JP7763707B2 - Work detection system, device and method - Google Patents

Description

The present invention generally relates to task detection, for example, to assisting in skill acquisition.

There is a shortage of workers trained in ultra-high voltage CV (Cross-linked polyethylene insulated vinyl sheath) cable jointing (hereinafter referred to as cable jointing). One reason for this is the increase in plans for underground power transmission construction, etc., and the concentration of replacement periods for ultra-high voltage CV cables nearing the end of their lifespan.

Cable joint work includes the scraping of the outer semiconductive layer (hereinafter simply referred to as "scraping work"). The scraping work involves scraping off a certain length of the outer semiconductive layer around the entire circumference of the cable tip to expose an insulating layer of specified dimensions. Once the cable has been scraped to the specified dimensions and installation conditions, it has a near-perfect circular shape and a smooth surface without any horizontal scratches, which also contributes to improving the durability of the equipment. The scraping work involves repetitive actions, such as multiple scraping strokes using a cutting tool. The scraping is done in one direction, with widths measured in millimeters.

Acquiring cable jointing skills, especially the scraping skills, requires considerable experience. For example, it can take a young person several years to master this skill.

For instructors (e.g., experienced workers), it is not easy to allocate time to skills training due to an increase in construction work (or other reasons). For trainees (e.g., young people), it is difficult to acquire skills through verbal instruction alone from an instructor.

Therefore, there is a need for technology to support skill acquisition. Examples of this type of technology are disclosed in Patent Documents 1 and 2.

According to Patent Document 1, the motion data of a first worker is compared with the motion data of a second worker, and the similarity of the motion data is determined. Depending on the results of this determination, data indicating areas for improvement is presented to the first worker.

According to Patent Document 2, repetitive movements are extracted from sensor data and compared with other repetitive movements. Based on the results of this comparison, it is determined whether the extracted repetitive movement is an outlier.

Japanese Patent Application Publication No. 2019-20913 US2021/0177307

According to Patent Documents 1 and 2, data from sensors is operational data.

As a possible method for evaluating the movements of a trainee in a scraping task, an expert and trainee wearing or using the sensors disclosed in Patent Documents 1 or 2 perform the scraping task, and the data obtained from the expert's sensor is compared with the data obtained from the trainee's sensor.

The sensors disclosed in Patent Document 1 are wearable sensors worn on the arm or waist, and a different type of pressure sensor, and the data is data from these sensors. The sensor disclosed in Patent Document 2 is a wearable glove motion capture device, and the data is motion capture data captured by the device.

However, even if such data is used as motion data for scraping work, it is difficult to help trainees acquire scraping work skills. This is because scraping work requires moving the hands while the fingers are pressed against an ultra-high voltage CV cable, which involves scraping the workpiece to a width of millimeters or less. Such movements are difficult to adequately capture in the data described above. Furthermore, when performing work using such tools, if only the worker's movements are acquired and analyzed, the relationship between the tools and the work data alone becomes unclear, making it difficult for trainees to acquire work skills using motion data alone. Furthermore, while it is possible to evaluate the worker's work and the tools and the work together using a camera alone, it may not be possible to evaluate the work primarily using the camera, for example, if the work area or tools change position for each task, if the camera cannot be placed around the work area, or if there are obstructions between the camera and the work area.

Similar challenges may also arise when assisting with skill acquisition for other types of tasks that require specific movements, including moving the hand while the fingers are pressed against an object.

A sensor set including a pressure sensor that measures the pressure applied by the fingers and an acceleration sensor that measures the acceleration of the hand is provided for a first worker performing a task that requires a specific movement, including movement of the hand while the fingers are pressed against an object. A computer detects the movement based on sensing data including the pressure and acceleration measured by the sensor set. To evaluate the first worker, the computer compares first movement information, which is movement information obtained from the sensing data of the first worker, with second movement information regarding the detected movement.

This invention can assist in the acquisition of skills for tasks that require specific movements, including moving the hand while the fingers are pressed against an object.

An example of the site is shown before scraping work began. An example of a site where scraping work is being carried out is shown. An example of the site after scraping work was completed is shown below. FIG. 10 is an explanatory diagram of step 1 cutting. FIG. 10 is an explanatory diagram of step 2 cutting. FIG. 10 is an explanatory diagram of step 3 cutting. FIG. 2 is a rear view of the sensor glove. FIG. 2 is a front view of the sensor glove. 1 shows the overall configuration of an operation detection system including a mobile PC. 1 shows the configuration of detection target data. An example of expected acceleration time series data for an expert is shown below. An example of acceleration time series data expected for beginners is shown below. An example of the evaluation method is shown below. 10 shows an example of an evaluation UI.

In the following description, an "interface apparatus" may refer to one or more interface devices, which may be at least one of the following:
An I/O interface device is one or more I/O (Input/Output) interface devices. The I/O (Input/Output) interface devices are interface devices for at least one of an I/O device and a remote display computer. The I/O interface device for the display computer may be a communications interface device. The at least one I/O device may be a user interface device, for example, either an input device such as a keyboard and a pointing device, or an output device such as a display device.
A communication interface apparatus that is one or more communication interface devices. The one or more communication interface devices may be one or more homogeneous communication interface devices (e.g., one or more NICs (Network Interface Cards)) or two or more heterogeneous communication interface devices (e.g., a NIC and an HBA (Host Bus Adapter)).

In the following description, "memory" refers to one or more memory devices, which are an example of one or more storage devices, and may typically be a primary storage device. At least one memory device in the memory may be a volatile memory device or a non-volatile memory device.

In the following description, a "persistent storage device" may refer to one or more persistent storage devices, which are an example of one or more storage devices. A persistent storage device may typically be a non-volatile storage device (e.g., an auxiliary storage device), and more specifically, may be, for example, a hard disk drive (HDD), a solid state drive (SSD), a non-volatile memory express (NVME) drive, or a storage class memory (SCM).

Also, in the following description, "storage device" may refer to at least memory, including memory and persistent storage devices.

In the following description, a "processor" may refer to one or more processor devices. The at least one processor device may typically be a microprocessor device such as a CPU (Central Processing Unit), but may also be another type of processor device such as a GPU (Graphics Processing Unit). The at least one processor device may be single-core or multi-core. The at least one processor device may also be a processor core. The at least one processor device may also be a broader processor device such as a circuit that is a collection of gate arrays written in a hardware description language that performs some or all of the processing (e.g., an FPGA (Field-Programmable Gate Array), a CPLD (Complex Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit)).

In the following explanation, information that produces an output in response to an input may be described using expressions such as "xxx database," but this information may be data of any structure (for example, structured or unstructured data), or it may be a neural network that generates an output in response to an input, or a learning model such as a genetic algorithm or random forest. Therefore, "xxx database" can also be referred to as "xxx information." In the following explanation, the structure of each database is an example, and one database may be divided into two or more databases, or all or part of two or more databases may be one database.

In the following description, functions may be described using the expression "yyy unit," but the functions may be realized by one or more computer programs being executed by a processor, by one or more hardware circuits (e.g., FPGA or ASIC), or by a combination of these. When a function is realized by a program being executed by a processor, the specified processing is performed using a storage device and/or interface device, etc., as appropriate, and therefore the function may be considered to be at least part of the processor. Processing described using a function as the subject may be processing performed by a processor or a device having that processor. A program may be installed from program source. The program source may be, for example, a program distribution computer or a computer-readable storage medium (e.g., a non-transitory storage medium). The description of each function is an example, and multiple functions may be combined into a single function, or a single function may be divided into multiple functions.

In the following description, common reference symbols will be used when describing elements of the same type without distinguishing between them, and reference symbols will be used when distinguishing between elements of the same type.

One embodiment is described below.

As shown in Figures 1A to 1C, in this embodiment, the "task requiring a predetermined action including moving a hand while applying pressure to an object with a finger" is the task of scraping off an outer semiconductive layer from CV cable 115 to expose an insulating layer of a specified size from CV cable 115, and the "object" is CV cable 115. Note that Figure 1A shows an example of the site before the scraping work begins. Figure 1B shows an example of the site during the scraping work. Figure 1C shows an example of the site after the scraping work has been completed.

Here, we will explain the relationship between workers and specified tasks. Depending on the specified task, there are parameters that evaluate the results of the work, such as good quality of the workpiece, short processing time, and low wear on tools used in processing. Workers who have good results in these parameters are said to be skilled or experienced in the specified task, and are accustomed to the specified task, and are called experts or instructors. Meanwhile, those who are less skilled or have less experience in the specified task than experts or instructors are called trainees or trainees. Trainees and trainees are simply called trainees and trainees.

In this embodiment, an example of a first worker is the trainee 110. An example of a second worker is an expert. One or more experts may be employed for the entire scraping work of the outer semiconductive layer. One or more experts may be employed for each of the tasks that make up the scraping work (specifically, step 1 scraping, step 2 scraping, and step 3 scraping, which will be described later).

The process of scraping off the outer semiconductive layer requires delicate movements, with widths measured in millimeters. To ensure that such movements are properly reflected in the data, this embodiment employs a sensor glove 102 equipped with a sensor set. A mobile PC (Personal Computer) 150 is also provided, which receives data including values measured by the sensor glove 102 and detects and evaluates the movements.

The sensor set is a set of multiple sensors, including a pressure sensor that measures the pressure applied by the fingers and an acceleration sensor that measures the acceleration of the hand. As shown in FIG. 1B, when the trainee 110 performs a scraping task while wearing such a sensor glove 102 on his or her hand, data including values measured by the sensor glove 102 (pressure, angular velocity, acceleration, etc.) is obtained. The mobile PC 150 then detects and evaluates the movement based on the sensing data including such data. The sensor glove 102 may be worn on only one hand (e.g., the dominant hand) or on both hands. Alternatively, instead of the sensor glove 102, each sensor in the sensor set may be worn directly on the hand (e.g., the fingers or the back of the hand) as an example of a wearable sensor.

The mobile PC 150 is an example of an information processing device. The mobile PC 150 has a motion detection unit and a motion comparison unit. A general-purpose computer can be used for the mobile PC 150. Furthermore, if the information acquired by the sensor glove 102 requires a large amount of computation, a computer using a GPU (Graphics Processing Unit) or FPGA, etc., may be used. The data acquired by the sensor glove 102 may be transmitted to a cloud service for data analysis, etc. The motion detection unit detects motion based on sensing data including values measured by the sensor glove 102. To evaluate the trainee 110, the motion comparison unit compares first motion information (motion information obtained from the trainee 110's sensing data) with second motion information regarding the detected motion and performs evaluation based on the comparison results. "Movement information" will be described later. In the mobile PC 150, the motion detection unit detects motion in response to data from the sensor glove 102 (e.g., in real time), and depending on the detected motion, the motion comparison unit determines whether or not there is an abnormality in the motion. If an abnormality is detected, an alert may be output. In the example shown in FIG. 1B, the alert is output as a display and sound by the mobile PC 150, and as an LED light on the sensor glove 102. In particular, the sound alert output allows the worker to know whether their work is going well or poorly while continuing their work. Furthermore, outputting different sounds depending on whether the worker's work is going well or poorly is effective because it allows the worker to adjust their work to improve their current work.

In this embodiment, in addition to the data obtained by the sensor glove 102, data on the shooting operation by the camera 101 is input to the mobile PC 150. The shooting may be fixed or mobile. If fixed shooting is used, the camera 101 may be attached to a tower frame as shown in FIG. 1A. If mobile shooting is used, the camera 101 may be attached to the trainee 110 (for example, a helmet worn by the trainee 110). Alternatively, a camera with an adjustable angle and magnification to change the angle of view, or a camera mounted on a rail or equipped with a self-propelled means, may be used. The angle of view of the captured video may be at least a portion of the trainee's 110 field of view, typically a range that includes all or part of the trainee's 110's hand (for example, the fingers). Furthermore, although video is used in this embodiment, still images may be used instead of or in addition to the video.

The mobile PC 150 displays output information, which includes information representing the trainee's 110's evaluation. The output information may include at least one captured image. As shown in FIG. 1C, after the scraping work is completed (or after each of the multiple tasks that make up the scraping work is completed), the instructor 111 instructs the trainee 110 while viewing the output information displayed on the mobile PC 150 together with the trainee 110. The output information may be displayed on a remote instructor's PC (instructor's PC) via a communications network (details will be described later).

The scraping operation consists of multiple operations: step 1 scraping, step 2 scraping, and step 3 scraping. The number of steps in the scraping operation is described as a representative example, and the number of steps may be appropriate for the tool and the object being scraped.

Step 1 (cutting) is the process of roughly scraping away the outer semiconductive layer 202 from the entire circumference of the tip of the CV cable, as shown in Figure 2A. This process involves repeatedly pressing a cutting tool 201 against the outer semiconductive layer 202 and moving the cutting tool 201 repeatedly a certain distance in one direction 203 indicated by an arrow. Each stroke of the cutting tool 201 scrapes away a few millimeters of the outer semiconductive layer 202. This process exposes the insulating layer 204 over the entire tip of the CV cable, as shown in Figure 2B. It is desirable that the hardness of the cutting tool 201 be higher than the Vickers hardness of the material of the outer semiconductive layer 202.

As shown in Figure 2B, step 2 scraping is the process of scraping off the base end of the distal end of the CV cable to a specified dimension (e.g., several millimeters in the longitudinal direction) of the outer semiconductive layer 202. Step 2 scraping is a process of scraping off using finer strokes than step 1 scraping. In this process, a cutting tool 201 is pressed against the base end of the distal end of the CV cable while being repeatedly moved back and forth along the reciprocating direction 205 indicated by the arrow. With each stroke of the cutting tool 201 from the distal end to the proximal end, a width of several millimeters is scraped off of the outer semiconductive layer 202.

As shown in Figure 2C, step 3 cutting is the step 3 cutting of the surface of the insulating layer 204. In this operation, a repetitive action is performed in which the cutting tool 201 is pressed against the insulating layer 204 and repeatedly moved a certain distance in one direction 206 as indicated by the arrow. Each stroke of the cutting tool 201 removes a width of several millimeters from the insulating layer 204.

The sensor glove 102 described above is used to accurately detect the movements during the scraping work, which consists of step 1 scraping, step 2 scraping, and step 3 scraping. Because it is difficult to capture the angle, movement distance, and pressure of the worker's hand movements and the cutting tool 201 movements using a camera alone, using the sensor glove 102 is effective. Furthermore, the smoothness of the surface of the outer semiconductive layer 202 is affected by the quality of the stroke in which the worker presses the cutting tool 201 from the tip end to the base end to scrape away. Therefore, obtaining the pressure applied by the worker's hand to the cutting tool 201 and the inertia of the hand movement are important evaluation parameters when evaluating the worker's work. A detailed evaluation method will be described later.

As shown in Figures 3A and 3B, the sensor glove 102 includes a pressure sensor 302, a microphone 303, and a module device 301.

Pressure sensors 302 are provided on the fingertips of the sensor glove 102 and measure the pressure applied by the fingers. For example, pressure sensors 302A to 302C are provided on the fingertips of the thumb, index finger, and middle finger of the sensor glove 102.

Microphones 303 are provided at predetermined locations. For example, microphone 303A is provided near the tip of the thumb, and microphone 303B is provided near the wrist. Microphones 303 collect sounds from around the hand. For example, if an unusual sound is emitted from cutting tool 201 during scraping work, an alert can be sent to an experienced worker, or a separate flag can be set in the data as a checkpoint for the worker's work, making it possible to later confirm that there was an abnormality in the work operation.

The module device 301 is provided on the back of the sensor glove 102. The module device 301 includes an inertial sensor and a communication device. The inertial sensor includes at least an acceleration sensor. In this embodiment, in addition to the acceleration sensor, the inertial sensors include a gyro sensor and a geomagnetic sensor. The acceleration sensor measures acceleration related to hand movement. The gyro sensor detects angular velocity related to hand movement. The gyro sensor detects the orientation of the hand (direction of hand movement). The communication device transmits data including data (values) measured by the pressure sensor 302, microphone 303, and each inertial sensor to an external device (in this embodiment, the mobile PC 150). Note that in this embodiment, the orientation of the inertial sensor includes a first direction from the fingertip to the wrist, a second direction from the little finger to the thumb that is perpendicular to the first direction, and a third direction from the palm to the back of the hand that is perpendicular to the first and second directions.

The mobile PC 150 detects and evaluates movement based on sensing data including data received from the communication device of the sensor glove 102. In this embodiment, the sensor glove 102 and mobile PC 150 communicate wirelessly, but wired communication may be used instead. Wireless communication may be any method, such as radio waves, infrared rays, visible light, or sound waves, and the most suitable method can be selected depending on the work site.

Figure 4 shows the overall configuration of the work detection system including the mobile PC 150.

The mobile PC 150 has a communication interface device (communication I/F) 401, an input/output interface device (I/O-I/F) 402, an input device 403, a display device 404, a storage device 405, and a processor 406. The interface devices (communication I/F 401 and I/O-I/F 402) and the storage device 405 are connected to the processor 406.

The communication I/F 401 communicates with devices external to the mobile PC 150, such as the camera 101, the sensor glove 102, and the external system 450. In the communication I/F 401, the communication interface device for communication with the camera 101, the communication interface device for communication with the sensor glove 102, and the communication interface device for communication with the external system 450 may be different, or the communication interface device may be common to two or more of the camera 101, the sensor glove 102, and the external system 450. The external system 450 may be a computer system external to the mobile PC 150, such as a remote server (e.g., a server as a cloud computing service) or the instructor PC described above.

The I/O-I/F 402 mediates input and output between the processor 406 and the input device 403 and display device 404.

The input device 403 is a user interface device such as a keyboard or pointing device. The display device 404 is an example of an output device, and is a user interface device such as a liquid crystal display. The input device 403 and display device 404 may be integrated, such as a touch panel.

The storage device 405 stores information input and output by the processor 406, and computer programs executed by the processor 406. Examples of information include accumulated data 410, detection target data 411, evaluation method data 412, evaluation data 413, proficiency management data 414, trainee management data 415, behavior evaluation data 416, quality evaluation data 417, and integrated data 418.

By executing the computer program, the processor 406 realizes functions such as a data receiving unit 420, a motion detection unit 421, a motion comparison unit 422, a UI (User Interface) control unit 423, and an integration unit 424.

Data 410-418 and functions 420-424 are explained below.

The data receiving unit 420 receives captured video data from the camera 101 and stores the received captured video data in the storage device 405. The data receiving unit 420 also receives data from the sensor glove 102 and stores the received data in the storage device 405. The stored captured video data from the camera 101 and data including the stored data from the sensor glove 102 constitute the accumulated data 410. In other words, the accumulated data 410 includes the captured video data from the camera 101 and sensing data including data from the sensor glove 102. The sensing data includes data on one or more values (specifically, a time series of measured values) measured by each of the pressure sensor 302, microphone 303, acceleration sensor, gyro sensor, and geomagnetic sensor.

The motion detection unit 421 detects the motion of the trainee 110 based on the accumulated data 410. Motion detection uses at least sensing data. The motion to be detected is specified in the detection target data 411. As shown in FIG. 5, the detection target data 411 represents, for each task that constitutes the scraping operation, the motion to be detected and a flag indicating whether the motion is subject to real-time detection. Some motions are subject to detection for two or more of the tasks that constitute the scraping operation. For example, the motion "(8) Change in the contact angle of the cutting tool" is subject to detection for all of step 1 scraping, step 2 scraping, and step 3 scraping. Furthermore, among the motions shown in FIG. 5, examples of repetitive motions involving repeated hand movement are "(1) Constant speed change in scraping stroke," "(4) Regular motion," and "(7) Application of constant pressure." Furthermore, motions for which the real-time detection flag is on in the detection target data 411 are motions for which the presence or absence of an abnormality is determined in response to data received from the sensor glove 102. The operation comparison unit 422 determines whether the detected operation is abnormal. For example, the operation comparison unit 422 compares one or more predetermined measurement values in the received data with threshold values for the various measurement values. If at least one measurement value is abnormal based on its relationship with the threshold value, the operation comparison unit 422 may output an alert via the UI control unit 423. For example, the UI control unit 423 may display the alert on the display device 404, output the alert as audio from a speaker (an example of an output device) (not shown), or output the alert to the external system 450. As a modified example of real-time detection, simple data processing may be performed within the processor of the sensor glove 102 to detect abnormalities or signs of abnormalities in the operation of the worker or the tool, etc., and then output a sound from a microphone separately provided in the sensor glove 102. In this case, the sensor glove 102 can be operated standalone without using a PC for analysis and evaluation.

The motion comparison unit 422 compares the first motion information with the second motion information regarding the detected motion, performs an evaluation based on the comparison results, and stores evaluation data 413 representing the evaluation in the storage device 405. In this embodiment, evaluation includes scoring.

Here, "motion information" refers to information obtained from sensing data. The motion information may depend on the type of motion detected, and may be, for example, a waveform as a time series of measurement values, a portion of that waveform, or information including various values obtained from the time series of measurement values (e.g., the number of strokes).

The "first movement information" is information obtained from the sensing data of the trainee 110. The "second movement information" compared with the first movement information is information obtained from the sensing data of an expert (e.g., information prepared based on information obtained from the sensing data of each of multiple experts). For example, the second movement information may be prepared based on sensing data including data obtained from the sensor glove 102 of an expert performing a scraping task. The second movement information may be information including a waveform (measurement value time series) to be compared, a portion of a waveform, or thresholds for various values (e.g., the number of sensing operations). The second movement information may be identified, for example, from the evaluation method data 412. For example, for each task constituting a scraping task, the evaluation method data 412 includes second movement information and evaluation method information for each detection target movement belonging to that task. The evaluation method information indicates the evaluation method.

The motion detection method, first motion information, second motion information, and evaluation method information depend on the motion to be detected (and the task to which the motion belongs). As described above, some motions (e.g., motion "(8) Change in cutting tool contact angle") are detection targets for two or more of the tasks that make up the scraping operation, but even for the same motion, at least some of the motion detection method, first motion information, second motion information, and evaluation method information may differ depending on which task it belongs to. Furthermore, if different cutting tools are used by different workers or if the tip of the cutting tool has different characteristics (sharpness, taper, etc.) for each cutting tool, the type of cutting tool, the tip condition of the cutting tool, and the angle of the cutting tool may be detected separately.

Below, we will explain examples of the first operation information, second operation information, and evaluation method using the operation "(1) Constant speed change of the cutting stroke" as an example.

When detecting the motion of step 1 scraping in the scraping work, the motion detection unit 421 identifies the motion of the detection target corresponding to step 1 scraping from the detection target data 411. The motion detection unit 421 detects the motion "(1) a constant speed change in the scraping stroke" from the acceleration time series data in the sensing data, among the identified motions of the detection target. Note that which part of the scraping work is being performed may be specified from the input device 403, may be specified from the sensor glove 102, or may be detected based on the sensing data. Furthermore, as described above, the motion of the detection target may be narrowed down depending on which part of the scraping work is being performed.

The action comparison unit 422 divides the trainee's 110 acceleration time-series data (waveform data) into multiple partial waveforms based on the peak values of the acceleration. Each partial waveform corresponds to one stroke in step 1 cutting. The partial waveform has one peak value and two minimum values before and after the peak (i.e., two minimum values). For each partial waveform, the action comparison unit 422 compares the partial waveform with a reference waveform (triangle) and determines whether the difference between the partial waveform and the reference waveform is equal to or less than the difference threshold. The first action information may be information including multiple partial waveforms, and the second action information may be information including the reference waveform and the difference threshold. The action comparison unit 422 may assign a score for the action "(1) Constant speed change in cutting strokes" based on the total number of partial waveforms and the comparison results. The "comparison result" here may be the percentage or number of passing waveforms (partial waveforms whose difference is equal to or less than the difference threshold).

If the operator is an expert, it is expected that the ratio of passing waveforms to the total number of partial waveforms for operation "(1) Constant speed change of cutting stroke" will be high, as shown in Figure 6A. On the other hand, if the operator is a beginner, it is expected that the ratio of passing waveforms to the total number of partial waveforms will be low, as shown in Figure 6B. In Figures 6A and 6B, the symbol 600 represents a time period during which the ratio of passing waveforms to the total number of partial waveforms is below a certain percentage.

The evaluation method information corresponding to the action "(1) Constant speed change of the scraping stroke" includes the graph shown in Figure 7. This graph shows the relationship between the proportion of passing waveforms and the score. The action comparison unit 422 identifies the score corresponding to the proportion of passing waveforms of the trainee 110 from the graph, and assigns the identified score as the score for the action "(1) Constant speed change of the scraping stroke."

The above are examples of the first motion information, second motion information, and evaluation method for the motion "(1) Constant speed change of the scraping stroke." Depending on the motion to be detected, data including values measured by a sensor other than an acceleration sensor may be used. For example, the motion "(5) Scrapings removal motion" in step 2 scraping may be detected based on time-series data from a geomagnetic sensor.

For each scraping task, the movement detection unit 421 detects all detection target movements associated with that task based on at least the sensing data in the accumulated data 410. For each detected movement, the movement comparison unit 422 assigns a score to that movement based on the first movement information, second movement information, and evaluation method information obtained for that movement. The evaluation data 413 includes the score for each detected movement.

As described above, the evaluation data 413 is data including scores assigned to each detectable action for each task. As described below, the evaluation data 413 also includes data representing the task proficiency determined based on the scores assigned to each detectable action for each task. The action comparison unit 422 may identify from the accumulated data 410 video footage captured during a time period corresponding to the time-series data (part of the sensing data) on which the scores are based, and associate the captured video footage with the evaluation data 413. Based on at least a portion of the evaluation data 413, the UI control unit 423 displays an evaluation UI on the display device 404 (or the instructor's PC). The evaluation UI is a UI (typically a GUI (Graphical User Interface)) that displays the evaluation results. The evaluation UI may be prepared for the entire scraping task, for each task that constitutes the scraping task, or for each pair of task and detectable action. As shown in FIG. 8 , the evaluation UI 800 displays a time axis 801 and a video 802 captured at a specified time point on the time axis 801. The evaluation UI 800 may also display, at reference numeral 803, a comment offering advice regarding a point on the time axis 801 for an evaluation item with a score below a certain value. The evaluation UI 800 also displays, at reference numeral 804, the assigned score and a time series of measured values (such as acceleration) for each detection target action for the entire task targeted by the evaluation UI 800. The evaluation UI 800 also displays, at reference numeral 805, the total score. In addition to the total score, the specified task proficiency level (e.g., "beginner") may be displayed as setting X, and the total score evaluation for the task proficiency level (e.g., "pass") may also be displayed as setting Y.

Refer to Figure 4 again.

The proficiency management data 414 is data that represents the task proficiency corresponding to each of a number of different score ranges. The action comparison unit 422 may store the assigned score in the storage device 405, identify the task proficiency corresponding to the score range to which the score belongs, and include the identified task proficiency in the evaluation data 413. The "score range" referred to here may be prepared for the entire scraping operation, for each operation that makes up the scraping operation, or for each pair of operation and detection target operation. Furthermore, the "score" compared with the score range may be a score statistic such as the cumulative value or average value of the assigned scores.

The trainee management data 415 is data that includes information for each trainee (e.g., the trainee's ID). The evaluation data 413 may include at least a portion of the trainee's 110 information (e.g., ID).

The movement evaluation data 416 is data in a predetermined format obtained by the integration unit 424 from the evaluation data 413. The "predetermined format" referred to here may be a format that includes, as items, the ID of the trainee 110 and each detection target movement for each task that constitutes the scraping work. The movement evaluation data 416 may include the ID of the trainee 110 and the movement evaluation results (e.g., scores) of each detection target movement for each task that constitutes the scraping work. Movement evaluation data 416 may exist for each trainee.

The quality evaluation data 417 may be data that represents quality in the same format as the above-mentioned predetermined format. Specifically, for example, the quality evaluation data 417 may include the trainee's 110 ID and the quality evaluation results (e.g., three levels: A, B, and C) of each detection target action for each task that makes up the scraping work. The quality evaluation results may be input by the trainer 111, or may be input automatically based on the analysis results of the filmed video. Quality evaluation data 417 may exist for each trainee.

The integrated data 418 is data obtained by integrating the movement evaluation data 416 and quality evaluation data 417 by the integration unit 424. For example, the integration unit 424 identifies the movement evaluation data 416 and quality evaluation data 417 using the trainee ID input from the input device 403 as a key, and integrates the identified movement evaluation data 416 and quality evaluation data 417. As a result, for each detection target movement for each task that makes up the scraping work, the quality evaluation results are associated with the movement evaluation results in one integrated data 418, making it possible to manage both the movement evaluation results and the quality evaluation results.

The above explanation can be summarized, for example, as follows. The summary below may include supplementary and modified explanations of the above explanation.

A sensor set including a pressure sensor 302 that measures pressure applied by a finger and an acceleration sensor that measures the acceleration of the worker's hand is worn on the worker's hand. In an embodiment, a sensor glove 102 equipped with the sensor set and a communication device is worn on the worker's hand. The worker may be a trainee 110 (an example of a first worker) or an expert (an example of a second worker). If the latter is a worker, second movement information may be prepared based on sensing data from one or more experts, and the second movement information may be compared with the movement information of the first worker.

Of the functions 420-424 described above, the activity detection system includes at least the action detection unit 421 and the action comparison unit 422. In the embodiment, the activity detection system includes a sensor set (one example is the sensor glove 102) and an information processing device (one example is the mobile PC 150), but the sensor set may also be the activity detection system (for example, the sensor glove 102 may store the accumulated data 410 and include the action detection unit 421 and the action comparison unit 422). Furthermore, at least a portion of the action detection unit 421 and the action comparison unit 422 may be implemented by an information processing device, which may be a local or remote physical computer system (one or more physical computers) capable of short-range wireless communication or other types of communication with the sensor set, or a logical computer system (for example, a cloud computing service) based on a physical computer system (for example, a cloud platform).

The movement detection unit 421 detects the movements of the trainee 110 during work based on sensing data including pressure and acceleration measured by the sensor set. The movement comparison unit 422 compares first movement information (movement information obtained from the sensing data of the trainee 110) with second movement information regarding the detected movements in order to evaluate the trainee 110.

This can assist in the acquisition of skills for a task that requires a specific movement, including moving the hand while the fingers are pressed against an object (an example is CV cable 115). Movement detection may be performed, for example, as follows: For each movement to be detected, movement meta-information such as a pattern or characteristics of a time series of measurement values in the sensing data is prepared (for example, the movement meta-information for each movement to be detected is included in the evaluation method data 412), and the movement detection unit 421 may detect a movement to be detected that corresponds to movement meta-information whose similarity to the movement meta-information obtained from the sensing data of the trainee 110 is equal to or greater than a predetermined value.

If the difference between the first movement information and the second movement information exceeds a predetermined range, the movement comparison unit 422 may detect that the difference exceeds the predetermined range. This makes it possible to determine whether the trainee's 110 work is good or bad.

The specified movement can be a repetitive movement involving repeated hand movement. This makes it possible to accurately detect repetitive movements (for example, delicate repetitive movements such as scraping in millimeters) and appropriately evaluate tasks that include such movements (one example is scraping work).

The motion detection unit 421 may detect a predetermined motion based on at least one of pressure, speed, acceleration, angle, angular velocity, movement distance, and geomagnetism identified from the sensing data. This is expected to improve the accuracy of detecting the predetermined motion.

The task detection system may include a UI control unit 423. The UI control unit 423 outputs output information, which is information including information representing an evaluation based on the results of the comparison, to the trainee 110 or to the trainee's trainee 110. When output information (e.g., an evaluation UI) for the trainee 110 is output, the trainee 110 can train himself. When output information for the trainee 110 is output, the trainee 110 can understand the trainee's situation (e.g., tasks to improve proficiency) from the output information and identify what instruction to provide to the trainee 110. For example, in response to the trainee 110 outputting output information for the trainee 110, the UI control unit 423 may accept input of instruction information representing instruction to the trainee 110, and output output information for the trainee 110 including the input instruction information to the trainee 110. In this way, the trainer can provide support for instruction from the trainee 110.

The second movement information may be movement information of a second worker who is more skilled at repetitive movements than the trainee 110. The movement comparison unit 422 may identify first movement information of the repetitive movements from the sensing data. The movement comparison unit 422 may assign a score to the trainee 110 based on the result of comparing the first movement information with the second movement information and a predetermined evaluation method. The evaluation of the trainee 110's work may be based on the assigned score. This makes it possible to quantitatively check the skill of the work.

The action comparison unit 422 may assign a score for each of one or more evaluation items. If an assigned score for any evaluation item is lower than a predetermined score, the output information output by the UI control unit 423 may include information representing the evaluation item and information representing the factor(s) that caused the assigned score to be lower than the predetermined score. This is expected to appropriately improve the actions of the trainee 110, thereby promoting task proficiency. The output of information representing the factors may be realized, for example, as follows: The evaluation method data 412 may include, for each detection target action (or each task constituting the scraping task), a predetermined score (score threshold), information representing one or more cases in which the score is lower than the predetermined score (e.g., information representing the characteristics of the measurement value time series), and information representing possible factors for the score being lower than the predetermined score for each case (e.g., a text message). The action comparison unit 422 may identify possible factors from the evaluation method data 412 based on the first action information and the results of the comparison, and output information including information representing the identified factors may be output. The factor represented by the output information may be the main factor, which may be the factor that accounts for the largest proportion of one or more applicable factors. For example, the main factor may be the factor corresponding to the evaluation item that has been assigned a score that deviates most from the target score among one or more evaluation items. Information representing the target score for the evaluation item for each detection target action may be included in the evaluation method data 412.

The motion detection unit 421 may detect motions different from repetitive motions based on the sensing data. This is expected to improve the accuracy of the evaluation by the motion comparison unit 422. The different motions may be motions between repetitive motions. For example, if a repetitive motion is interrupted, a different motion is performed, and then the repetitive motion is resumed in a series of repetitive motions, the different motion performed between the repetitive motions may be detected. Specific examples of different motions may include replacing the cutting tool 201 (an example of a processing tool) or wiping sweat. Different motions may be detected, for example, by preparing information representing the characteristics of the measurement value time series of the different motions and identifying a measurement value time series from the sensing data that has a high similarity to such characteristics. Furthermore, the different motions may be motions corresponding to portions of the sensing data that have a correlation with the portion of the sensing data corresponding to the repetitive motion that is lower than a predetermined value. This is expected to enable accurate detection of different motions.

The movement comparison unit 422 may assign a score to the trainee 110 based on the rate or number of different movements that occur within a specified time period. This is expected to improve the accuracy of the assigned score. For example, the score may be based on at least one of the quality of the repetitive movements, the rate of occurrence of non-repetitive movements (movements that are not repeated), the number of occurrences, and the duration of occurrence.

Each of the different score ranges is associated with a task proficiency level corresponding to that score range, and the output information output by the UI control unit 423 may include information indicating the task proficiency level corresponding to the score range to which the assigned score belongs. This makes it possible to estimate the task proficiency level of the trainee 110.

Furthermore, for each of the different levels of work proficiency, there may be one or more evaluation items corresponding to that level of work proficiency. The predetermined evaluation method may include assigning a score for each of the one or more evaluation items corresponding to the work proficiency of the trainee 110. The work proficiency of the trainee 110 may be a work proficiency corresponding to the score range to which the assigned score belongs. This is expected to provide evaluation and work proficiency support according to the work proficiency of the trainee 110.

The movement detection unit 421 may use data from one or more captured images of the work being done by the trainee 110 to identify the movement. This is expected to improve the accuracy of movement identification.

In addition to comparing the first movement information with the second movement information, the movement comparison unit 422 may also compare the first status information with the second status information. The first status information may be information representing the status of an object as a result of the trainee's 110's repetitive movement. The second status information may be information representing the status of the object and being compared with the first status information. The status of the object, which is the result of the movement (in other words, quality), is also considered one of the evaluation criteria for the trainee 110, and therefore, improved evaluation accuracy is expected. Both the first and second status information may be manually input. The status represented by the first status information may be a state identified based on data from one or more captured images of the work performed by the trainee 110. This reduces the burden of manually inputting the first status information.

The task detection system may include an integration unit 424. The integration unit 424 may associate the assigned score with a score representing the state of the object as a result of the task performed by the trainee 110. The output information may include information representing the score assigned to the trainee 110 and the score representing the state of the object as a result of the task performed by the trainee 110. This enables centralized management of tasks and their resulting quality. For example, the task evaluation data 416 may include information representing the task score, and the quality evaluation data 417 may include information representing the quality score (e.g., a three-level score of A, B, and C). The integration unit 424 may generate integrated data 418 that associates the task score represented by the task evaluation data 416 with the quality score represented by the quality evaluation data 417, and the integrated data 418 may be displayed by the UI control unit 423.

The output information output by the UI control unit 423 may include information representing advice for improvement regarding items of the first movement information that deviate from the second movement information by a certain degree or more. This provides further support for work mastery. Note that the information representing advice may be included, for example, in the evaluation method data 412 for each movement to be detected.

The predetermined motion may be a repetitive motion including repeated movement of the hand while the cutting tool 201 is pressed against the CV cable 115 by the fingers. The different motion may be a motion of changing the orientation of the cutting tool 201. The motion comparison unit 422 may evaluate the trainee 110 based on the elapsed time from the start of the predetermined motion and the ratio or number of times that different motions occur within the predetermined time. This is expected to appropriately evaluate the trainee 110's work proficiency in terms of the frequency of the motion of changing the orientation of the cutting tool 201. For example, if the motion of changing the orientation of the cutting tool 201 is performed frequently within a short time after the start of the repetitive motion, the evaluation may be low. However, if the motion of changing the orientation of the cutting tool 201 is performed frequently after a long time has passed since the start of the repetitive motion, the evaluation may not be low. This is because if the time that has elapsed since the start of the repetitive motion is short, the degree of wear on the cutting tool 201 is small, and so the frequency of the operation of changing the orientation of the cutting tool 201 is low; on the other hand, if the time that has elapsed since the start of the repetitive motion is long, the cutting tool 201 wears out, and the frequency of the operation of changing the orientation of the cutting tool 201 increases. Note that the cutting tool 201 may be an example of a processing tool that is subject to wear and tear. Furthermore, if there are many different motions, the time spent on cutting work will be relatively small out of the time the worker is engaged in work, and so the evaluation may be lowered as it is considered that the time contributed to the worker's work is small.

The task of scraping off the outer semiconductive layer consists of multiple tasks, and the motion detection unit 421 may detect the motion of the trainee 110 based on information indicating which of the multiple tasks the trainee 110 is performing. The same motion may be detected in two or more of the multiple tasks. The motion comparison unit 422 may identify to which task the detected motion belongs based on the information indicating which of the multiple tasks the trainee 110 is performing. The motion comparison unit 422 may compare first motion information related to the detected motion with second motion information corresponding to the task to which the detected motion belongs. This enables an appropriate evaluation based on the task to which the detected motion belongs.

Although one embodiment has been described above, this is merely an example for the purpose of explaining the present invention, and is not intended to limit the scope of the present invention to only these embodiments. The present invention can also be implemented in various other forms.

The above-described embodiments can also be implemented for tasks in which workers process workpieces using cutting tools, abrasives, etc. For example, they can be applied to carpentry work such as planing the surface of wood. This can detect tasks that differ from the repetitive task of a worker moving the surface of wood from a first position to a second position, and the task of changing the blade of the plane. They can also be applied to so-called spatula squeezing, in which a tool is applied to a rotating metal object to change its shape. In other words, this can detect tasks that differ from the repetitive task of a worker moving a spatula from a first position to a second position on the metal object to be processed while pressing it against the object, such as changing the angle of the spatula or pressing it against the metal object to check for changes in the spatula's surface shape or the shape of the metal object to be processed. They can also be applied to polishing work to increase the sphericity of the spherical surface of a glass lens. When polishing a rotating glass lens, a worker must move their hands in a certain amount while pressing a cloth against it, which is a repetitive action. Typical different tasks include changing the position of the cloth that comes into contact with the glass lens, changing to a cloth with a different surface roughness or hardness, impregnating the cloth with abrasives, applying cutting oil, and visually checking the condition of the glass lens.

The above-described embodiment can be implemented for tasks other than the removal of an external semiconductor layer as described above, and can be used to evaluate processes and detect work conditions in which different actions are performed between repeated tasks in which a worker uses a tool or implement on a workpiece.

102: Sensor Gloves 150: Mobile PC

Claims (23)

a sensor set including a pressure sensor that measures pressure applied by a finger of a first worker performing a task that requires a predetermined movement including movement of a hand while the finger is pressed against an object, and an acceleration sensor that measures acceleration of the hand of the first worker;
a motion detection unit that detects a motion during the work based on sensing data including pressure and acceleration measured by the sensor set;
a motion comparison unit that compares first motion information, which is motion information obtained from sensing data of the first worker, with second motion information regarding the detected motion for the evaluation of the first worker;
Equipped with
the work is a work of scraping off an outer semiconductive layer from the CV cable to expose an insulating layer of a specified size from the CV cable;
the object is a CV cable,
the sensor set is mounted in a glove that is worn on a hand;
the predetermined motion is a repetitive motion including repeated hand movements with the fingers pressing the cutting tool against the CV cable;
the motion detection unit detects a motion different from the repetitive motion based on the sensing data;
the different motion is a motion of changing the orientation of the cutting tool;
the action comparison unit evaluates the first worker based on the elapsed time from the start of the predetermined action and the rate or number of times the different action occurs within the predetermined time.
A work detection system characterized by: a sensor set including a pressure sensor that measures pressure applied by a finger of a first worker performing a task that requires a predetermined movement including movement of a hand while the finger is pressed against an object, and an acceleration sensor that measures acceleration of the hand of the first worker;
a motion detection unit that detects a motion during the work based on sensing data including pressure and acceleration measured by the sensor set;
a motion comparison unit that compares first motion information, which is motion information obtained from sensing data of the first worker, with second motion information regarding the detected motion for the evaluation of the first worker;
Equipped with
the work is a work of scraping off an outer semiconductive layer from the CV cable to expose an insulating layer of a specified size from the CV cable;
the object is a CV cable,
the sensor set is mounted in a glove that is worn on a hand;
The process of scraping off the outer semiconductive layer consists of several steps:
the motion detection unit detects a motion of the first worker based on information indicating which of the plurality of tasks the first worker is performing; and
The same motion is detected in two or more of the plurality of tasks,
The operation comparison unit
Identifying to which task the detected movement belongs based on information indicating which of the plurality of tasks the first worker is performing; and
comparing first motion information relating to the detected motion with second motion information corresponding to the type of task to which the detected motion belongs;
A work detection system characterized by: a sensor set including a pressure sensor that measures pressure applied by a finger of a first worker performing a task that requires a predetermined movement including movement of a hand while the finger is pressed against an object, and an acceleration sensor that measures acceleration of the hand of the first worker;
a motion detection unit that detects a motion during the work based on sensing data including pressure and acceleration measured by the sensor set;
a motion comparison unit that compares first motion information, which is motion information obtained from sensing data of the first worker, with second motion information regarding the detected motion in order to evaluate the first worker ;
the motion detection unit detects a motion different from a repetitive motion based on the sensing data,
the action comparison unit assigns a score to the first worker based on a rate or number of times the different actions occur within a predetermined time period;
the different action is an action corresponding to a sensing data portion of the sensing data, the correlation of which with the sensing data portion corresponding to the repetitive action is lower than a predetermined value;
A work detection system characterized by: The operation detection system according to claim 3 ,
When a difference between the first motion information and the second motion information exceeds a predetermined range, the motion comparison unit detects that the difference exceeds the predetermined range.
A work detection system characterized by: The operation detection system according to claim 3 ,
The predetermined motion is a repetitive motion in which the movement of the hand is repeated.
A work detection system characterized by: The operation detection system according to claim 5 ,
the motion detection unit detects the predetermined motion based on at least one of pressure, speed, acceleration, angle, angular velocity, movement distance, sound, and geomagnetism identified from the sensing data;
A work detection system characterized by: The operation detection system according to claim 3 ,
a user interface control unit that outputs output information, which is information including information representing an evaluation based on a result of the comparison, to the first worker or to an instructor of the first worker;
The work detection system further comprises: The operation detection system according to claim 5 ,
the second motion information is motion information of a second worker who is more skilled in the repetitive motion than the first worker,
The operation comparison unit
Identifying first motion information of the repetitive motion from the sensing data;
assigning a score to the first worker based on a result of comparing the identified first motion information with the second motion information and a predetermined evaluation method;
The evaluation of the work of the first worker is based on the assigned score.
A work detection system characterized by: The operation detection system according to claim 8 ,
a user interface control unit that outputs output information, which is information including information representing an evaluation based on a result of the comparison, to the first worker or to an instructor of the first worker;
Further provided with
the action comparison unit assigns a score for each of one or more evaluation items;
If there is an evaluation item for which the assigned score is lower than a predetermined score, the output information includes information representing the evaluation item and information representing a factor for the assigned score being lower than the predetermined score.
A work detection system characterized by: The operation detection system according to claim 8 ,
a user interface control unit that outputs output information, which is information including information representing an evaluation based on a result of the comparison, to the first worker or to an instructor of the first worker;
Further provided with
A task proficiency level corresponding to each of the different score ranges is associated with the score range;
the output information includes information representing a task proficiency level corresponding to a score range to which the assigned score belongs among the plurality of score ranges;
A work detection system characterized by: The operation detection system according to claim 5 ,
the motion detection unit uses data of one or more captured images of the work performed by the first worker to identify the motion;
A work detection system characterized by: The operation detection system according to claim 5 ,
the operation comparison unit compares the first operation information with the second operation information and also compares the first status information with the second status information;
the first state information is information representing a state of the object as a result of the repetitive action of the first worker,
the second status information is information that represents a status of the object and is compared with the first status information;
A work detection system characterized by: The operation detection system according to claim 12 ,
the state represented by the first state information is a state identified based on data of one or more captured images of the work performed by the first worker;
A work detection system characterized by: The operation detection system according to claim 7 ,
The user interface control unit
receiving input of guidance information representing guidance to the first worker in response to output of output information intended for the instructor of the first worker;
outputting output information for the first worker, including the input guidance information, to the first worker;
A work detection system characterized by: The operation detection system according to claim 3 ,
a user interface control unit that outputs output information to the first worker or to an instructor of the first worker, the output information including information representing an evaluation based on a result of the comparison;
an integration unit,
the motion comparison unit assigns a score to the first worker based on a result of comparing the first motion information with the second motion information and a predetermined evaluation method;
the integrating unit associates the assigned score with a score representing a state of the object as a result of the work of the first worker;
the output information includes information representing a score assigned to the first worker and a score representing a state of the object as a result of the work of the first worker;
A work detection system characterized by: The operation detection system according to claim 3 ,
the action comparison unit assigns a score to the first worker based on a result of comparing the first action information with the second action information, the work proficiency of the first worker, and a predetermined evaluation method;
A task proficiency level corresponding to each of the different score ranges is associated with the score range;
For each of a plurality of different work proficiencies, there are one or more evaluation items corresponding to the work proficiency,
the predetermined evaluation method includes assigning a score to each of one or more evaluation items corresponding to the work proficiency of the first worker;
The task proficiency of the first worker is a task proficiency corresponding to a score range to which the score assigned to the first worker belongs.
A work detection system characterized by: The operation detection system according to claim 3 ,
a user interface control unit that outputs output information to the first worker, the output information including information representing an evaluation based on the result of the comparison;
Further provided with
the output information includes information indicating advice for improvement for an item of the first motion information that deviates from the second motion information by a certain degree or more;
A work detection system characterized by: a motion detection unit that detects a motion in a task based on sensing data including pressure and acceleration measured by a sensor set including a pressure sensor that measures pressure applied by the fingers of a first worker performing a task that requires a predetermined motion including movement of a hand while the fingers are pressed against an object, and an acceleration sensor that measures acceleration of the hand of the first worker;
a motion comparison unit that compares first motion information, which is motion information obtained from sensing data of the first worker, with second motion information regarding the detected motion in order to evaluate the first worker ;
the work is a work of scraping off an outer semiconductive layer from the CV cable to expose an insulating layer of a specified size from the CV cable;
the object is a CV cable,
the sensor set is mounted in a glove that is worn on a hand;
the predetermined motion is a repetitive motion including repeated hand movements with the fingers pressing the cutting tool against the CV cable;
the motion detection unit detects a motion different from the repetitive motion based on the sensing data;
the different motion is a motion of changing the orientation of the cutting tool;
the action comparison unit evaluates the first worker based on the elapsed time from the start of the predetermined action and the rate or number of times the different action occurs within the predetermined time.
A work detection device characterized by: a motion detection unit that detects a motion in a task based on sensing data including pressure and acceleration measured by a sensor set including a pressure sensor that measures pressure applied by the fingers of a first worker performing a task that requires a predetermined motion including movement of a hand while the fingers are pressed against an object, and an acceleration sensor that measures acceleration of the hand of the first worker;
a motion comparison unit that compares first motion information, which is motion information obtained from sensing data of the first worker, with second motion information regarding the detected motion for the evaluation of the first worker;
Equipped with
the work is a work of scraping off an outer semiconductive layer from the CV cable to expose an insulating layer of a specified size from the CV cable;
the object is a CV cable,
the sensor set is mounted in a glove that is worn on a hand;
The process of scraping off the outer semiconductive layer consists of several steps:
the motion detection unit detects a motion of the first worker based on information indicating which of the plurality of tasks the first worker is performing; and
The same motion is detected in two or more of the plurality of tasks,
The operation comparison unit
Identifying to which task the detected movement belongs based on information indicating which of the plurality of tasks the first worker is performing; and
comparing first motion information relating to the detected motion with second motion information corresponding to the type of task to which the detected motion belongs;
A work detection device characterized by: a motion detection unit that detects a motion in a task based on sensing data including pressure and acceleration measured by a sensor set including a pressure sensor that measures pressure applied by the fingers of a first worker performing a task that requires a predetermined motion including movement of a hand while the fingers are pressed against an object, and an acceleration sensor that measures acceleration of the hand of the first worker;
a motion comparison unit that compares first motion information, which is motion information obtained from sensing data of the first worker, with second motion information regarding the detected motion for the evaluation of the first worker;
Equipped with
the motion detection unit detects a motion different from a repetitive motion based on the sensing data,
the action comparison unit assigns a score to the first worker based on a rate or number of times the different actions occur within a predetermined time period;
the different action is an action corresponding to a portion of the sensing data, the portion of the sensing data having a correlation with the portion of the sensing data corresponding to the repetitive action lower than a predetermined value;
A work detection device characterized by: a motion detection step in which a computer detects a motion in a task based on sensing data including pressure and acceleration measured by a sensor set including a pressure sensor that measures pressure applied by the fingers of a first worker performing a task that requires a predetermined motion including movement of a hand while the fingers are pressed against an object, and an acceleration sensor that measures acceleration of the hand of the first worker;
a motion comparison step in which a computer compares first motion information, which is motion information obtained from sensing data of the first worker, with second motion information regarding the detected motion for evaluation of the first worker;
and
the work is a work of scraping off an outer semiconductive layer from the CV cable to expose an insulating layer of a specified size from the CV cable;
the object is a CV cable,
the sensor set is mounted in a glove that is worn on a hand;
the predetermined motion is a repetitive motion including repeated hand movements with the fingers pressing the cutting tool against the CV cable;
In the motion detection step, a motion different from the repetitive motion is detected based on the sensing data,
the different motion is a motion of changing the orientation of the cutting tool;
In the action comparison step, the first worker is evaluated based on the elapsed time from the start of the predetermined action and the rate or number of occurrences of the different action within the predetermined time.
A work detection method characterized by: a motion detection step in which a computer detects a motion in a task based on sensing data including pressure and acceleration measured by a sensor set including a pressure sensor that measures pressure applied by the fingers of a first worker performing a task that requires a predetermined motion including movement of a hand while the fingers are pressed against an object, and an acceleration sensor that measures acceleration of the hand of the first worker;
a motion comparison step in which a computer compares first motion information, which is motion information obtained from sensing data of the first worker, with second motion information regarding the detected motion for evaluation of the first worker;
and
the work is a work of scraping off an outer semiconductive layer from the CV cable to expose an insulating layer of a specified size from the CV cable;
the object is a CV cable,
the sensor set is mounted in a glove that is worn on a hand;
The process of scraping off the outer semiconductive layer consists of several steps:
In the movement detection step, a movement of the first worker is detected based on information indicating which of the plurality of tasks the first worker is performing;
The same motion is detected in two or more of the plurality of tasks,
In the operation comparison step,
Identifying to which task the detected movement belongs based on information indicating which of the plurality of tasks the first worker is performing; and
comparing first motion information relating to the detected motion with second motion information corresponding to the type of task to which the detected motion belongs;
A work detection method characterized by: a motion detection step in which a computer detects a motion in a task based on sensing data including pressure and acceleration measured by a sensor set including a pressure sensor that measures pressure applied by the fingers of a first worker performing a task that requires a predetermined motion including movement of a hand while the fingers are pressed against an object, and an acceleration sensor that measures acceleration of the hand of the first worker;
a motion comparison step in which a computer compares first motion information, which is motion information obtained from sensing data of the first worker, with second motion information regarding the detected motion for evaluation of the first worker;
and
In the motion detection step, a motion different from a repetitive motion is detected based on the sensing data,
In the action comparison step, a score is assigned to the first worker based on a rate or number of times the different actions occur within a predetermined time period;
the different action is an action corresponding to a portion of the sensing data, the portion of the sensing data having a correlation with the portion of the sensing data corresponding to the repetitive action lower than a predetermined value;
A work detection method characterized by: