Analytical apparatus and analytical method

Abstract

Automatically generates features for evaluating work performance. [Solution] An analysis device for analyzing work movements, comprising a computer having a calculation device that performs predetermined calculation processing and a storage device connected to the calculation device, comprising: a shape input unit that receives shape information of a work object; an object position and orientation input unit that receives position and orientation information of the work object; an motion input unit that receives data of work movements by a worker; a basic shape extraction unit in which the calculation device extracts basic shapes that constitute the shape of the work object; a work motion conversion unit in which the calculation device converts the movements of the worker into relative movements from the coordinate system of the work object; and a motion decomposition unit in which the calculation device decomposes the relative movements into movements belonging to the basic shapes and outputs the momentum of each decomposed motion component.

Description

Technical Field

[0001] The present invention relates to an analysis device for analyzing the actions of an operator.

Background Art

[0002] There is an action analysis method that measures the actions of an object or a work subject, three-dimensionally reconstructs the measured actions, and evaluates the work efficiency in detail, which is used in various fields such as device design.

[0003] As the background art in this technical field, there is the following prior art. Patent Document 1 (Japanese Unexamined Patent Application Publication No. 2020-177680) discloses a device for analyzing the work performed by an operator, which includes a position information acquisition unit that acquires first position information, which is depth image data output from a depth sensor that photographs the operator, and second position information, which is image data output from a digital camera that photographs the object; an action identification unit that identifies the position of the operator's hand from the first position information, identifies the position of the object from the second position information, and identifies the content of the action performed by the operator in the work based on the identified position of the operator's hand and the identified position of the object.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] Currently, data on on-site work is measured without omission, and work analysis engineers individually consider what to focus on in the work, evaluating it in terms of feature amounts and importance, and the man-hours required for evaluation are increasing. In order to apply work analysis to a large number of processes, it is necessary to automatically extract the features to be analyzed.

Means for Solving the Problems

[0006] A representative example of the invention disclosed in this application is as follows: an analytical device for analyzing work movements, comprising a computer having a calculation unit that performs predetermined calculation processing and a storage device connected to the calculation unit, and characterized by comprising: a shape input unit that receives shape information of a work object; an object position and orientation input unit that receives position and orientation information of the work object; an motion input unit that receives data of work movements by a worker; a basic shape extraction unit that extracts basic shapes that constitute the shape of the work object; a work motion conversion unit that converts the movements of the worker into relative movements from the coordinate system of the work object; and a motion decomposition unit that decomposes the relative movements into movements belonging to the basic shapes and outputs the momentum of each decomposed motion component. [Effects of the Invention]

[0007] According to one aspect of the present invention, work evaluation features can be automatically generated from work actions. Problems, configurations, and effects other than those described above will be clarified by the following description of the embodiments. [Brief explanation of the drawing]

[0008] [Figure 1] This block diagram shows the logical configuration of an analytical apparatus according to an embodiment of the present invention. [Figure 2] This is a block diagram of the computing device that constitutes the analytical apparatus according to an embodiment of the present invention. [Figure 3] This is a flowchart of the analysis process in an embodiment of the present invention. [Figure 4] This figure shows the basic shape extraction process in an embodiment of the present invention. [Figure 5] This figure shows an example of the output of the motion component output unit in an embodiment of the present invention. [Figure 6] This figure shows the importance analysis process for an embodiment of the present invention. [Figure 7] This figure shows an example of the output of the importance analysis process in an embodiment of the present invention. [Figure 8] This figure shows an example of a deviation alert screen according to an embodiment of the present invention. [Modes for carrying out the invention]

[0009] Figure 1 is a block diagram showing the logical configuration of the analytical device 100 in this embodiment.

[0010] The analysis device 100 of this embodiment includes a work subject motion input unit 101, an object position and orientation input unit 102, a work motion conversion unit 103, a shape input unit 104, a basic shape extraction unit 105, a motion decomposition unit 106, a motion component output unit 107, a work information input unit 108, a work importance analysis unit 109, a similar work extraction unit 110, a model motion generation unit 111, a spatial model input unit 112, a body model input unit 113, a comprehensive work score calculation unit 114, a comprehensive work score output unit 115, a deviation determination unit 116, a deviation alert image generation unit 117, and a video output unit 118.

[0011] The worker motion input unit 101 receives a camera image 201 showing the worker's movements, extracts hand skeletal motion information from the input camera image 201, and outputs the extracted hand skeletal motion information to the work motion conversion unit 103. The camera image 201 is a moving image captured by a depth image camera, with color and distance data attached to each pixel. For each frame of the moving image, the image information with color information attached is used to determine the position of each skeletal position in the image using an image processing AI (Artificial Intelligence) model that has learned the correspondence between the color image and the image coordinates of each feature point that makes up the hand, i.e., the wrist and each finger joint. Next, the distance information corresponding to the image coordinates is used to convert it into three-dimensional coordinates of each skeletal position in the camera coordinate system. The camera-specific intrinsic parameter matrix for converting from the image coordinate system to the camera coordinate system is obtained by calibration. In this case, the camera image is not limited to one camera; the position of each skeleton in the camera coordinate system for two or more simultaneously captured camera images may be estimated by an image processing AI model, and then converted into 3D coordinates of each skeleton position using the external parameter matrices of the two cameras that have been determined in advance. With this method, since only color information is used for each camera image, distance data does not need to be attached. The work subject motion input unit 101 may receive time-series data of position and posture indicating the work actions of the work subject, rather than camera images 201. The work subject is a worker or work robot handling an object. The worker's actions are determined from video images of the work taken with a depth image camera capable of acquiring distance information for each pixel, or from the actions of a body model analyzed from measurement results by a sensing device worn by the worker (for example, a sensor glove that detects the movement of the worker's fingers).

[0012] The object position and orientation input unit 102 receives a camera image 201 showing an object related to the work (e.g., a work object), extracts object motion information from the input camera image 201, and outputs the extracted object motion information to the work motion conversion unit 103. Markers are attached to the object related to the work in predetermined locations, and a correspondence relationship between the object related to the work and the marker is defined. The object motion information is obtained by detecting the area of ​​the marker from the camera image 201 and estimating the marker pattern and the position of the marker in the camera coordinate system. If it is not possible to attach a marker for reasons such as the object being too small, the object motion information may be estimated using an image processing AI model that has learned the correspondence relationship between the object's orientation and images from various directions generated using a video of the object or a three-dimensional model corresponding to the object. The object position and orientation input unit 102 may also receive time-series data of the position and orientation of the object related to the work (e.g., a work object) instead of a camera image 201. The time-series data of the position and orientation of the object related to the work may be represented by the movement of points in an object model that geometrically represents the object.

[0013] The work motion conversion unit 103 converts the movements of the worker (body model) input to the work subject motion input unit 101 from an absolute coordinate system to relative movements in a relative coordinate system. Specifically, it performs a process of subtracting a three-dimensional vector representing the relative position between the camera coordinate system and the object so that the hand skeletal motion information in the camera coordinate system becomes the relative position to the position of the object motion information at the same time.

[0014] The shape input unit 104 receives an object model that represents the shape of an object related to the work. The object model is a geometric model that represents the object related to the work.

[0015] The basic shape extraction unit 105 decomposes the object model input to the shape input unit 104 into basic shapes and extracts one or more basic shapes from the object model representing the shape of the object related to the work.

[0016] The operation decomposition unit 106 decomposes the relative operation converted by the work operation conversion unit 103 into the movements of the motion components belonging to the basic shape decomposed by the basic shape extraction unit 105. When the amount of the relative operation is smaller than a predetermined threshold, the operation decomposition unit 106 may determine that the part of the work subject and the work object are moving together, and decompose the operation of the work object into the motion belonging to the basic shape.

[0017] The motion component output unit 107 outputs the motion components decomposed by the operation decomposition unit 106. An output example of the motion component output unit 107 will be described later with reference to FIG. 5.

[0018] The work information input unit 108 receives the input of the information of the work to be analyzed. The work information input to the work information input unit 108 includes the motion components of the work in each work process included in the work and the information on the importance.

[0019] The work importance analysis unit 109 evaluates the work quality in each of the motion components 213 decomposed by the operation decomposition unit 106, and evaluates the importance of each motion component based on the evaluation result of the work quality by referring to the work importance information 214.

[0020] The similar work extraction unit 110 extracts similar work operation information from the records of high-quality work recorded in the past by using the values of each motion component analyzed from the previous operation of the worker.

[0021] The model operation generation unit 111 combines the spatial model representing the shape information (structures such as walls and desks) of the work space at the time of the work input by the spatial model input unit 112, the body model of the work subject input by the body model input 113, and the object model representing the shape of the object related to the work input by the shape input unit 104, based on the high-quality work operation information recorded in the past extracted by the similar work extraction unit 110, and generates display data for displaying the operation of the high-quality work closest to the work information evaluated for the motion components.

[0022] The spatial model input unit 112 receives the input of a spatial model representing the shape of the work space during work.

[0023] The body model input unit 113 receives input of a body model representing the worker or work robot that is the subject of the work.

[0024] The overall work score calculation unit 114 uses the decomposed motion components 213 and work importance information 214 to calculate the overall work score from the amount of motion of the motion components belonging to the decomposed basic shape.

[0025] The overall work score output unit 115 generates display data for displaying the calculated overall work score.

[0026] The deviation determination unit 116 uses the decomposed motion components 213 and the work importance information 214 to calculate an evaluation value for each motion component, and determines that there is a deviation in the work if the evaluation value weighted by importance is less than or equal to a predetermined value.

[0027] The deviation alert image generation unit 117 generates display data for a deviation alert image that includes the display of the parts of the work that need to be corrected, as shown on the deviation alert screen 800.

[0028] The video output unit 118 outputs the display data generated by the model motion generation unit 111, the overall work score output unit 115, and the deviation alert image generation unit 117 as video.

[0029] Figure 2 is a block diagram of the computer that constitutes the analysis apparatus 100 in this embodiment.

[0030] The analysis device 100 in this embodiment consists of a computer having a processor (CPU) 11, ROM 12, RAM 13, auxiliary storage device 14, input / output device 15, and communication device 16. The analysis device 100 is preferably implemented in a general-purpose computer or a terminal device such as a tablet.

[0031] The processor 11 is an arithmetic unit that executes programs stored in memory. The functions of each functional unit of the analysis device 100 are realized by the execution of various programs by the processor 11. Note that some of the processing performed by the processor 11 when executing programs may be performed by other arithmetic units (e.g., hardware such as ASICs or FPGAs).

[0032] The memory includes a non-volatile memory element, ROM12, and a volatile memory element, RAM13. ROM12 stores immutable programs (e.g., BIOS). RAM13 is a high-speed, volatile memory element, such as DRAM (Dynamic Random Access Memory), and temporarily stores programs executed by the processor 11 and data used during program execution.

[0033] The auxiliary storage device 14 is a high-capacity, non-volatile storage device such as a flash memory (SSD) or a magnetic storage device (HDD). The auxiliary storage device 14 also stores data used by the processor 11 when executing a program, and the program that the processor 11 executes. In other words, the program is read from the auxiliary storage device 14, loaded into the RAM 13, and executed by the processor 11 to realize the various functions of the analysis device 100.

[0034] The communication device 16 is a network interface device that controls communication with other devices according to a predetermined protocol.

[0035] The input / output device 15 includes input devices, cameras, and output devices. The input device is an interface such as a touch panel that receives input from the operator. The output device is an interface such as a display device that outputs the program execution results in a format visible to the user.

[0036] The program executed by the processor 11 is provided to the analyzer 100 via removable media (such as a CD-ROM or flash memory) or a network, and is stored in the non-volatile auxiliary storage device 14, which is a non-temporary storage medium. For this reason, the analyzer 100 should have an interface for reading data from the removable media.

[0037] The analysis device 100 may consist of a computer system comprising multiple logically or physically configured computers, rather than being a terminal device, or it may operate on a virtual computer built on multiple physical computing resources. For example, the multiple programs that implement the functions of the analysis device 100 may each operate on separate physical or logical computers, or multiple programs may be combined and operate on a single physical or logical computer. In this case, a user terminal connected to the analysis device 100 via a network may provide input and output devices.

[0038] Figure 3 is a flowchart of the analysis process performed by the analyzer 100 in this embodiment. The analysis process shown in Figure 3 is performed by the processor 11 of the analyzer 100 executing a program.

[0039] First, camera images 201 taken by a depth image camera are input to the work subject motion input unit 101 and the object position and orientation input unit 102.

[0040] The work-oriented motion input unit 101 extracts hand skeletal motion information from the input camera image 201 and outputs the extracted hand skeletal motion information to the work motion conversion unit 103 (202).

[0041] The object position and orientation input unit 102 extracts object motion information from the input camera image 201 and outputs the extracted object motion information to the work motion conversion unit 103 (203).

[0042] Furthermore, object shape information 204 is input to the shape input unit 104. The input object shape information 204 may be an object model representing the shape of the object. The object shape information 204 is output to the work motion conversion unit 103 and the basic shape extraction unit 105.

[0043] The basic shape extraction unit 105 decomposes the input object shape information 204 into basic shapes and extracts one or more basic shapes from the object model representing the shape of the object related to the work (205). For example, complex shapes can be constructed by Boolean operations on simple basic shapes, known as CSG (Constructive Solid Geometry) representation, which is widely used in three-dimensional computer graphics modeling. Boolean operations consider three-dimensional shapes with volume as a set, and create composite shapes by combining multiple shapes using set operations such as addition, subtraction, and multiplication, and create divided shapes. The input object shape information 204 is converted into a CSG representation represented by Boolean operations on multiple basic shapes using a trained AI model. Motion components are associated with the basic shapes in this embodiment. Details of the basic shape extraction process performed by the basic shape extraction unit 105 will be described later with reference to Figure 4.

[0044] Furthermore, the work motion conversion unit 103 converts the movements of the worker (body model) input to the work subject motion input unit 101 from an absolute coordinate system to relative movements in a relative coordinate system with a point on the work object as the origin, analyzes the relative movements and contact amounts with respect to the work object, and outputs interrelationship information 207 to the motion decomposition unit 106 (206).

[0045] The motion breakdown unit 106 uses the interaction information 208, which represents the relative movement between the work object and the worker, contained in the interaction information 207, to determine whether the relative movement amount is greater than or equal to a threshold (209). If the relative movement amount between the work object and the worker is greater than or equal to a predetermined threshold, it is determined that the worker is performing some kind of operation on the work object, and this relative movement information is input to the motion breakdown unit 106 (210). On the other hand, if the relative movement amount between the work object and the worker is less than a predetermined threshold, it is determined that a specific part of the worker and the work object are moving together, and this relative movement information is input to the motion breakdown unit 106 as object movement (211).

[0046] The motion decomposition unit 106 then decomposes the relative motion converted by the work motion conversion unit 103 into motions belonging to the basic shape extracted by the basic shape extraction unit 105, and outputs the values ​​213 of the decomposed motion components to the work importance analysis unit 109 (212). Furthermore, if the amount of the relative motion is smaller than a predetermined threshold, the motion decomposition unit 106 decomposes the motion of the object into motions belonging to the basic shape. For example, if the basic shape of the work object belongs to the arc direction, the motion is decomposed into motions in the arc direction and in the direction normal to the arc surface. Also, if the basic shape of the work object is a cube, the motion is decomposed into motions in the plane of the cube and in the direction normal to the plane.

[0047] The work importance analysis unit 109 evaluates the work quality of each of the motion components 213 decomposed by the motion decomposition unit 106, and, referring to the work importance information 214, evaluates the importance of each motion component from the work quality evaluation results (215). Details of the importance analysis process performed by the work importance analysis unit 109 will be described later with reference to Figure 6.

[0048] Then, the similar task extraction unit 110 extracts similar tasks (216).

[0049] The overall work score calculation unit 114 uses the decomposed motion components 213 and work importance information 214 to calculate the overall work score from the amount of motion of the motion components belonging to the decomposed basic shape (217).

[0050] The deviation determination unit 116 uses the decomposed motion components 213 and the work importance information 214 to calculate an evaluation value for each motion component (for example, the work success probability in Figure 7), and determines that the work has deviated if the importance is greater than or equal to a predetermined value and the evaluation value weighted by importance is less than or equal to a predetermined value (218).

[0051] Subsequently, the model motion generation unit 111 generates display data to display the high-quality work that is closest to the work information evaluated for its motion components as the motion of the body model. The overall work score output unit 115 generates display data to display the calculated overall work score. The deviation alert image generation unit 117 generates display data to display the deviation judgment result that will be played back on the deviation alert screen 800 (219).

[0052] Figure 4 shows the basic shape extraction process performed by the basic shape extraction unit 105.

[0053] The basic shape extraction unit 105 extracts the basic shape from the object shape information 204. For example, in the example shown in Figure 4, the workpiece 1 is a rectangular parallelepiped 2 with a cylindrical hole A (3A) and a cylindrical hole B (3B). In other words, the shape of the workpiece 1 is a combination of a rectangular parallelepiped 2 and cylinders 3A and 3B of equal height.

[0054] The basic shape extraction unit 105 extracts one rectangular prism 1 and two cylinders 3A and 3B from the object shape information 204 of the work object 1 using a trained AI model that converts complex shapes into CGS representations constructed by Boolean operations.

[0055] The basic shape extraction unit 105 then refers to the motion components belonging to the basic shapes and identifies the motion components belonging to the rectangular parallelepiped 2 and the cylinders 3A and 3B. The motion components of the basic shapes are a classification of the movements of the worker during work performed on the work object of the basic shape. In the example shown in Figure 4, the motion components of the rectangular parallelepiped 2 are the three orthogonal x, y, and z axes parallel to the sides of the rectangular parallelepiped 2 (perpendicular to the faces of the rectangular parallelepiped 2), and the motion components of the cylinders are the three directions: the θ direction of the arc along the side of the cylinder, the r direction of the diameter of the circular base, and the z direction perpendicular to the circular base.

[0056] For example, in the task of wiping a disc-shaped workpiece, uniform wiping can be achieved by having the main part of the workpiece move at a predetermined speed in the θ direction of the arc. Therefore, the average value of the movement speed in the θ direction is calculated. For example, in a wiping operation that includes forward and reverse movements, the average value of the movement speed in the θ direction will be 0 or close to 0, and it can be determined that the operation is not appropriate.

[0057] Figure 5 shows an example of output from the motion component output unit 107. While Figure 5 shows an example of tabular data output, data may be output in other formats.

[0058] The motion decomposition unit 106 decomposes the relative motion between the work subject and the work object into motion components belonging to the basic shape, and the motion component output unit 107 outputs values ​​indicating the motion in the decomposed motion components.

[0059] In operation 1, which involves assembling a part onto the workpiece 1 shown in Figure 4, the movement in the y-direction belonging to the rectangular parallelepiped 2 determines the position of the part to be assembled, and the movement in the z-direction places the part onto the workpiece. In operation 1, the motion components belonging to cylinders 3A and 3B are not extracted.

[0060] Next, in operation 2, where a screw is tightened in hole A, the movement of the screw in the r-direction belonging to cylinder 3A moves the screw to the position of hole A, the movement in the θ-direction rotates the screw in hole A, and the movement in the z-direction inserts the screw into hole A. In operation 2, the motion components belonging to the rectangular parallelepiped 2 and cylinder 3B are not extracted.

[0061] Next, in step 3, where a screw is tightened in hole B, the movement of the screw in the r-direction belonging to cylinder 3B moves the screw to the position of hole B, the movement in the θ-direction rotates the screw in hole B, and the movement in the z-direction inserts the screw into hole B. In step 3, the motion components belonging to the rectangular parallelepiped 2 and cylinder 3A are not extracted.

[0062] Figures 6 and 7 show the importance analysis process performed by the work importance analysis unit 109.

[0063] The work importance analysis unit 109 evaluates the work quality in each of the motion components 213 decomposed by the motion decomposition unit 106, and, referring to the work importance information 214, evaluates the importance of each motion component based on the work quality evaluation results.

[0064] For example, as shown in Figure 6, work quality evaluation data is created by dividing the values ​​of each motion component in previously recorded work into a work success group and a work failure group, and the importance of the work is determined by the significance probability of the difference between the average value of the motion component of the work success group and the average value of the motion component of the work failure group. The significance probability is the probability that the result can be judged as having been obtained assuming that there is actually no difference in the average values ​​between the work success group and the work failure group. The smaller the significance probability, the more likely it is that there is a difference between the work success group and the work failure group in the motion component of interest. For example, the importance of the work can be determined by classifying the significance probabilities at a predetermined threshold. For example, in wiping work, the work success group and the work failure group can be classified by comparing the number of foreign objects remaining on the surface after wiping with a predetermined threshold. Also, if the information representing the success / failure of the work can be quantitatively evaluated, the correlation coefficient with each motion component is calculated, and the motion component with a large absolute value of the correlation coefficient is judged to be an important component.

[0065] Then, as shown in Figure 7, the overall work score calculation unit 114 calculates the overall work score using the significance probabilities calculated for each motor component by the work importance analysis unit 109. For example, the overall work score is calculated as the average value of the work success probabilities weighted by importance, which is the reciprocal of the significance probability.

[0066] Figure 8 shows an example of a deviation alert screen 800 output by the video output unit 118.

[0067] The deviation alert screen 800 includes a work display area 810, a message display area 820, and an overall work score display area 830.

[0068] In the work display area 810, the movement of the body model 5, which reflects the movements in the work process and is generated by the deviation alert image generation unit 117, is displayed together with the object model of the work object 1. The cylinder 3A, which is the basic shape extracted by the basic shape extraction unit 105 associated with the motion component that detected the deviation and is the part of the work that needs to be corrected, is highlighted and displayed. The message display area 820 displays the message of the deviation judgment result determined by the deviation judgment unit 116. By presenting the deviation judgment result to the worker, an opportunity for correction can be provided. The deviation alert screen 800 can be used in two ways: displaying the result based on the deviation judgment result made on the spot based on real-time hand skeletal movement information obtained during work, or displaying it at any time using hand skeletal movement information recorded after the completion of work for the purpose of work analysis.

[0069] The overall work score display area 830 displays the overall work score calculated by the overall work score calculation unit 114. By presenting the overall work score to the worker, the work level can be improved.

[0070] According to one aspect of the present invention, since motion components, which are work evaluation features, are automatically generated based on work motion and shape data of the work object, work analysis can be applied to a wide range of tasks in a short period of time, and tasks can be analyzed individually and evaluated with high accuracy. Furthermore, since individual tasks are evaluated in relation to the shape of the objects related to the task, intuitive work support can be provided. In other words, the following effects can be obtained in one embodiment of the present invention.

[0071] Quantitative evaluation of work quality: The worker's actions are broken down into components belonging to the basic shape of the object being worked on (such as rotational components in the arc direction around holes, and components parallel or perpendicular to the plane). The values ​​of each motion component in the work being evaluated are then assessed using statistical values ​​from previously recorded work, and an overall work score is calculated. This allows for feedback to be given to the worker regarding the overall quality of the work.

[0072] Work deviation warning: Compare the movement of the work component being evaluated with the movement of work components recorded in the past. This allows the worker to be shown the possibility of a work deviation.

[0073] Information presentation for work correction: For actions that may deviate from the work process, the basic shape of the work object related to the potentially deviating motion component is visually presented along with a reproduction of the worker's action. This provides the worker with an opportunity to correct their work.

[0074] Automation of Work: While the above describes the work performed by human workers, the present invention can also be applied to work performed by robots. When automating work with a robot, it is advisable to search for the optimal movements so that the deviation of each movement component in past work records falls within a predetermined value. This enables high work quality to be achieved with the robot.

[0075] Work support: By using the values ​​of each motion component analyzed from the worker's immediate preceding movement, similar work motion information is extracted from previously recorded high-quality work records. This allows for the visual presentation of work examples that are easy for workers to emulate.

[0076] It should be noted that the present invention is not limited to the embodiments described above, but includes various modifications and equivalent configurations within the spirit of the attached claims. For example, the embodiments described above are described in detail to make the present invention easier to understand, and the present invention is not necessarily limited to having all of the described configurations. Furthermore, some of the configurations of one embodiment may be replaced with those of another embodiment. Furthermore, configurations of other embodiments may be added to the configuration of one embodiment. Furthermore, some of the configurations of each embodiment may be added, deleted, or replaced with those of other embodiments.

[0077] Furthermore, each of the aforementioned configurations, functions, processing units, and processing means may be implemented in hardware, for example, by designing them as integrated circuits, or they may be implemented in software by having a processor interpret and execute programs that realize each function.

[0078] Information such as programs, tables, and files that implement each function can be stored in memory, hard disks, SSDs (Solid State Drives), or other storage media such as IC cards, SD cards, and DVDs.

[0079] Furthermore, the control lines and information lines shown are those deemed necessary for explanation purposes and do not necessarily represent all control lines and information lines required for implementation. In reality, it can be assumed that almost all components are interconnected. [Explanation of Symbols]

[0080] 1. Object to be worked on 2. Rectangular prism 3A, 3B cylinder 5 Body Models 11 processors 12 ROM 13 RAM 14 Auxiliary storage 15 Input / Output Devices 16. Communication equipment 100 Analyzer 101 Operation Main Action Input Unit 102 Object position and orientation input unit 103 Operation motion conversion unit 104 Shape Input Section 105 Basic shape extraction part 106 Motion analysis section 107 Motor component output section 108 Work Information Input Section 109 Work importance analysis department 110 Similar work extraction part 111 Model motion generation unit 112 Spatial Model Input Section 113 Body Model Input Section 114 Integrated Work Score Calculation Unit 115. Integrated work score output unit 116 Deviation detection unit 117 Deviation Alert Image Generation Unit 118 Video Output Section 201 Camera Images 204 Object shape information 207 Interrelationship Information 208 Interaction information 213 Motion component 214 Work importance information 800 Deviation Alert Screen 810 Work display area 820 Message display area 830 Overall work score display area

Claims

1. An analytical device for analyzing work movements, It is composed of a computer having an arithmetic unit that performs predetermined arithmetic processing and a storage device connected to the arithmetic unit, A shape input unit that receives shape information of the object to be worked on, An object position and orientation input unit that receives position and orientation information of the work object, An action input unit that receives data on the actions of the work performed by the person performing the work, A basic shape extraction unit that extracts the basic shapes that constitute the shape of the object to be worked on, A work motion conversion unit that converts the actions of the work subject into relative actions from the coordinate system of the work object, An analytical apparatus characterized by comprising: a motion decomposition unit that decomposes the aforementioned relative motion into motions belonging to the aforementioned basic shape and outputs the momentum of each decomposed motion component.

2. The analytical apparatus according to claim 1, The basic shape extraction unit is an analytical apparatus characterized by approximating the work object with a plurality of basic shapes and extracting the basic shape.

3. The analytical apparatus according to claim 1, An analytical apparatus characterized by comprising an importance analysis unit that evaluates the importance of the motion components using work importance information created from work quality evaluation data that evaluates the work quality in each work movement.

4. The analytical apparatus according to claim 3, An analysis device characterized by comprising a score calculation unit that calculates an overall work score for evaluating work movements using the aforementioned work importance information and the aforementioned movement components.

5. The analytical apparatus according to claim 3, An analytical apparatus characterized by comprising a deviation determination unit that determines deviations from work using the aforementioned work importance information and the aforementioned motion components.

6. The analytical apparatus according to claim 5, The deviation determination unit calculates an evaluation value for each motion component using the work importance information and the motion components, and determines that the work is a deviation if the importance is above a predetermined value and the evaluation value is below a predetermined value.

7. The analytical apparatus according to claim 5, An analysis device characterized by comprising a deviation alert image generation unit that generates display data for a deviation alert image, including an indication of the part of the work that should be corrected, based on the deviation determination result.

8. The analytical apparatus according to claim 1, The motion decomposition unit is characterized in that, when the amount of the relative motion is less than a predetermined threshold, it decomposes the motion of the work object into motions belonging to the basic shape.

9. The analytical apparatus according to claim 4, The aforementioned task is performed by a robot. The analysis device is characterized in that the score calculation unit calculates an overall work score for evaluating the actions of a robot in manipulating an object, using the work importance information and the motion components.

10. The analytical apparatus according to claim 3, The aforementioned work quality evaluation data is data in which multiple operational data are classified into a work success group and a work failure group, The analytical apparatus is characterized in that the aforementioned work importance information is calculated based on the statistically significant difference between the group that succeeded in the work and the group that failed in the work for each exercise component.

11. The analytical apparatus according to claim 1, The subject of the aforementioned work is a person. An analysis device characterized by having a video output unit that displays the results of the analysis of the operations of the aforementioned work.

12. An analytical method in which an analytical device analyzes work movements, The analysis apparatus is comprised of a computer having a arithmetic unit that performs predetermined calculation processing and a storage device connected to the arithmetic unit. The analysis method described above involves the computing device, A shape input procedure for receiving shape information of the work object, A procedure for inputting object position and orientation information of the work object, A motion input procedure for receiving data on the actions of the work performed by the work subject, A basic shape extraction procedure for extracting the basic shapes that constitute the shape of the object to be worked on, A work motion conversion procedure that converts the actions of the work subject into relative actions from the coordinate system of the work object, An analysis method characterized by comprising: a motion decomposition procedure for decomposing the aforementioned relative motion into motions belonging to the aforementioned basic shape, and outputting the momentum of each decomposed motion component.

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