademark) standard, an interface circuit for performing infrared communication such as the IrDA (Infrared Data Association) standard, and an interface circuit using the USB (Universal Serial Bus) standard. Further, the IF unit 5 is a circuit that communicates with an external device, and for example, it may be a data communication card or a communication interface circuit according to the IEEE802.11 standard or the like.

[0043] The storage unit 6 is a circuit connected to the control processing unit 2 and stores various predetermined programs and various predetermined data according to the control of the control processing unit 2. The various predetermined programs include, for example, a control processing program. The control processing program includes, for example, a control program for controlling each part 1, 3 to 6 of the skill evaluation device A, and in the unit space of the Mahalanobis distance system defined by the plurality of measurement items, a unit space generation program for generating unit space data based on the data of the model person acquired by the data acquisition unit 1, a distance calculation program for obtaining the Mahalanobis distance with respect to the unit space data generated by the unit space generation program based on the data of the person to be evaluated acquired by the data acquisition unit 1, an evaluation program for evaluating the skill of the person to be evaluated based on the Mahalanobis distance obtained by the distance calculation program, and the like. The various predetermined data include, for example, data necessary for executing these programs. Such a storage unit 6 includes, for example, a ROM (Read Only Memory) which is a non-volatile storage element, an EEPROM (Electrically Erasable Programmable Read Only Memory) which is a rewritable non-volatile storage element, and the like. And the storage unit 6 includes a RAM (Random Access Memory) and the like which serve as a working memory of the so-called control processing unit 2 for storing data and the like generated during the execution of the predetermined program. The storage unit 6 may be configured to include a hard disk device with a relatively large storage capacity.

[0044] The control processing unit 2 is a circuit for controlling each part 1, 3 to 6 of the skill evaluation device A according to the functions of the respective parts and evaluating the skill of the person to be evaluated. The control processing unit 2 is configured to include, for example, a CPU (Central Processing Unit) and its peripheral circuits. The control processing unit 2 functionally includes a control unit 21, a unit space generation unit 22, a distance calculation unit 23, and an evaluation unit 24 when the control processing program is executed.

[0045] The control unit 21 controls each part 1, 3 to 6 of the skill evaluation device A according to the functions of the respective parts, and is responsible for the overall control of the skill evaluation device A. The control unit 21 stores each data of a plurality of measurement items acquired by the data acquisition unit 1 in the storage unit 6.

[0046] The unit space generation unit 22 generates unit space data based on the data of the model person acquired by the data acquisition unit 1 in the unit space of the Mahalanobis system defined by the plurality of measurement items. The unit space generation unit 22 generates unit space data based on the data among the data of the model person for which the Mahalanobis distance converges. Preferably, the unit space generation unit 221 generates unit space data based on the data among the one data for which the Mahalanobis distance converges near 1. Converging near 1 means within a predetermined range corresponding to the class interval (interval between classes) including 1. For example, when the class of the Mahalanobis distance in the frequency distribution of the Mahalanobis distance is in steps of 0.5, it converges within a range less than 2 (classes less than 2), and more specifically, converges within the range of 1 to 1.5 (each class of 1 and 1.5). Or, for example, when the class of the Mahalanobis distance in the frequency distribution of the Mahalanobis distance is in steps of 1, it converges to 1 (class of 1). The Mahalanobis system is a method of information processing for determining the approximation degree (dissociation degree) of target data based on the Mahalanobis distance considering the correlation between each item constituting the multi-dimension with respect to the unit space in data composed of multi-dimensional information.

[0047] The distance calculation unit 23 obtains the Mahalanobis distance with respect to the unit space data generated by the unit space generation unit 22 based on the data of the person to be evaluated acquired by the data acquisition unit 1.

[0048] The evaluation unit 24 evaluates the skill of the person to be evaluated based on the Mahalanobis distance obtained by the distance calculation unit 23. The evaluation unit 24 displays the Mahalanobis distance obtained by the distance calculation unit 23 on the output unit 4 for display as the evaluation of the skill of the person to be evaluated.

[0049] These control processing unit 2, input unit 3, output unit 4, IF unit 5, and storage unit 6 can be configured by, for example, a computer such as a desktop type, notebook type, or tablet type.

[0050] Next, the operation of this embodiment will be described. FIG. 5 is a flowchart showing the operation of the skill evaluation device.

[0051] When the power of the skill evaluation device A having such a configuration is turned on, it executes initialization of each necessary part and starts its operation. In the control processing unit 2, a control unit 21, a unit space generation unit 22, a distance calculation unit 23, and an evaluation unit 24 are functionally configured by executing its control processing program.

[0052] When an operator (user) inputs an instruction to start skill evaluation from the input unit 3, in FIG. 5, the skill evaluation device A first acquires, by the control unit 21 of the control processing unit 2, each data of a plurality of measurement items for each of the model person and the evaluation target person by the data acquisition unit 1, and stores it in the storage unit 6 (S1).

[0053] Next, the skill evaluation device A generates unit space data based on the data of the model person acquired by the data acquisition unit 1 in process S1 in the unit space of the Mahalanobis system by the unit space generation unit 22 of the control processing unit 2 (S2). The unit space generation unit 22 selects, as the unit space data, the data for which the Mahalanobis distance converges among the data of the model person.

[0054] Next, the skill evaluation device A obtains the Mahalanobis distance with respect to the unit space data generated by the unit space generation unit 22 in process S2 based on the data of the evaluation target person acquired by the data acquisition unit 1 in process S1 by the distance calculation unit 23 of the control processing unit 2 (S3).

[0055] Next, the skill evaluation device A evaluates the skill of the evaluation target person based on the Mahalanobis distance of the evaluation target person obtained by the distance calculation unit 23 in process S3 by the evaluation unit 24 of the control processing unit 2, and ends this process (S4). In the present embodiment, the evaluation unit 24 displays, on the output unit 4, the Mahalanobis distance of the evaluation target person obtained by the distance calculation unit 23 in process S3 as an evaluation of the skill of the evaluation target person. Incidentally, if necessary, the Mahalanobis distance of the evaluation target person obtained by the distance calculation unit 23 in process S3 may be output from the IF unit 5 to an external device.

[0056] Regarding the knowledge and verification that the skill evaluation device A can evaluate the skill of the evaluation target person in consideration of the mental state by such a configuration and operation, the following will be described.

[0057] FIG. 6 is a diagram for explaining a test piece used in the work. FIG. 6A is a perspective view schematically showing the test piece before the work, and FIG. 6B is a perspective view schematically showing the test piece after the work. FIG. 7 is a diagram showing the frequency distribution of the Mahalanobis distance in the unit space data and the measurement data. The horizontal axis in FIG. 7 is the Mahalanobis distance, and the vertical axis is the frequency. FIG. 8 is, as an example, a diagram showing the signal-to-noise ratio of the larger-the-better characteristic by the two-level orthogonal array L 12 The horizontal axes in FIGS. 8A to 8D are each a measurement item, and the vertical axes in these are each the signal-to-noise ratio of the larger-the-better characteristic. A to I are each measurement items 1 to 9. For example, A is measurement item 1 (= heart rate), and B is measurement item 2 (= RRSD). Incidentally, the illustration of the remaining 10 results out of the 14 measurement data, excluding each of the 4 results, is omitted. FIG. 9 is, as an example, a diagram showing the distribution of work influencing factors, which are factors affecting the stabilization of the work. The horizontal axis in FIG. 9 is the Mahalanobis distance, and the vertical axis is the content rate of each brain wave. FIG. 10 is a diagram for explaining the result when measures for improving skills are implemented. FIG. 10A shows the case of the β wave, where the horizontal axis is the Mahalanobis distance and the vertical axis is the content rate of the β wave. FIG. 10B shows the case of the δ wave, where the horizontal axis is the Mahalanobis distance and the vertical axis is the content rate of the δ wave.

[0058] In this finding and verification, the operation is the above-described die grinding or polishing operation (grinder operation) using a grinder as the tool. More specifically, this grinder operation includes two steps: a roughing step of roughly cutting the die and a finishing step of polishing the roughly cut surface to a smooth surface. However, in order to improve the cutting accuracy (dimensional accuracy and machining accuracy of the work piece) by the grinder operation, it is usually important to perform the roughing step operation with high precision and high efficiency. For this reason, a test piece TP modeled after the die is prepared, and a skilled worker performs the roughing step operation of the grinder operation on the test piece TP, and the working time and data of each measurement item are acquired from the start to the end of this operation. Then, the cutting accuracy of the test piece TP after the grinder operation is measured with a three-dimensional shape measuring machine.

[0059] This test piece TP is, for example, as shown in FIG. 5A, a 100×100×35 [mm] SS material (rolled steel for general structure) in which a concave strip TPb extending in a band shape with a width of 20 [mm] in one direction and a depth of 0.1 [mm] is formed in the central part by machining. With respect to this test piece TP, the roughing step operation is an operation of uniformly grinding, for example, the convex part TPc with a height of 0.1 mm on one side of the convex parts TPa and TPc with a height of 0.1 mm formed on both sides of the concave strip TPb by forming the concave strip TPb with a handy grinder by 0.1 mm. After such a roughing step operation, as shown in FIG. 5B, a grinding plane part TPd substantially flush with the bottom surface of the concave strip TPb is formed on the test piece TP.

[0060] The plurality of measurement items are the above-described heart rate (measurement item 1, A), RRSD (measurement item 2, B), RMSSD (measurement item 3, C), LF / HF (measurement item 4, D), beta wave (measurement item 5, E), alpha wave (measurement item 6, F), theta wave (measurement item 7, G), delta wave (measurement item 8, H), and dispersion of viewpoints (measurement item 9, I).

[0061] The model worker is a skilled worker belonging to the top class pre-classified by the method disclosed in Japanese Patent Application Laid-Open No. 2021-015225 based on the working time and cutting accuracy of such grinder work. In order to confirm that skills can be evaluated by this method, the person to be evaluated was set as a skilled worker belonging to the class next to the top class. If a difference (significant difference) is obtained between the model worker and the person to be evaluated by this method, it can be confirmed that skills can be evaluated by this method. In the method disclosed in Japanese Patent Application Laid-Open No. 2021-015225, skilled workers are classified into five classes: beginners (Class A), intermediate-level workers (Class B), semi-advanced workers (Class C), advanced workers (Class D), and top-level workers (Class E). Here, there are 4 model workers (top-level workers) belonging to the top class, and 4 persons to be evaluated (advanced workers) belonging to the next class. Even for the same skilled worker, the working time and the accuracy of the work may vary by about 10% from day to day in grinder work. Since it is considered that the top-level workers and the advanced workers have fully mastered the operations of grinder work, it is considered that the above-mentioned variation of about 10% is caused by the fact that the potential of the skilled worker could not be exerted due to the mental state. Accordingly, here, top-level workers and advanced workers were selected as the model workers and the persons to be evaluated.

[0062] These 4 model workers and 4 persons to be evaluated each performed grinder work on the test piece TP three times. As described above, during each work, the working time and the data of each measurement item were acquired, and the cutting accuracy of the test piece TP after the grinder work was measured. Each data of each measurement item was measured multiple times at a predetermined sampling interval, and the average value of each numerical value measured from the start to the end of the work was used as the final data of each measurement item.

[0063] Twelve data (data sets consisting of data for each of nine measurement items) were obtained from three grinder operations each by four model workers. Among these twelve data, the working time and cutting accuracy of each were compared with the criteria (threshold values) for classifying as the top - level workers, and ten data classified as top - level worker data were selected as unit - space data in the unit space of the Mahalanobis distance system defined by the nine above - mentioned measurement items. The remaining two data were classified as upper - level worker data and thus treated as the data of the subjects to be evaluated. Therefore, fourteen data consisting of the twelve data from three grinder operations each by four subjects to be evaluated and these remaining two data were regarded as measurement data. A part (Sample No.1 - 5) of the unit - space data is shown in Table 1, and a part (Sample No.1 - 5) of the measurement data is shown in Table 2.

[0064]

Table 1

[0065]

Table 2

[0066] And for this unit - space data, the Mahalanobis distance D in the fourteen measurement data was calculated by Equation 1 below, and the occurrence frequencies of these fourteen Mahalanobis distances were counted as frequencies for each distance. The ten unit - space data were similarly calculated and counted. The resulting frequency distribution of the Mahalanobis distances is shown in Figure 7.

[0067]

Equation

[0068] In FIG. 7, the frequency represented by the black hatching is the counting result of the Mahalanobis distance of the unit space data, and the frequency represented by the slanted hatching is the counting result of the Mahalanobis distance of the measurement data. As can be seen from FIG. 7, the Mahalanobis distance of the unit space data converges near 1, and the inverse matrix R of the correlation matrix R -1It existed and it was confirmed that it was appropriate as a unit space. The data obtained by the work of the top-level person (the exemplary person belonging to the highest rank) does not necessarily converge near 1. As described above, among the data obtained by the work of the top-level person, it is necessary to select the data classified as the data of the top-level person. In other words, it is necessary to generate unit space data based on the data where the Mahalanobis distance converges near 1. And as can be seen from FIG. 7, the Mahalanobis distances based on the respective data of the upper-level person (the person to be evaluated belonging to the rank next to the highest rank) are distributed above 2, and the Mahalanobis distances based on the respective data of the top-level person (each data classified as the data of the top-level person) and the Mahalanobis distances based on the respective data of the upper-level person are clearly differently distributed and a significant difference is recognized. Therefore, it can be understood that the skill difference between the top-level person and the upper-level person appears as the distribution difference of the Mahalanobis distance in the Mahalanobis Taguchi system defined by 9 measurement items. In other words, in the Mahalanobis Taguchi system defined by 9 measurement items, it is possible to determine whether a person is an exemplary person based on the Mahalanobis distance, and when the person is not an exemplary person, it is possible to evaluate the person to be evaluated against the exemplary person. Also, based on the distribution difference of the Mahalanobis distance, it is possible to determine the presence or absence of a significant difference.

[0069] The top-level person has a shorter working time, higher cutting accuracy, and more stable work compared to the upper-level person. In other words, since the data obtained by the work of the upper-level person contains components that destabilize the work, it is considered that by analyzing the data obtained by the work of the upper-level person, work influencing factors that affect the stabilization of the work can be extracted. Here, the work influencing factors were extracted from the 9 measurement items by obtaining the SN ratio of the maximization characteristics of the Mahalanobis distance using a two-level orthogonal array for the 9 measurement items.

[0070] Since the influence of each measurement item on the Mahalanobis distance is analyzed, and it depends on whether the measurement item is used in the calculation of the Mahalanobis distance (calculation of the SN ratio with the larger-the-better characteristic), a two-level system is used. To efficiently generate the patterns of whether to use or not, an orthogonal array is utilized. Here, the two-level orthogonal array L 12 is used. "1" represents the first level where the measurement item is used, and "2" represents the second level where the measurement item is not used. At the first level "1", the data of the measurement item is directly used in the calculation, and at the second level "2", the data of the measurement item is replaced with 0 and used.

[0071]

Table 3

[0072] Since the true value of the data of the measurement item is unknown, the SN ratio η with the larger-the-better characteristic is used. This SN ratio η with the larger-the-better characteristic is obtained by the following formula (2). To each of the above 14 measurement data, sample numbers k from 1 to 14 are assigned (k is an integer from 1 to 14), and D k is the Mahalanobis distance of sample number k.

[0073]

Equation

[0074] Two-level orthogonal array L 12The signal-to-noise ratio η of the maximum likelihood property of the Mahalanobis distance according to [reference] is shown in FIG. 8. For each of the nine measurement items, the signal-to-noise ratio η of the maximum likelihood property of the Mahalanobis distance when using the measurement item and the signal-to-noise ratio η of the maximum likelihood property of the Mahalanobis distance when not using the measurement item are obtained as the first signal-to-noise ratio η1 and the second signal-to-noise ratio η2, respectively. These first signal-to-noise ratio η1 and second signal-to-noise ratio η2 were obtained for each of the 14 measurement data. FIGS. 8A to 8D show the first signal-to-noise ratio η1 and the second signal-to-noise ratio η2 in each measurement item obtained for 4 of the 14 measurement data. For example, FIG. 8A shows the first signal-to-noise ratio η1 and the second signal-to-noise ratio η2 of each measurement item in the measurement data of sample number 1 shown in Table 2, and FIG. 8B shows the first signal-to-noise ratio η1 and the second signal-to-noise ratio η2 of each measurement item in the measurement data of sample number 4 shown in Table 2.

[0075] Then, in the first signal-to-noise ratio η1 and the second signal-to-noise ratio η2 obtained for each of these 14 measurement data, for each of the nine measurement items, the number of measurement data in which the second signal-to-noise ratio η2 is smaller than the first signal-to-noise ratio η1 is counted, and from among the nine measurement items, a predetermined number is extracted as the work influencing factor from the larger of the counted numbers. For example, in the case of the 4 measurement data shown in FIGS. 8A to 8D, the number in the heart rate (measurement item 1, A) is counted as 3, the number in the RRSD (measurement item 2, B) is counted as 1, and the number in the RMSSD (measurement item 3, C) is counted as 3. In the 14 measurement data, the top 3 measurement items with the largest numbers, beta waves, alpha waves, and delta waves, were extracted as the work influencing factors. Note that it is not limited to the top 3, and it may be any number such as the top 1, top 2, top 4, etc.

[0076] Figure 9 shows the distributions of beta waves, alpha waves, and delta waves with respect to the Mahalanobis distance D for unit space data and measurement data, respectively. From Figure 9, it can be seen that as the Mahalanobis distance D approaches 1, the beta waves increase and the delta waves decrease. Therefore, it is considered that the work can be stabilized by increasing the beta waves and decreasing the delta waves. Beta waves are generally said to increase in the so-called concentrated and awake states where the brain is activated. Note that delta waves generally do not appear much during wakefulness and are associated with slow-wave-sleep. The sum of each brain wave is 100 [%]. As described above, beta waves are related to wakefulness and delta waves are related to slow-wave sleep. Therefore, there is a relationship between beta waves and delta waves such that as the beta waves increase, the delta waves decrease. Also, since work is considered to be a repetition of situation judgment and reflection of the judgment result on actions, it is considered that the top-level person who generated the data adopted as unit space data repeats situation judgment intensively with high concentration. Therefore, the countermeasures for work stabilization are, first, to incorporate actions that enhance concentration into skilled workers, or, conversely, second, to eliminate environmental factors that inhibit concentration. Such actions that enhance concentration and, conversely, environmental factors that inhibit concentration often vary from person to person, and it is considered that the specific actions and environmental factors depend on skilled workers.

[0077] While taking actions to enhance concentration for senior workers, the above-mentioned grinder work was carried out three times, and beta waves and delta waves were acquired during the work. The Mahalanobis distance D of the resulting beta waves is indicated by ○ in Figure 10A, and the Mahalanobis distance D of the resulting delta waves is indicated by ○ in Figure 10B. The working time and cutting accuracy are shown as No.N4, No.N5, and No.N6 in the following Table 4 with their standard deviations. The Mahalanobis distances D of the beta waves and delta waves indicated by ● in Figures 10A and 10B are the results obtained from the above-mentioned measurement data without taking actions to enhance concentration, and the working time and cutting accuracy are shown as No.N1, No.N2, and No.N3 in the following Table 4 with their standard deviations.

[0078]

Table 4

[0079] From FIGS. 10A and 10B, it was confirmed that by the action of enhancing concentration, the beta waves increased and the delta waves decreased during the grinder operation. And from Table 4, it can be seen that the variation in cutting accuracy decreased and the work was stabilized. Therefore, from the nine measurement items, it was confirmed that the beta waves, alpha waves, and delta waves extracted as described above are work influencing factors. In other words, it can be seen that the Mahalanobis distance D is particularly affected by the beta waves and delta waves of the brain waves. As described above, there is a relationship between the beta waves and delta waves such that when the beta waves increase, the delta waves decrease. Therefore, it is sufficient to observe at least one of the beta waves and delta waves. For this reason, by including at least one of the beta waves of the brain waves and the delta waves of the brain waves in a plurality of measurement items, the Mahalanobis distance reflecting the mental state related to the skill can be obtained, so that the skill of the evaluation target person can be evaluated more appropriately.

[0080] As described above, it is presumed that the quality of the work is related to the mental state of the skilled person who performs the work. According to the inventor's experiment, a finding was obtained that there is a significant difference in the mental state between the skilled person who is not the model person and the mental state of the model person. Therefore, by comparing the mental state of the model person with the mental state of the evaluation target person, the skill of the evaluation target person can be evaluated more appropriately. The skill evaluation device A and the skill evaluation method implemented therein in the embodiment are in the unit space of the Mahalanobis taguchi system defined by a plurality of different measurement items related to the mental state when performing a predetermined work. By obtaining the Mahalanobis distance of the evaluation target person with respect to the unit space data generated based on the data of the model person and evaluating the skill of the evaluation target person, the skill of the evaluation target person can be evaluated more appropriately.

[0081] When classifying skilled persons into a plurality of classes, the above-described skill evaluation device A and skill evaluation method use the skilled person classified into the highest class as the model person, so that the model person can be set more appropriately.

[0082] According to the present embodiment, the skill evaluation device A and the skill evaluation method for evaluating the skills of skilled workers classified into the class next to the top class can be provided.

[0083] Since the skill evaluation device A and the skill evaluation method generate unit space data based on the data of a modeler whose Mahalanobis distance converges, the unit space data serving as an evaluation criterion can be generated more appropriately.

[0084] Since at least one of the β wave of the electroencephalogram and the δ wave of the electroencephalogram is included in a plurality of measurement items in the skill evaluation device A and the skill evaluation method, a Mahalanobis distance reflecting the mental state related to the skill can be obtained, so that the skills of the person to be evaluated can be evaluated more appropriately.

[0085] The skill evaluation device A and the skill evaluation method can evaluate the skills of an evaluation target person who performs an operation using a tool.

[0086] The skill evaluation device A and the skill evaluation method can evaluate the skills of an evaluation target person who performs an operation of grinding or polishing a mold.

[0087] The skill evaluation device A and the skill evaluation method can evaluate the skills of an evaluation target person who performs an operation of grinding or polishing a mold for forming vehicle members.

[0088] In order to represent the present invention, the present invention has been appropriately and sufficiently described through embodiments with reference to the drawings above. However, it should be recognized that those skilled in the art can easily make changes and / or improvements to the above-described embodiments. Therefore, as long as the changes or improvements implemented by those skilled in the art do not depart from the scope of the claims described in the claims, the changes or the improvements are construed as being included in the scope of the claims.

Explanation of Reference Numerals

[0089] A Skill evaluation device 1 Data acquisition unit 2 Control processing unit 4 Output section 6 Memory section 21 Control section 22 Unit space generation section 23 Distance calculation section 24 Evaluation section

Claims

【Claim 1】 A skill evaluation device for evaluating the skill related to a predetermined operation of handling a predetermined object for an evaluation target, a data acquisition unit that acquires data of a plurality of different measurement items related to the mental state of the evaluation target when the evaluation target performs the predetermined operation; a distance calculation unit that obtains a Mahalanobis distance with respect to unit space data generated based on the data of a model person in the plurality of measurement items in the unit space of the Mahalanobis system defined by the plurality of measurement items, based on the data of the evaluation target acquired by the data acquisition unit; and an evaluation unit that evaluates the skill of the evaluation target based on the Mahalanobis distance obtained by the distance calculation unit. Skill evaluation device.

Images