JP2023011271A - System, information processing device, method and program - Google Patents

Abstract

To provide a system, an information processing device, a method and a program that can implement further effective vocational training.SOLUTION: A system includes: means for displaying a first image relating to vocational training; means for receiving an operation of a trainee having the vocational training; means for acquiring first biological information including at least an amount of perspiration of the trainee viewing the first image; means for calculating a first value relating to a stress of the trainee from the first biological information; and means for estimating characteristics of the trainee on work corresponding to the vocational training based on the first value.SELECTED DRAWING: Figure 8

Description

The present disclosure relates to systems, information processing apparatuses, methods, and programs.

Techniques for analyzing the mental and physical conditions of a person to be measured, such as the level of stress, are being developed based on biological information acquired by a sensing device attached to the body of the person to be measured. In Patent Literature 1, a biological information acquisition device having a sensor is used to measure a user's vital signs such as pulse, fever, and perspiration, and a stress evaluation value is estimated based on the measured vital signs.

Japanese Patent Application Laid-Open No. 2020-010881

For example, devices such as those mentioned above may be used to manage stress so that employees can work healthily. However, there is no evaluation based on the degree of stress whether the job is suitable for oneself.

An object of the present disclosure is to provide more effective vocational training.

Means for displaying a first image relating to vocational training, means for receiving an operation from a trainee attending the vocational training, and means for acquiring first biometric information including at least the amount of perspiration of the trainee viewing the first image. and means for calculating a first value related to the stress of the trainee from the first biometric information, and means for estimating the characteristics of the trainee regarding the work corresponding to the vocational training based on the first value.

According to the present disclosure, more effective vocational training can be implemented.

2 is a block diagram showing an example of a hardware configuration of system 1; FIG. 2 is a block diagram showing an example of a hardware configuration of main unit 11 shown in FIG. 1; FIG. FIG. 3 is a block diagram showing a configuration example of the training device 10 shown in FIGS. 1 and 2. FIG. 3 is a diagram illustrating an example of a functional configuration of server 20; FIG. It is a figure which shows the data structure of employee information DB2021. It is a figure which shows the data structure of training information DB2022. It is a figure which shows the data structure of content information DB2023. FIG. 3 is a schematic diagram showing an example of a configuration when a worker conducts vocational training using the training device 10; FIG. 9 is a flow chart showing an example of the operation of the training device 10 shown in FIG. 8 when the trainees are given vocational training. FIG. 10 is a flow chart representing an example of the operation of the training device 10 during training shown in FIG. 9; 9 is a schematic diagram showing an example of an image displayed on the display of the terminal device 30 shown in FIG. 8. FIG. FIG. 9 is a flow chart showing an example of operation when the server 20 shown in FIG. 8 evaluates a student's characteristics and proficiency. FIG. 9 is a schematic diagram showing an example of an image displayed on the display of the terminal device 30 shown in FIG. 8. FIG. FIG. 3 is a schematic diagram showing the hand tracking function of the training device 10; 9 is a schematic diagram showing an example of an image displayed on the display of the terminal device 30 shown in FIG. 8. FIG.

Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. In the following description, the same parts are given the same reference numerals. Their names and functions are also the same. Therefore, detailed description thereof will not be repeated.

<Overview>
The system according to the present embodiment measures in real time the stress experienced by workers undergoing virtual vocational training. The system manages the characteristics of workers obtained through vocational training, the degree of proficiency in predetermined tasks, and the like. The system determines the worker's work based on the information it manages. For example, the system determines worker assignments or shifts based on the information it manages.

<1 Configuration diagram of the entire system>
FIG. 1 is a block diagram showing an example of the hardware configuration of system 1. As shown in FIG. A system 1 shown in FIG. 1 includes, for example, a training device 10, a server 20, and a terminal device 30. FIG. The training device 10, the server 20, and the terminal device 30 are connected for communication via the network 80, for example.

FIG. 1 shows an example in which system 1 includes one training device 10, but the number of training devices 10 included in system 1 is not limited to one. Two or more training devices 10 may be included in the system 1 .

In this embodiment, a set of multiple devices may be used as one server. How to distribute a plurality of functions required to realize the server 20 according to the present embodiment to one or a plurality of pieces of hardware takes into account the processing capability of each piece of hardware and/or the specifications required for the server 20. can be determined as appropriate.

A training device 10 shown in FIG. 1 is, for example, a device used by a worker who undergoes virtual vocational training. The training apparatus 10 has, for example, a main unit 11, a controller 101, a perspiration sensor 102, a pulse wave sensor 103, and a temperature sensor 104.

The main unit 11 is a device capable of displaying VR (Virtual Reality) images related to vocational training. A VR image related to vocational training is an image created by simulating an actual work site. An avatar in the shape of a person is displayed in the VR video, and the image configuration is such that the vocational training student can experience the events in the video as the avatar. The avatar in the VR image walks or performs a predetermined action with respect to an object according to the operation by the student. VR images related to vocational training include, for example, a plurality of vocational training courses corresponding to the type of industry or business (work) content. In a vocational training course, for example, a plurality of tasks are set in advance according to the content of the course, and the student operates an avatar to perform the set tasks.

The main unit 11 is implemented by, for example, an HMD (Head Mount Display). Note that the video regarding vocational training is not limited to the VR video, and may be a general two-dimensional video. When vocational training is conducted using general two-dimensional images, main unit 11 may be, for example, a stationary PC (Personal Computer) or a laptop PC.

The controller 101 is a device that is held by a student who wears the main unit 11 to receive vocational training and receives input from the student. The shape of the controller 101 may be any shape. For example, the controller 101 may have a shape in which a controller for the left hand and a controller for the right hand are separated and can be operated with each hand. Also, the controller 101 may be shaped so that it can be operated with only one hand. Also, the controller 101 may be shaped so that it can be operated with both hands. For example, the controller 101 may be shaped like a controller for operating a video game, or shaped like a steering wheel.

The perspiration sensor 102 is a sensor that measures the perspiration of the palm or fingers. The perspiration sensor 102 is an example of a sensor that acquires biological information. The perspiration amount sensor 102 has a structure that enables miniaturization and cost reduction. For example, the perspiration amount sensor 102 measures the amount of perspiration based on the humidity of the space formed including the living body (palm or fingers).

Specifically, for example, the perspiration amount sensor 102 has a member that forms a space so that one surface is covered with a living body, an intake path that supplies air to the space, and an exhaust path that exhausts air from the space. A humidity sensor is provided in each of the intake path and the exhaust path. The perspiration amount sensor 102 calculates the amount of perspiration vaporized from the living body into the space based on the humidity of the air supplied to the space and the humidity of the air exhausted from the space. Thereby, the perspiration amount sensor 102 acquires the amount of perspiration.

The perspiration amount sensor 102 is provided in the controller 101, for example. For example, a member forming a space for the perspiration amount sensor 102 is provided in the area of the controller 101 held by the student. As a result, the perspiration amount sensor 102 can measure the perspiration amount of the student when the student holds and uses the controller 101 .

If the controller 101 is separated into a controller for the left hand and a controller for the right hand and has a shape that can be operated with each hand, the perspiration amount sensor 102 is provided in one of the controllers, for example. At this time, it is desirable that the controller held by the non-dominant hand is provided with the perspiration amount sensor 102 . Note that the perspiration amount sensor 102 may not be provided in the controller 101 but may be separately worn by the student.

A pulse wave sensor 103 measures the pulse wave of the student. The pulse wave sensor 103 is an example of a sensor that acquires biological information. The pulse wave sensor 103 is attached, for example, to the student's finger and measures the pulse wave at a preset cycle. When the perspiration sensor 102 is independently attached to the student's finger, the pulse wave sensor 103 is attached, for example, to a finger different from the finger on which the perspiration sensor 102 is attached. Note that the pulse wave sensor 103 may be attached to a part other than the finger as long as the pulse wave can be measured. Moreover, when the temperature sensor 104 is provided, the pulse wave sensor 103 may not be provided.

A temperature sensor 104 measures the student's body temperature. The temperature sensor 104 is an example of a sensor that acquires biological information. The temperature sensor 104 is provided in the controller 101, for example, and measures body temperature at a preset cycle. Note that the temperature sensor 104 may not be provided in the controller 101 but may be separately attached to the student. Moreover, when the pulse wave sensor 103 is provided, the temperature sensor 104 may not be provided.

FIG. 2 is a block diagram showing an example of the hardware configuration of main unit 11 shown in FIG. The main unit 11 shown in FIG. 2 includes a communication IF (Interface) 12, an output device 14, a memory 15, a storage 16, and a processor 19, for example.

The communication IF 12 is for inputting and outputting signals for the training device 10 to communicate with devices in the system 1 such as the server 20, the controller 101, the perspiration sensor 102, the pulse wave sensor 103, and the temperature sensor 104. interface.

The output device 14 is a device (display, speaker, etc.) for presenting information to the user.

The memory 15 is for temporarily storing programs, data processed by the programs, etc., and is a volatile memory such as a DRAM (Dynamic Random Access Memory).

The storage 16 is for storing data, and is, for example, a flash memory or a HDD (Hard Disc Drive).

The processor 19 is hardware for executing an instruction set described in a program, and is composed of arithmetic units, registers, peripheral circuits, and the like.

The server 20 shown in FIG. 1 manages, for example, information about employees who are workers. Specifically, for example, the server 20 manages the employee's characteristics, the employee's degree of proficiency in work, and the like. In this embodiment, employee characteristics include, for example, the following elements.
・ Work (work) that is easy to feel stress
・ Weak or good at work (work)
・Time spent continuously working (work) ・Aptitude for work (work)

The server 20 is realized by a computer connected to the network 80, for example. As shown in FIG. 1, the server 20 includes a communication IF 22, an input/output IF 23, a memory 25, a storage 26, and a processor 29.

The communication IF 22 is an interface for inputting and outputting signals for the server 20 to communicate with devices in the system 1 such as the training device 10, for example.

The input/output IF 23 functions as an interface with an input device for receiving input operations from the user and an output device for presenting information to the user.

The memory 25 temporarily stores programs and data processed by the programs, and is a volatile memory such as a DRAM.

The storage 26 is for storing data, and is, for example, flash memory or HDD.

The processor 29 is hardware for executing an instruction set described in a program, and is composed of arithmetic units, registers, peripheral circuits, and the like.

The terminal device 30 shown in FIG. 1 is, for example, a terminal used by a manager of vocational training or a manager who manages the work of employees. The terminal device 30 is realized by, for example, a stationary PC (Personal Computer), a laptop PC, or the like. Also, the terminal device 30 may be a mobile terminal such as a smart phone or a tablet.

The terminal device 30 receives a request from the administrator and transmits the received request to the server 20 . The terminal device 30 receives information output from the server 20 in response to a request, and presents the received information to the administrator.

<1.1 Configuration of training device>
FIG. 3 is a block diagram showing a configuration example of the training device 10 shown in FIGS. 1 and 2. As shown in FIG. A training apparatus 10 shown in FIG. As shown in FIG. 3 , main unit 11 includes communication unit 120 , output device 14 , audio processing unit 17 , microphone 171 , speaker 172 , camera 160 , motion sensor 150 , storage unit 180 , and control unit 190 . Each block included in the training device 10 is electrically connected by, for example, a bus.

The communication unit 120 performs processing such as modulation/demodulation processing for the training device 10 to communicate with other devices. The communication unit 120 performs transmission processing on the signal generated by the control unit 190 and transmits the signal to the outside (for example, the server 20). Communication unit 120 performs reception processing on a signal received from the outside and outputs the signal to control unit 190 . Although one communication unit 120 is shown in FIG. 3, it does not mean that the training device 10 is compatible with only one communication standard. The training device 10 is compatible with multiple existing communication standards.

The output device 14 is a device for presenting information to the user wearing the training device 10 . The output device 14 is, for example, a display device that displays an image on a display panel 141 such as an organic EL panel positioned in front of the user when the main device 11 is worn on the user's head. For example, an image may be stereoscopically viewed by generating parallax images viewed from left and right viewpoints and displaying them in left and right regions obtained by dividing the display screen into two. Note that one image may be displayed on the entire display screen. The display panel 141 displays an image according to the control of the control section 190, for example.

The audio processing unit 17 performs digital-analog conversion processing of audio signals, for example. The audio processing unit 17 converts the signal supplied from the microphone 171 into a digital signal and supplies the converted signal to the control unit 190 . Also, the audio processing unit 17 provides an audio signal to the speaker 172 . The audio processing unit 17 is realized by, for example, a processor for audio processing. The microphone 171 receives an audio input and provides an audio signal corresponding to the audio input to the audio processing section 17 . The speaker 172 converts the audio signal given from the audio processing unit 17 into audio and outputs the audio to the outside of the training device 10 .

The camera 160 is a device for receiving light with a light receiving element and outputting it as a photographing signal. The camera 160 is installed, for example, at a position where the front of the main unit 11 can be photographed, for example, at a position where the line of sight of the wearer can be photographed. Images captured by the camera 160 are used, for example, to read the movement of the user. If a sensor that detects the movement of the user is provided separately from main device 11, camera 160 may not be provided.

The motion sensor 150 includes an acceleration sensor, an angular velocity sensor, a magnetic sensor, etc., and detects the movement of the student wearing the main device 11 . For example, the motion sensor 150 detects the acceleration of a student who is undergoing vocational training with the training device 10 . Also, the motion sensor 150 detects the rotation of the student's head and changes in orientation. Main unit 11 may include a sensor that tracks the position of main unit 11 . By using the output from these sensors, it is possible to identify the movement of the student's head and synchronize it with the student's view in real time.

The storage unit 180 is realized by, for example, the memory 15 and the storage 16, and stores data and programs used by the training device 10. FIG. The storage unit 180 stores employee information 181, training information 182, and content information 183, for example.

Employee information 181 includes, for example, information about employees as students using training device 10 . The information about the employee includes, for example, information identifying the employee, the employee's name, gender, date of joining the company, characteristics of work content, and proficiency level of work content. The characteristics of the work content and the proficiency level of the work content are based on information acquired in vocational training using the training device 10, for example. Information about the employee may further include age, address, date of birth, phone number, email address, and the like.

The training information 182 includes, for example, information acquired in vocational training using the training device 10 . Specifically, training information 182 includes, for example, results of vocational training. The result of vocational training is represented, for example, by the degree of achievement of duties set in the vocational training course. Training information 182 includes, for example, information measured in vocational training. Information measured in vocational training includes, for example, data on heart rate, perspiration, body temperature, etc. measured in vocational training. The training information 182 includes, for example, stress values calculated based on information measured in vocational training.

The content information 183 includes, for example, content provided in vocational training using the training device 10, such as information about vocational training courses. The information about the vocational training course includes, for example, information identifying the course, the name of the course, the classification of the course, the corresponding industry, and the corresponding work content.

For example, if the industry is construction, the vocational training courses include a "preparation course" for training preparations before starting work, and a "pre-start inspection course" for training on site inspections before starting work. "Slope Work Course" for training work on slopes, and "High Altitude Scaffolding Work Course" for training work on scaffolding at height.

Control unit 190 is implemented by processor 19 reading a program stored in storage unit 180 and executing instructions included in the program. The control section 190 controls the operation of the training device 10 . The control unit 190 functions as an operation reception unit 191, a transmission/reception unit 192, and a training control unit 193 by operating according to a program.

The operation accepting unit 191 performs processing for accepting an operation input from the controller 101 . Instructions input via the controller 101 or the like are, for example, as follows.
・Contents of vocational training, for example, selection of vocational training course ・Input about prescribed actions during vocational training

Further, the operation accepting unit 191 accepts voice instructions input from the microphone 171 . Specifically, for example, the operation reception unit 191 receives an audio signal input from the microphone 171 and converted into a digital signal by the audio processing unit 17 . The operation reception unit 191 acquires an instruction from the user, for example, by analyzing the received audio signal and extracting a predetermined noun.

The transmitting/receiving unit 192 performs processing for transmitting/receiving data between the training device 10 and an external device such as the server 20 according to a communication protocol. Specifically, for example, the transmitting/receiving unit 192 receives measurement data measured by the perspiration amount sensor 102, the pulse wave sensor 103, and the temperature sensor 104 during vocational training. The transmitter/receiver 192 transmits the acquired measurement data and the stress value calculated from the measurement data to the server 20 . The transmitting/receiving unit 192 also receives from the server 20 information about students who receive vocational training.

The training control unit 193 performs processing for workers to receive virtual vocational training. Specifically, for example, the training control unit 193 performs calibration for the student before starting vocational training. Calibration is a process for comparing the state of the student and the measured values of each sensor. The image displayed in calibration is, for example, an example of a second image assumed to have a predetermined stress value.

For example, the training control unit 193 displays an image in which the student is assumed to be extremely tense in calibration, and based on the measured values of the sweat amount sensor 102, the pulse wave sensor 103, and the temperature sensor 104 at that time The stress value calculated by The biological information obtained by the perspiration amount sensor 102, the pulse wave sensor 103, and the temperature sensor 104 at this time is, for example, an example of the second biological information. Also, the stress value calculated at this time is an example of a second value related to stress, for example.

In addition, for example, the training control unit 193 displays an image that the student is supposed to relax in the calibration, and based on the measured values of the sweat amount sensor 102, the pulse wave sensor 103, and the temperature sensor 104 at that time The stress value calculated by The biological information obtained by the perspiration amount sensor 102, the pulse wave sensor 103, and the temperature sensor 104 at this time is, for example, an example of the second biological information. Also, the stress value calculated at this time is an example of a second value related to stress, for example.

In addition, the training control unit 193, for example, causes the student to perform a simple exercise during calibration. In the calibration, the image displayed to make the student do a simple exercise is, for example, an example of the third image for making the student do a moderate exercise. The training control unit 193 determines whether or not the student is likely to sweat based on the measured value of the perspiration amount sensor 102 at that time. The biological information obtained by the perspiration amount sensor 102 at this time is, for example, an example of the third biological information.

When the calibration is completed, the training control section 193 causes the display panel 141 of the output device 14 to display an image related to vocational training. The video regarding vocational training here is an example of the first image regarding vocational training. The training control unit 193 displays an avatar that can be operated by the student via the controller 101 on the video regarding the vocational training. The training control section 193 moves the avatar according to the operation input from the controller 101 . The training control unit 193 causes the storage unit 180 to store the video during the vocational training. The training control unit 193 calculates the degree of achievement of the vocational training, for example, according to the number of tasks set in the vocational training that have been completed by the students.

The training control unit 193 controls the perspiration amount sensor 102, the pulse wave sensor 103, and the temperature sensor 104 so as to measure the values at predetermined intervals, for example, during vocational training. Exercise control unit 193 acquires information measured by perspiration sensor 102 , pulse wave sensor 103 , and temperature sensor 104 . The training control unit 193 corrects the acquired information based on the information obtained by calibration. The training control unit 193, for example, calculates the stress value based on the corrected information. The exercise control unit 193 calculates the stress value using, for example, the amount of perspiration, pulse rate, and body temperature. Specifically, for example, the training control unit 193 calculates the stress value by multiplying the amount of perspiration, the pulse rate, and the body temperature by predetermined coefficients, and adding the values obtained by multiplying the coefficients. The biological information obtained by the perspiration amount sensor 102, the pulse wave sensor 103, and the temperature sensor 104 at this time is an example of the first biological information, for example. Also, the stress value calculated at this time is an example of a first value related to stress, for example.

Note that the training control unit 193 may calculate the stress value using, for example, the amount of perspiration and the pulse rate. Also, the exercise control unit 193 may calculate the stress value using, for example, the amount of perspiration and body temperature. Also, the stress value may be calculated by the server 20 .

<1.2 Functional Configuration of Server>
FIG. 4 is a diagram showing an example of the functional configuration of the server 20. As shown in FIG. As shown in FIG. 4 , the server 20 functions as a communication section 201 , a storage section 202 and a control section 203 .

The communication unit 201 performs processing for the server 20 to communicate with an external device.

The storage unit 202 has, for example, an employee information database (DB) 2021, a training information database (DB) 2022, a content information database (DB) 2023, a shift information database (DB) 2024, and the like.

The employee information DB 2021 is, for example, a database for holding information on employees. Details will be described later.

The training information DB 2022 is, for example, a database for holding information acquired in vocational training using the training device 10 . The information acquired through vocational training includes, for example, results of vocational training, information measured through vocational training, stress values calculated from the measured information, and the like. Details will be described later.

The content information DB 2023 is, for example, a database for holding information on content provided in vocational training using the training apparatus 10, such as vocational training courses. Details will be described later.

The shift information DB 2024 is a database for holding information about the work schedule of workers.

The control unit 203 is implemented by the processor 29 reading a program stored in the storage unit 202 and executing instructions included in the program. The control unit 203 operates according to a program to exhibit functions shown as a reception control module 2031, a transmission control module 2032, an analysis module 2033, an assignment determination module 2034, a shift setting module 2035, and a presentation module 2036.

The reception control module 2031 controls processing for the server 20 to receive a signal from an external device according to a communication protocol. Specifically, for example, the reception control module 2031 receives information measured by the training device 10 and a stress value calculated based on the measured information, which are transmitted from the training device 10 .

The transmission control module 2032 controls the processing by which the server 20 transmits signals to external devices according to a communication protocol.

The analysis module 2033 analyzes the information acquired in the vocational training by the training device 10, and estimates the characteristics of the student who has taken the vocational training. Also, the analysis module 2033 analyzes the information acquired in the vocational training, and estimates the proficiency level of the trainee in the vocational training he/she has attended and the work corresponding thereto.

Specifically, for example, the analysis module 2033 calculates the index value of the stress value for the predetermined work content based on the calculated stress value after completing the vocational training course. For example, the analysis module 2033 calculates an average stress value and a maximum stress value for predetermined work content based on the calculated stress values. For example, the vocational training course and the business content of the predetermined industry are associated as follows.
・One vocational training course is associated with one job content of a given industry ・Multiple vocational training courses are associated with one job content of a given industry ・Vocational training course Elements that express the characteristics of are set, and each element is associated with the knowledge required for the work content of a given industry

The analysis module 2033, for example, estimates the aptitude for the type of business and work content based on information acquired through vocational training. In other words, the analysis module 2033 estimates whether the employee has work (work) that is likely to cause stress and whether there is work (work) that the employee is not good at or good at.

The analysis module 2033 may estimate suitability in any way. For example, the analysis module 2033 compares the result of the vocational training course and the index value of the stress value obtained by taking the vocational training course against a preset lookup table to estimate aptitude. Also, for example, the analysis module 2033 is a trained person who has been trained to output the result of the vocational training course and the index value of the stress value obtained by taking the vocational training course, and the aptitude based on the result and the index value. Aptitudes may be obtained by inputting them into the model.

Also, the analysis module 2033 may acquire aptitude from the results and index values acquired based on the vocational training course taken this time. For example, when the result is good and the difference between the average stress value and the maximum stress value is small, the analysis module 2033 evaluates that the employee has high aptitude for the predetermined work content. On the other hand, for example, if the result is not favorable and the difference between the average stress value and the maximum stress value is large, the analysis module 2033 evaluates that the employee has low aptitude for the predetermined work content.

Also, the analysis module 2033 may acquire aptitude based on changes in the results of vocational training courses taken in the past and changes in index values calculated in the vocational training courses taken in the past. For example, the analysis module 2033 determines that, as the number of lectures increases, the result becomes better, and if the difference between the average stress value and the maximum stress value becomes smaller, the employee is highly aptitude for the predetermined work content. and evaluate. On the other hand, the analysis module 2033, for example, if the result does not improve even if the number of lectures increases and the difference between the average stress value and the maximum stress value remains high, the employee Evaluate as low aptitude for The analysis module 2033 may represent aptitude by a rank using alphabets or the like, or may be represented by points.

Information referred to by the analysis module 2033 is not limited to the above. For example, the analysis module 2033 presets a predetermined threshold for the stress value. The analysis module 2033 may also refer to the time the calculated stress value was at the threshold, the number of times the threshold was exceeded, and the like.

The analysis module 2033 calculates the time during which the employee can work continuously, that is, the recommended work time, according to the evaluated aptitude. How the analysis module 2033 may evaluate the recommended working time. For example, the analysis module 2033 may check the evaluated aptitude against a preset lookup table to obtain a recommended working time. Also, for example, the analysis module 2033 may acquire the recommended work time by inputting the evaluated aptitude into a trained model trained to output the recommended work time based on the aptitude. The analysis module 2033 sets, for example, a long recommended work time for a highly aptitude employee and a short recommended work time for a low aptitude employee.

The analysis module 2033, for example, estimates the proficiency level based on information acquired in vocational training. The analysis module 2033 may estimate the proficiency in any way. Specifically, for example, the analysis module 2033 inputs changes in the results of vocational training courses taken in the past and changes in index values calculated in the vocational training courses taken in the past into a predetermined function, A proficiency level may be calculated.

Also, for example, the analysis module 2033 inputs changes in results of past vocational training courses and changes in index values of stress values calculated in past vocational training courses to the learned model. , may acquire proficiency. At this time, the trained model is trained to output the proficiency level based on, for example, changes in the results of the vocational training course and changes in the index value of the stress value calculated in the vocational training course. The analysis module 2033 may represent the proficiency level by a rank using an alphabet or the like, or may be represented by a score.

The assignment determination module 2034 determines the employee's assignment destination based on the employee information shown in the employee information DB 2021 . Specifically, for example, when the assignment decision module 2034 receives a request from the terminal device 30 to indicate the assignment of the specified employee together with the designation of the employee, the assignment determination module 2034 determines the assignment of the specified employee to the employee. It is determined based on the proficiency level and employee characteristics shown in the employee information DB 2021 .

Assignment determination module 2034 may determine assignments in any manner. For example, the control unit 203 may match the proficiency level and the employee's characteristics with a preset lookup table to determine the assignment destination. Also, for example, the assignment determination module 2034 may determine the assignment destination by inputting the proficiency level and the employee's characteristics into the learned model. At this time, the learned model has been trained to output assignments based on, for example, proficiency level and employee characteristics. Note that the assignment determination module 2034 may propose suitable industries and work contents to employees based on information stored in the employee information DB 2021 .

The shift setting module 2035 determines an employee's shift based on the employee information shown in the employee information DB 2021 . For example, when the assignment determination module 2034 receives a designation of a predetermined day or a predetermined period from the terminal device 30, the employee's work schedule on the designated day or period is displayed in the employee information DB 2021. Determined based on working hours.

The presentation module 2036 presents information managed by the server 20 to the administrator via the terminal device 30 . The presentation module 2036 also presents, for example, real-time information about on-going vocational training to the administrator via the terminal device 30 .

<2 Data structure>
5 to 7 are diagrams showing the data structure of the database stored by the server 20. FIG. It should be noted that FIGS. 5 to 7 are examples and do not exclude data that are not shown.

FIG. 5 is a diagram showing the data structure of the employee information DB 2021. As shown in FIG. As shown in FIG. 5, each record of the employee information DB 2021 includes, for example, an item "employee ID", an item "employee name", an item "sex", an item "date of employment", The item "department", the item "work content", the item "average stress value", the item "maximum stress value", the item "proficiency level", the item "aptitude", and the item "recommended work hours". include.

The item "employee ID" indicates information for identifying an employee.

The item "employee name" indicates the name of the employee.

The item "gender" indicates the gender of the employee.

The item "date of employment" indicates the date when the employee joined the company.

The item "affiliation" indicates the employee's affiliation.

The item "work content" indicates the content of work evaluated for proficiency and characteristics through vocational training. For example, a plurality of tasks for each employee are stored in the item "work content". In the example shown in FIG. 5, there is no description of the type of business, but for example, the business is associated with the type of business.

The item "average stress value" and the item "maximum stress value" indicate values as an example of index values for stress values. The item "average stress value" stores, for example, the average stress value calculated based on the information measured in the vocational training course corresponding to the work content. The item "maximum stress value" stores, for example, the maximum stress value calculated based on the information measured in the job content and the corresponding vocational training course.

The item "proficiency level" indicates the degree to which the employee is proficient in the corresponding work. The item "proficiency" is assessed on the basis of vocational training. Specifically, the item "proficiency level" is evaluated based on, for example, an index value calculated based on vocational training and the result of vocational training.

The item "suitability" indicates the degree of suitability of the employee for the corresponding work. The item "Aptitude" is assessed on the basis of vocational training. Specifically, the item "aptitude" is evaluated based on, for example, an index value calculated based on vocational training and the result of vocational training.

The item "recommended working time" indicates the time during which the employee can work continuously. The item "recommended work time" is calculated based on the item "suitability", for example.

The items "Average stress value", "Maximum stress value", "Proficiency level", "Aptitude", and "Recommended work hours" store values acquired in the past for each work content. good too.

FIG. 6 is a diagram showing the data structure of the training information DB 2022. As shown in FIG. As shown in FIG. 6, each record of the training information DB 2022 includes, for example, the item “employee ID”, the item “training date”, the item “training ID”, the item “Time”, and the item “heart rate , the item 'perspiration amount', the item 'body temperature', the item 'stress value', and the item 'achievement level'.

The item "employee ID" indicates information for identifying an employee.

The item "training date" indicates the date on which the vocational training was conducted.

The item "Training ID" indicates information for identifying the vocational training that was attended. Specifically, the item “training ID” indicates, for example, information for identifying the vocational training course taken.

The item "Time" indicates the elapsed time from the start of vocational training. Specifically, the item "Time" indicates, for example, the elapsed time since the start of the vocational training course selected by the student.

The item "heart rate" indicates values measured by the pulse wave sensor 103 during vocational training.

The item "perspiration amount" indicates a value measured by the perspiration amount sensor 102 during vocational training.

The item "body temperature" indicates the value measured by the temperature sensor 104 during vocational training.

The item "stress value" indicates a value representing the degree of stress calculated based on the measured value.

The item "Achievement" indicates how well the requirements imposed in the vocational training have been met. The item "achievement" is evaluated, for example, according to how well the task set in the vocational training course was completed. The item "achievement level" may be represented by a rank using alphabets or the like, or may be represented by points.

FIG. 7 is a diagram showing the data structure of the content information DB 2023. As shown in FIG. As shown in FIG. 7, each record of the content information DB 2023 includes an item “training ID”, an item “training name”, an item “classification”, an item “industry”, an item “work content”, an item Including "mission".

The item "training ID" indicates information for identifying vocational training.

The item "training name" indicates the name of the vocational training. Specifically, the item “training name” indicates, for example, the name of the vocational training course.

The item "classification" indicates the classification to which the vocational training belongs. Specifically, the item "classification" indicates, for example, the classification to which the vocational training course belongs. For example, the type of industry and business content associated with each classification may be set.

The item "industry" indicates the industry to which vocational training is associated. Specifically, the item "industry" indicates, for example, the industry to which the vocational training course is associated.

The item "work content" indicates the work content associated with vocational training. Specifically, the item "job content" indicates, for example, the job content associated with the vocational training course.

The item "duty" indicates a task set for vocational training. Specifically, the item "duties" indicates, for example, the duties set in the vocational training course. Each vocational training course, for example, has a plurality of duties.

<3 Operation>
Operations of the training device 10 and the server 20 will be described.

FIG. 8 is a schematic diagram showing an example of a configuration when a worker conducts vocational training using the training device 10. As shown in FIG. In the following description, as shown in FIG. 8, the case where the exercise device 10 has a perspiration sensor 102, a pulse wave sensor 103, and a temperature sensor 104 will be described. In addition, the main unit 11 displays a VR image regarding vocational training. The training device 10 stores first to third courses as vocational training. The first to third courses are associated with, for example, one business content of a predetermined industry.

(Vocational training by training device 10)
FIG. 9 is a flow chart showing an example of the operation of the training device 10 shown in FIG.

First, the student wears the main unit 11 on his or her head. The student holds the controller 101 in each hand, for example. At this time, a perspiration amount sensor 102 and a temperature sensor 104 are provided on the controller 101 held by the non-dominant hand. A pulse wave sensor 103 is attached to the non-dominant hand. When the student is ready for the vocational training, the student inputs an instruction to start the vocational training from the controller 101, for example.

In step S11, the control unit 190 of the training apparatus 10 performs calibration to compare the state of the student and the measured values of the sensors. Specifically, the control unit 190 causes the training control unit 193 to display, for example, an image that is assumed to make the student extremely tense. An image that is expected to make the student extremely nervous is, for example, an image that makes the student feel fear, such as an image of a high place, an image of an accident, and the like. The exercise control unit 193 acquires the measured values of the perspiration amount sensor 102, the pulse wave sensor 103, and the temperature sensor 104 at that time, and calculates the stress value based on the acquired measured values. The training control unit 193 sets the calculated stress value as the maximum stress value of the student.

The training control unit 193 displays, for example, an image that is expected to relax the student. Images that are supposed to make students relax are, for example, images of quiet forests or calm seas. The exercise control unit 193 acquires the measured values of the perspiration amount sensor 102, the pulse wave sensor 103, and the temperature sensor 104 at that time, and calculates the stress value based on the acquired measured values. The training control unit 193 sets the calculated stress value as the minimum stress value of the student.

The training control unit 193 displays, for example, an image for making the student perform a simple exercise. The training control unit 193 acquires the tendency of the amount of perspiration of the student based on the measured value of the temperature sensor 104 at that time.

In step S12, the control unit 190 starts vocational training. For example, the training control unit 193 displays a selection screen for the first to third courses, and accepts course selection from the student. When a student selects a vocational training course, the training control unit 193 causes the main device 11 to display a VR image related to the selected vocational training course.

FIG. 10 is a flow chart representing an example of the operation of the training device 10 during exercise shown in FIG. In the training shown in FIG. 10 , the control unit 190 accepts an avatar operation in the VR video displayed on the display panel 141 .

In step S121, the control unit 190 uses the transmission/reception unit 192 to acquire measurement data measured by the perspiration amount sensor 102, the pulse wave sensor 103, and the temperature sensor 104 worn by the student at predetermined intervals. The control unit 190 corrects the acquired measurement data regarding the amount of perspiration by the training control unit 193 based on the tendency of the amount of perspiration of the student obtained by the calibration.

In step S122, the training control unit 193 calculates a stress value based on the corrected information, for example. Specifically, for example, the exercise control unit 193 calculates the stress value based on the pulse wave measured by the pulse wave sensor 103, the body temperature measured by the temperature sensor 104, and the corrected amount of perspiration. The training control unit 193 standardizes the calculated stress values, for example, based on the minimum and maximum stress values obtained by calibration. The exercise control unit 193 stores the acquired pulse wave, body temperature, corrected perspiration amount, and standardized stress value in the exercise information 182 . The control unit 190 uses the transmission/reception unit 192 to transmit the acquired pulse wave, body temperature, corrected perspiration amount, and standardized stress value to the server 20 .

Server 20 stores the data received from training device 10 in training information DB 2022 .

In step S123, the control unit 190, through the training control unit 193, determines whether or not the vocational training course has ended. If not completed, the training control unit 193 repeats the processing of steps S121 to S123. When finished, the process proceeds to step S124.

In step S124, the control unit 190 causes the training control unit 193 to give an achievement level according to the task solved in the vocational training course, for example. The training control unit 193 stores the given achievement level in the training information 182 . The control unit 190 uses the transmission/reception unit 192 to transmit the given achievement level to the server 20, and terminates the processing for the vocational training course.

Server 20 stores the data received from training device 10 in training information DB 2022 .

For example, in step S12 shown in FIG. 9, when the next vocational training course is selected by the student, the control section 190 executes the process shown in FIG. 10 for the selected vocational training course.

The terminal device 30 shown in FIG. 8 accesses the training device 10 or the server 20 according to an instruction from an administrator who operates the terminal device 30 . The terminal device 30 causes the display of the terminal device 30 to display the state of the vocational training being carried out according to the instruction from the manager. For example, on the display of the terminal device 30, the vocational training in progress is displayed so as to be selectable. When vocational training is selected by the manager, the terminal device 30 requests information about the selected vocational training, such as VR video during training, amount of perspiration, pulse wave, body temperature, and stress value from the server 20 . The terminal device 30 may request information about the selected vocational training from the training device 10 .

The training device 10 or the server 20 that receives a request for information on vocational training from the terminal device 30 transmits the requested information to the terminal device 30 . The terminal device 30 receives the information and causes the display to display an image based on the received information.

FIG. 11 is a schematic diagram showing an example of an image displayed on the display of the terminal device 30 shown in FIG. The images shown in FIG. 11 include a VR play video 31 displayed to the student, a chart 32 showing changes in the amount of perspiration, a chart 33 showing changes in heart rate, a chart 34 showing changes in stress values, A display area 35 representing the current measurement is displayed. An administrator who operates the terminal device 30 visually recognizes the image displayed on the display of the terminal device 30 and checks the status of the vocational training.

At least one of the VR play video 31, the charts 33 to 34, and the display area 35 can be hidden according to an instruction from the administrator. As a result, the manager can confirm the state of the vocational training by narrowing down the information to be visually recognized.

(Evaluation of student characteristics and proficiency level by server 20)
FIG. 12 is a flow chart showing an example of the operation when the server 20 shown in FIG. 8 evaluates the characteristics and proficiency of the student. The control unit 203 executes the processing shown in FIG. 8, for example, in the following cases.
・When receiving a notification that the vocational training for the student has been completed ・When the administrator who operates the terminal device 30 requests the evaluation of the characteristics and proficiency level of the predetermined student ・Predetermined cycle ・When the specified time is reached

In step S13, the control unit 203 calculates the index value of the stress value for the predetermined business content. Specifically, for example, the control unit 203 causes the analysis module 2033 to refer to the training information DB 2022, and based on the stress values calculated while taking the first to third courses, the first to third An average stress value and a maximum stress value are calculated for the work content associated with the course. The analysis module 2033 stores the calculated average stress value and maximum stress value in the employee information DB 2021 .

In step S14, the control unit 203 estimates the aptitude and proficiency level for the predetermined business content. Specifically, for example, the control unit 203 uses the analysis module 2033 to refer to the training information DB 2022, and obtains the degree of achievement of the first to third courses and the work contents associated with the first to third courses. The calculated average stress value and maximum stress value are collated with a preset lookup table to acquire aptitude. Also, for example, the analysis module 2033 calculates the degree of achievement of the first to third courses, and the average stress value and maximum stress value calculated for the work contents associated with the first to third courses, and stores them in the learned model. You can acquire aptitude by entering Analysis module 2033 stores the evaluated suitability in employee information DB 2021 .

The analysis module 2033 analyzes past changes in achievement levels of the first to third courses, and past changes in average stress values and maximum stress values calculated for work content associated with the first to third courses. Aptitude may be obtained based on

In addition, the analysis module 2033, for example, calculates the transition of the achievement levels of the first to third courses from the past, and the average stress value and the maximum stress value calculated for the work content associated with the first to third courses. The proficiency level is calculated by inputting the transition from the past into a predetermined function. Also, for example, the analysis module 2033 calculates the transition of the achievement levels of the first to third courses from the past, and the average stress value and the maximum stress value calculated for the work contents associated with the first to third courses. You may acquire a proficiency level by inputting the transition from the past into the trained model. The analysis module 2033 stores the evaluated proficiency level in the employee information DB 2021 .

In step S15, the control unit 203 calculates a recommended work time for a predetermined work content according to the evaluated aptitude. Specifically, for example, the control unit 203 causes the analysis module 2033 to collate the evaluated aptitude with a preset lookup table, and acquires the recommended work time. Also, for example, the analysis module 2033 may acquire the recommended work time by inputting the estimated aptitude into the trained model. The analysis module 2033 stores the acquired recommended work time in the employee information DB 2021 and terminates the process.

The terminal device 30 shown in FIG. 8 accesses the server 20 according to instructions from the administrator who operates the terminal device 30 . The terminal device 30 causes the display of the terminal device 30 to display information about the employee's characteristics and proficiency level in accordance with instructions from the manager.

FIG. 13 is a schematic diagram showing an example of an image displayed on the display of the terminal device 30 shown in FIG. In the image shown in FIG. 13, a table 300 representing information about employees is displayed. An administrator who operates the terminal device 30 visually recognizes the image displayed on the display of the terminal device 30 and confirms the information about the employee.

(Determination of Employee Assignment by Server 20)
The server 20 determines an employee's assignment in response to a request from an administrator who uses the terminal device 30 . Specifically, for example, the manager operates the terminal device 30, designates a predetermined employee while referring to the screen shown in FIG. Request 20. The control unit 203 uses the reception control module 2031 to receive an instruction specifying an employee from the terminal device 30 . The reception control module 2031 also receives a request from the terminal device 30 to present an employee's assignment. The assignment determination module 2034 determines the assignment destination of the designated employee based on the employee information DB 2021 .

For example, the assignment determination module 2034 compares the average stress value, maximum stress value, proficiency level, and aptitude acquired for each job content with a preset lookup table, and determines the assignment destination of the specified employee. to decide. Also, for example, the assignment determination module 2034 may determine the assignment destination by inputting the average stress value, the maximum stress value, the proficiency level, and the aptitude acquired for each job content into the learned model. .

The presentation module 2036 transmits information about the determined assignment destination to the terminal device 30 and displays it on the display of the terminal device 30 .

(Determination of employee shift by server 20)
The server 20 determines employee shifts in response to a request from an administrator who uses the terminal device 30 . Specifically, for example, the administrator operates the terminal device 30 to specify the day or period for which it is necessary to determine who will work for a given task. The manager, for example, requests the server 20 to designate available employees and suggest shifts for the employees.

The control unit 203 uses the reception control module 2031 to receive from the terminal device 30 an instruction specifying the business content. Also, the reception control module 2031 receives from the terminal device 30 an instruction designating a predetermined day or a predetermined period. The reception control module 2031 also receives an instruction to designate an employee from the terminal device 30 . The reception control module 2031 also receives a request to present an employee's shift from the terminal device 30 . The shift setting module 2035 sets an employee's shift for a designated day or period based on the employee information DB 2021 .

For example, the shift setting module 2035 sets the employee's work schedule for the specified work content based on the recommended work hours. The assignment determination module 2034 sets the work schedule of the employee by filling in the work slots for the employee based on the recommended working hours.

The presentation module 2036 transmits information about the set employee's shift to the terminal device 30 and displays it on the display of the terminal device 30 .

As described above, in the above-described embodiment, the main unit 11 displays the first image regarding vocational training on the display panel 141 . Main unit 11 receives an operation via controller 101 from a student who is taking vocational training. Main unit 11 acquires first biological information including at least the amount of perspiration of the student viewing the first image, which is measured by perspiration amount sensor 102 , pulse wave sensor 103 and temperature sensor 104 . The main unit 11 calculates a first value related to the student's stress from the first biological information. Based on the first value, the server 20 estimates the student's characteristics of the vocational training and the corresponding job. As a result, it becomes possible to grasp from the vocational training which tasks employees find stressful and which tasks they are not good at.

Currently, in certain industries, there are various problems such as diversification of work and operations, increase in cost and complexity of machines used in work, and diversification of skill levels of workers. In such an environment, vocational training using VR images can handle a variety of situations, does not require the preparation of actual work machines, and can be taken repeatedly according to the level of proficiency. There are merits such as that there is no accident and that there is no accident. By using vocational training using VR images, it is possible to improve the capabilities of workers and reduce human errors during work while reducing the cost of training workers. Furthermore, according to the vocational training using the training device 10 according to the present embodiment, since it becomes clear which work the employee feels stressful and which work the employee is not good at, appropriate personnel allocation is possible. In addition, efficient training of workers becomes possible. Therefore, more effective vocational training can be implemented.

Further, in the above-described embodiment, the main device 11 displays the second image in which a predetermined stress is assumed during calibration. Main unit 11 acquires second biological information including at least the amount of perspiration of the student viewing the second image, which is measured by perspiration amount sensor 102 , pulse wave sensor 103 , and temperature sensor 104 . Main unit 11 calculates the minimum and maximum stress values based on the second biological information. Main unit 11 calculates a first value from the first biological information and the minimum and maximum stress values. This enables accurate measurement based on the state of the student during the course. That is, it becomes possible to calculate the stress value with high accuracy.

Further, in the above-described embodiment, main device 11 displays the third image for moderate exercise during calibration. Main unit 11 acquires third biological information including at least the amount of perspiration of the student viewing the third image, which is measured by perspiration amount sensor 102 , pulse wave sensor 103 and temperature sensor 104 . Main unit 11 corrects the first biometric information based on the third biometric information. As a result, the training apparatus 10 can accurately calculate the stress value even when the amount of sweat differs depending on the physical constitution of the student.

In addition, in the above-described embodiment, main unit 11 evaluates the degree of achievement of vocational training when vocational training is completed. The server 20 estimates the characteristic based on the first value and the degree of achievement. This enables the server 20 to more accurately estimate the characteristics of the student.

Further, in the above embodiment, the main unit 11 displays a VR image. As a result, the sense of reality of the vocational training is improved, so that the stress value can be calculated more accurately.

Further, in the above-described embodiment, the perspiration sensor 102 is a sensor that measures the humidity of the space formed including a part of the student's body. More specifically, the perspiration sensor 102 has a first path for supplying air to the space, a second path for discharging air from the space, and humidity sensors installed in the first path and the second path. As a result, a small, inexpensive, and highly accurate perspiration sensor capable of measuring the perspiration amount is realized, so that the burden of wearing the perspiration sensor 102 is reduced when using the perspiration sensor 102 in vocational training.

Further, in the above embodiment, the perspiration amount sensor 102 is provided in the grip portion of the controller 101 . As a result, the amount of perspiration is measured when the controller 101 is operated, so that the burden on the student can be reduced.

Further, in the above embodiment, the server 20 estimates the student's aptitude for vocational training and corresponding work based on the first value. As a result, the server 20 can clarify the work that the student is good at and the work that the student is not good at.

Further, in the above embodiment, the length of time that the student can work continuously is calculated based on aptitude. As a result, the server 20 can quantitatively express how long the student is engaged in work.

Further, in the above embodiment, the server 20 estimates aptitude based on the first value calculated in the past vocational training and the first value calculated in the current vocational training. This makes it possible to estimate aptitude more accurately.

Further, in the above embodiment, the server 20 estimates the proficiency level of the student for the work corresponding to the vocational training based on the first value and the achievement level. This makes it possible to quantitatively express how much skill the student has.

Further, in the above embodiment, the server 20 calculates the proficiency level based on the first value and achievement level calculated in past vocational training and the first value and achievement level calculated in current vocational training. presume. This makes it possible to estimate the proficiency level more accurately.

Further, in the above embodiment, the server 20 determines the student's assignment based on the characteristics of the student. As a result, the server 20 can appropriately assign students to the students.

Further, in the above embodiment, the server 20 determines the work schedule of at least one of the multiple trainees based on the hours calculated for the multiple trainees who have taken the vocational training. As a result, the server 20 can arrange shifts in an appropriate arrangement. In addition, the server 20 can reduce the labor of the manager when making shifts.

Further, in the above embodiment, the server 20 proposes participation in vocational training based on the characteristics of the student. As a result, trainees can efficiently receive vocational training.

Further, in the above-described embodiment, the server 20 proposes participation in vocational training based on the proficiency level of the student. This enables trainees to efficiently repeat vocational training until they acquire skills.

Further, in the above-described embodiment, the server 20 determines that vocational training is unnecessary based on the proficiency level and characteristics of the student. As a result, the server 20 can reduce useless participation in vocational training.

<4 Modifications>
(Operation using hand tracking)
In the above embodiment, an example in which the student operates the avatar in the VR video using the controller 101 has been described. However, the operation of the avatar is not limited to that by the controller 101 . If the training device 10 has a hand tracking function, the student may operate the avatar by moving the hand.

FIG. 14 is a schematic diagram showing the hand tracking function of the training device 10. As shown in FIG. In the example shown in FIG. 14, the training device 10 obtains the movement of the student's hand by photographing the student's hand with the camera 160 installed in the main unit 11 . The training device 10, for example, expresses the movement of the student's hand as the movement of the avatar's hand in the VR video. Also, the training device 10 moves the avatar according to the movement of the student's hand.

The hands targeted for hand tracking may be both hands or one hand of the student. If the exercise device 10 tracks movements of one hand, the other hand may hold the controller 101 . When the controller 101 is held in the other hand, the training apparatus 10 may accept input by hand tracking and input by the controller 101 .

In the above embodiment, the perspiration amount sensor 102 and the temperature sensor 104 are mounted on the controller 101, for example. If both hands of the student are targeted for hand tracking, the student cannot hold the controller 101 . Therefore, when both hands of the student are to be subjected to hand tracking, the perspiration amount sensor 102 and the temperature sensor 104 are separately attached to the student.

(Acquisition of employee characteristics and proficiency by hand tracking)
In the above embodiment, the proficiency level and employee characteristics are acquired based on the amount of perspiration measured by the perspiration amount sensor 102, the pulse wave measured by the pulse wave sensor 103, and the body temperature measured by the temperature sensor 104. I explained an example. However, proficiency and employee characteristics may be obtained based on hand tracking results.

For example, the control unit 190 of the training device 10 calculates the stress value based on information regarding hand movements obtained by hand tracking. Specifically, for example, the control unit 190 compares the information about the movement of the student's hand with the ideal hand movement stored in advance. The control unit 190 detects, for example, hand and finger tremors, hand movement speed, hand and finger positions, etc. from information related to hand movement, and calculates a stress value based on the difference from ideal movement. . Note that the control unit 190 may calculate the stress value from the characteristics of the student's hand movement regardless of the comparison with the ideal movement. At this time, the image related to vocational training may be, for example, an image related to medical vocational training.

The control unit 203 of the server 20 analyzes the information acquired in the vocational training by the training device 10, and acquires the characteristics of the trainee who has taken the vocational training. In addition, the control unit 203 analyzes the information acquired in the vocational training, and acquires the proficiency level of the trainee regarding the work corresponding to the vocational training that has been attended.

Specific operations of the training device 10 are, for example, as follows.

The controller 190 of the training apparatus 10 first performs calibration to compare the state of the student and the movement of the hand. Specifically, the control unit 190 causes the training control unit 193 to display, for example, an image that is assumed to make the student extremely tense. The training control unit 193 acquires information about the movement of the hand at that time, and calculates the stress value based on the acquired information. The training control unit 193 sets the calculated stress value as the maximum stress value of the student.

The training control unit 193 displays, for example, an image that is expected to relax the student. The training control unit 193 acquires information about the movement of the hand at that time, and calculates the stress value based on the acquired information. The training control unit 193 sets the calculated stress value as the minimum stress value of the student.

The training control unit 193 displays, for example, an image for making the student perform a simple exercise. The training control unit 193 acquires the movement tendency of the student based on the information about the hand movement at that time.

After completing the calibration, the control unit 190 starts vocational training. The control unit 190 receives the operation of the avatar in the VR video displayed on the display panel 141 by the training control unit 193 by tracking the movement of the student's hand. The training control unit 193 stores information about the tracked hand motion in the training information 182 at predetermined intervals, for example. The training control unit 193 corrects the acquired information about the hand movement based on the tendency of the student's movement obtained by calibration, for example.

The training control unit 193, for example, calculates the stress value based on the acquired information about the hand motion. The training control unit 193 standardizes the calculated stress values, for example, based on the minimum and maximum stress values obtained by calibration. The training control unit 193 stores the standardized stress value in the training information 182. FIG. The control unit 190 uses the transmission/reception unit 192 to transmit the information on the hand movement and the standardized stress value to the server 20 .

The training control unit 193, for example, gives a degree of achievement according to the difference between the task solved in the vocational training course and the ideal hand movement. The training control unit 193 stores the given achievement level in the training information 182 . The control unit 190 uses the transmission/reception unit 192 to transmit the given achievement level to the server 20 .

Server 20 stores the data received from training device 10 in training information DB 2022 .

The terminal device 30 shown in FIG. 8 displays, on the display of the terminal device 30, the state of the vocational training being carried out, for example, in response to an instruction from an administrator who operates the terminal device 30. FIG.

FIG. 15 is a schematic diagram showing an example of an image displayed on the display of the terminal device 30 shown in FIG. The images shown in FIG. 15 include the VR play video 31 displayed to the student, charts 36 and 37 showing transitions of information grasped from information on hand movements, charts 34 showing transitions of stress values, A display area 38 representing the current measurement is displayed. In FIG. 15, the chart 36 displays, for example, numerical values relating to hand tremors, and the chart 37 displays, for example, numerical values relating to hand movement speed. Information displayed in these charts is not limited to these. An administrator who operates the terminal device 30 visually recognizes the image displayed on the display of the terminal device 30 and checks the status of the vocational training.

At least one of the VR play video 31, the charts 34, 36, 37, and the display area 38 can be hidden according to an instruction from the administrator. As a result, the manager can confirm the state of the vocational training by narrowing down the information to be visually recognized.

Since the student's proficiency level and the employee's characteristics are evaluated based on hand movements obtained by hand tracking, perspiration sensors, pulse wave sensors, temperature sensors, etc. are not required. Therefore, it becomes possible to further lower the hurdles to implement vocational training. Also, it is possible to reduce the cost of introducing a system for vocational training.

In the above description, the case of estimating the student's proficiency level based on the stress value has been described. The control unit 203 may estimate the proficiency level by comparing not only the stress value but also the own past hand movements.

<5 Other Embodiments>
The server 20 may propose vocational training to employees based on information stored in the training information DB 2022 . For example, the control unit 203 suggests that an employee who has a job with a low proficiency level take vocational training associated with the job with a low proficiency level. If there is an employee whose proficiency level is not low but whose average stress value and maximum stress value are higher than predetermined values, the control unit 203 associates the employee with the corresponding job. Suggest taking vocational training.

In the case of an organization with many employees, it is possible for employees to engage in high aptitude tasks instead of low aptitude tasks. However, in an organization with a small number of employees, it may not be possible to substitute work. In such cases, although aptitude is low, it may be desired to increase proficiency. The control unit 203 may suggest that an employee who has a job with low aptitude and low proficiency take vocational training associated with the job. In other words, the control unit 203 increases training for work that the employee feels weak or stressful work.

Conversely, the control unit 203 may suggest that an employee who has a highly aptitude job take vocational training associated with the job. In other words, the control unit 203 increases training for jobs that the employee feels good at or jobs that are less stressful.

Also, the server 20 may determine the end of vocational training for the employee based on the information stored in the training information DB 2022 . For example, when the employee's proficiency level reaches a predetermined level, the control unit 203 informs the employee that he/she no longer needs to receive vocational training for the relevant work. Note that the control unit 203 does not determine that the vocational training is completed when the average stress value and the maximum stress value are, for example, higher than a predetermined threshold even if the proficiency level has reached a predetermined level. good too.

In the above embodiment, a case has been described in which a vocational training course is associated with the business content of a predetermined type of industry. A vocational course may be structured in chapters so that the corresponding tasks change over time. In this case, the control unit 203 associates the temporal fluctuation of the stress value with the chapter structure of the vocational training course to estimate the characteristics of the student's work.

In the above-described embodiment, the characteristics of the trainee are estimated for the work associated with the vocational training that was attended. The control unit 203 may estimate the characteristics of a job for which vocational training has not been taken, according to the degree of similarity with the job whose characteristics are estimated.

Although several embodiments of the present disclosure have been described above, these embodiments can be implemented in various other forms, and various omissions, replacements, and modifications are possible without departing from the spirit of the invention. It can be performed. These embodiments and their modifications are intended to be included in the invention described in the claims and their equivalents as well as included in the scope and gist of the invention.

<Appendix>
The items described in the above embodiments will be added below.
(Appendix 1)
Means (11) for displaying a first image related to vocational training; Means (102) for acquiring biometric information; Means (11) for calculating a first value related to stress of the trainee from the first biometric information; means (20) for estimating properties.
(Appendix 2)
The display means displays a second image in which a predetermined stress is assumed, and the acquisition means acquires and calculates second biological information including at least the amount of perspiration of the student who visually recognizes the second image. The system according to (Appendix 1), wherein a second value related to stress is calculated based on the second biometric information, and the first value is calculated from the first biometric information and the second value.
(Appendix 3)
The means for displaying displays a third image for moderate exercise, and the means for obtaining obtains the first biological information based on the third biological information including at least the amount of perspiration of the student who visually recognizes the third image. (Appendix 1) or (Appendix 2).
(Appendix 4)
Any of (Appendix 1) to (Appendix 3), wherein when the vocational training is completed, the means for evaluating the degree of achievement of the vocational training is provided, and the means for estimating estimates the characteristic based on the first value and the degree of achievement The system described in .
(Appendix 5)
The system according to any one of (Appendix 1) to (Appendix 4), wherein the means for displaying displays a VR image.
(Appendix 6)
The system according to any one of (Appendix 1) to (Appendix 5), wherein the acquiring means acquires the amount of perspiration using a sensor that measures the humidity of a space formed by including a part of the student's body.
(Appendix 7)
6. The system of Clause 6, wherein the sensor comprises a first path supplying air to the space, a second path exhausting air from the space, and humidity sensors installed in the first path and the second path.
(Appendix 8)
The system according to (Appendix 6) or (Appendix 7), wherein the means for receiving an operation is a controller, and the sensor is a space provided in a grip portion of the controller.
(Appendix 9)
The system according to any one of (Appendix 1) to (Appendix 8), wherein the estimating means estimates the student's aptitude for the vocational training and the corresponding job based on the first value.
(Appendix 10)
The system according to (Appendix 9), wherein the estimating means calculates the time that the student can continuously engage in work based on aptitude.
(Appendix 11)
The system according to (Appendix 10), wherein the means for estimating estimates aptitude based on a first value calculated in past vocational training and a first value calculated in current vocational training.
(Appendix 12)
The system according to (Appendix 4), wherein the estimating means estimates the student's proficiency level of the vocational training and corresponding work based on the first value and the achievement level.
(Appendix 13)
The estimating means estimates the proficiency level based on the first value and achievement level calculated in the past vocational training and the first value and achievement level calculated in the current vocational training (Appendix 12 ).
(Appendix 14)
13. The system according to any one of (Appendix 1) to (Appendix 13), comprising means for determining student assignment based on student characteristics.
(Appendix 15)
The system according to (Appendix 10), comprising means for determining the work schedule of at least one of the plurality of trainees based on the hours calculated for the plurality of trainees who have undergone vocational training.
(Appendix 16)
The system according to any one of (Appendix 1) to (Appendix 15), comprising means for suggesting participation in vocational training based on characteristics of the student.
(Appendix 17)
The system according to (Appendix 12) or (Appendix 13), comprising means for suggesting participation in vocational training based on the proficiency level of the student.
(Appendix 18)
The system according to (Appendix 12), (Appendix 13), or (Appendix 17), comprising means for determining that taking vocational training is unnecessary based on the student's proficiency and characteristics.
(Appendix 19)
Means for displaying a first image related to vocational training; Means for receiving an operation from a trainee based on the hand movement of the trainee taking the vocational training; A system comprising: means for calculating a value; and means for estimating a student's characteristic of the training and corresponding task based on the first value.
(Appendix 20)
An information processing apparatus comprising a control unit and a storage unit, wherein the control unit causes a display unit to display a first image related to vocational training; An information processing apparatus for executing a step of acquiring first biometric information including at least the amount of perspiration of a student viewing a first image, and a step of calculating a first value related to stress of the student from the first biometric information.
(Appendix 21)
A method executed in a system including a plurality of information processing apparatuses each having a control unit and a storage unit, wherein at least one of the plurality of information processing apparatuses causes a display unit to display a first image related to vocational training. a step of receiving an operation from a student who is taking vocational training; a step of acquiring first biometric information of the student who visually recognizes the first image, including at least the amount of perspiration; and estimating a student characteristic of the training and corresponding task based on the first value.
(Appendix 22)
A program to be executed by a computer comprising a processor and a memory, the program comprising a step of causing the processor to display a first image related to vocational training on a display unit, and an operation by a student who is to undergo vocational training. The step of receiving, the step of acquiring first biometric information including at least the amount of perspiration of the student viewing the first image, and the step of calculating a first value related to stress of the student from the first biometric information are executed. program.

DESCRIPTION OF SYMBOLS 1... System 10... Training apparatus 101... Controller 102... Perspiration sensor 103... Pulse wave sensor 104... Temperature sensor 11... Main unit 12... Communication IF
DESCRIPTION OF SYMBOLS 120... Communication part 14... Output device 141... Display panel 15... Memory 16... Storage 150... Motion sensor 160... Camera 17... Sound processing part 171... Microphone 172... Speaker 180... Storage part 181... Employee information 182... Training information 183 Content information 19 Processor 190 Control unit 191 Operation reception unit 192 Transmission/reception unit 193 Training control unit 20 Server 22 Communication IF
23 input/output IF
25... Memory 26... Storage 29... Processor 201... Communication unit 202... Storage unit 2021... Employee information DB
2022...Training information DB
2023 Content information DB
2024... Shift information DB
203...control unit 2031...reception control module 2032...transmission control module 2033...analysis module 2034...assignment determination module 2035...shift setting module 2036...presentation module 30...terminal device 80...network

Claims (22)

means for displaying a first image relating to vocational training;
means for receiving an operation from a trainee who attends the vocational training;
means for acquiring first biological information including at least perspiration amount of the student who visually recognizes the first image;
means for calculating a first value related to the student's stress from the first biological information;
means for estimating characteristics of said student for said training and corresponding tasks based on said first value. The display means displays a second image in which a predetermined stress is assumed,
the acquiring means acquires second biometric information including at least the amount of perspiration of the student viewing the second image;
2. The calculating means according to claim 1, wherein said calculating means calculates a second value related to stress based on said second biological information, and calculates said first value from said first biological information and said second value. system. The means for displaying displays a third image for moderate exercise,
3. The system according to claim 1, wherein said acquiring means corrects said first biometric information based on third biometric information including at least perspiration amount of said student who visually recognizes said third image. Upon completion of the vocational training, comprising means for evaluating the degree of achievement of the vocational training,
4. A system according to any preceding claim, wherein said means for estimating estimates said characteristic based on said first value and said degree of achievement. 5. The system according to any one of claims 1 to 4, wherein said means for displaying displays a VR image. 6. The system according to any one of claims 1 to 5, wherein said acquiring means acquires the amount of perspiration using a sensor that measures the humidity of a space formed by including a part of said student's body. 7. The system of claim 6, wherein the sensor comprises a first path supplying air to the space, a second path exhausting air from the space, and a humidity sensor located in the first path and the second path. the means for receiving the operation is a controller;
8. A system according to claim 6 or 7, wherein said sensor is provided with said space in the grip of said controller. 9. The system according to any one of claims 1 to 8, wherein the means for estimating estimates the suitability of the student for the vocational training and corresponding work based on the first value. 10. The system according to claim 9, wherein said estimating means calculates, based on said aptitude, the time during which said student is continuously engaged in said task. 11. The system according to claim 10, wherein said estimating means estimates said aptitude based on a first value calculated in past vocational training and a first value calculated in current vocational training. 5. The system according to claim 4, wherein said means for estimating estimates a proficiency level of said trainee for a task corresponding to said vocational training based on said first value and said achievement level. wherein the means for estimating estimates the proficiency level based on the first value and achievement level calculated in past vocational training and the first value and achievement level calculated in current vocational training; Item 13. The system according to Item 12. 14. A system according to any one of claims 1 to 13, comprising means for determining placement of said student based on characteristics of said student. 11. The system according to claim 10, further comprising means for determining a work schedule for at least one of said plurality of trainees based on said hours calculated for said plurality of trainees who have taken said vocational training. 16. The system according to any one of claims 1 to 15, comprising means for suggesting participation in vocational training based on characteristics of said student. 14. A system according to claim 12 or 13, comprising means for suggesting participation in vocational training based on the student's proficiency level. 18. The system according to claim 12, 13 or 17, comprising means for determining that vocational training is unnecessary based on the proficiency level and characteristics of the student. means for displaying a first image relating to vocational training;
means for receiving an operation from the trainee based on the movement of the hands of the trainee who attends the vocational training;
means for calculating a first value related to the student's stress from the student's hand movements;
means for estimating characteristics of said student for said training and corresponding tasks based on said first value. An information processing device comprising a control unit and a storage unit, wherein the control unit
causing a display to display a first image related to vocational training;
a step of receiving an operation from a trainee who attends the vocational training;
obtaining first biological information including at least the amount of perspiration of the student who visually recognizes the first image;
and calculating a first value related to the stress of the student from the first biometric information. A method executed in a system comprising a plurality of information processing apparatuses each comprising a control unit and a storage unit, wherein at least one of the plurality of information processing apparatuses includes:
causing a display to display a first image related to vocational training;
a step of receiving an operation from a trainee who attends the vocational training;
obtaining first biological information including at least the amount of perspiration of the student who visually recognizes the first image;
calculating a first value related to the student's stress from the first biometric information;
estimating a characteristic of the student for the training and corresponding task based on the first value. A program to be executed by a computer comprising a processor and a memory, the program comprising:
causing a display to display a first image related to vocational training;
a step of receiving an operation from a trainee who attends the vocational training;
obtaining first biological information including at least the amount of perspiration of the student who visually recognizes the first image;
and calculating a first value related to the student's stress from the first biological information.

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