JP2017062565A - Support apparatus and support method - Google Patents

Abstract

A support device and a support method capable of supporting an operating device with high accuracy are provided. A brain activity analysis unit for analyzing a driver's brain activity, a gaze analysis unit for analyzing a driver's gaze, and a driver's brain activity and gaze analysis unit analyzed by the brain activity analysis unit. And a driving intention detection unit 13 that detects the driver's operation intention based on the driver's line-of-sight movement amount analyzed in 11 and the driver's past driving behavior stored in the driving behavior database 25. . Furthermore, when the driving intention detection unit 13 detects that the driver has an operation intention, the driving intention detection unit 13 includes an environment determination control unit 27 that outputs a support command for the driving behavior. With such a configuration, it is possible to support an operating device such as a vehicle. [Selection] Figure 1

Description

  The present invention relates to a support device and a support method for supporting operation of an operation device such as a vehicle, a game machine, or factory equipment.

  As a support device that supports the driving operation of a vehicle driver, for example, one described in Patent Document 1 is known. The patent document 1 discloses that when a vehicle enters a curved road, an appropriate risk judgment is made by detecting a region that can be dangerous based on the past driving tendency of the driver of the vehicle. Has been.

JP 2007-164432 A

  However, the conventional example disclosed in Patent Document 1 described above has a problem that it cannot support recognition of the operating environment with high accuracy because it makes a risk judgment on a curved road based on the travel history of the vehicle.

  The present invention has been made to solve such a conventional problem, and an object of the present invention is to provide a support device and a support method capable of supporting recognition of an operation environment with high accuracy. There is.

  In order to achieve the above object, an aspect of the support device according to the present invention includes a brain activity analysis unit that analyzes a subject's brain activity and a gaze analysis unit that analyzes the subject's gaze. In addition, an operation intention detection unit that detects an operation intention of the subject based on the subject's brain activity, the subject's line-of-sight movement amount, and the subject's past operation behavior is provided. Furthermore, the operation intention detection unit includes a support control unit that outputs a support command for an operation action when it is detected that the subject has an operation intention.

  Also, one aspect of the support method according to the present invention includes a step of analyzing a subject's brain activity, a step of analyzing the subject's gaze, a subject's brain activity, a gaze movement amount of the subject, and a subject The operation intention of the target person is detected based on the past operation behavior of the person. Further, when it is detected that the subject has an operation intention, a support command for the operation action is output.

  According to the present invention, since the operation intention of the subject is detected based on the subject's brain activity and the amount of eye movement, it is possible to support recognition of the operation environment with high accuracy.

It is a block diagram which shows the structure of the support apparatus which concerns on embodiment of this invention, and its peripheral device. It is a flowchart which shows the process sequence of the support apparatus which concerns on embodiment of this invention. It is explanatory drawing which shows the timing which implements the support process by the support apparatus which concerns on embodiment of this invention. It is a graph which shows the predominance of a line-of-sight movement when measuring the amount of line-of-sight movement with respect to two drivers.

  Hereinafter, embodiments of the present invention will be described with reference to the drawings. FIG. 1 is a block diagram illustrating a configuration of a support device and peripheral devices according to an embodiment of the present invention. In the present embodiment, a vehicle is taken as an example of the operation device, and a case where a driving operation of a driver (target person) driving the vehicle is supported will be described.

  As shown in FIG. 1, the support device 100 includes a line-of-sight analysis unit 11, a brain activity analysis unit 12, a driving intention detection unit 13, and an environment determination control unit 27 (support control unit).

  Note that the support device 100 can be configured as an integrated computer including a central processing unit (CPU), storage means such as RAM, ROM, and hard disk.

  The line-of-sight analysis unit 11 is connected to a line-of-sight movement detection unit 21 and a line-of-sight movement amount database 22 (line-of-sight movement amount storage unit). The brain activity analysis unit 12 is connected to a brain activity detection unit 23 and a brain activity database 24 (brain activity data storage unit), and the driving intention detection unit 13 includes an ambient environment detection unit 26 and a driving behavior database 25 ( Connected to the operation action data storage unit).

  The line-of-sight movement detection unit 21 detects the movement of the driver's eye and detects the movement of the driver's line of sight. For example, a driver's eyeball is imaged with a camera disposed at an appropriate position of the vehicle, and dynamic information of the eyeball is acquired from an image obtained by imaging. Then, based on the movement of the eyeball during a predetermined time, the driver's gaze movement amount (gaze movement amount per predetermined time) is detected, and data of the gaze movement amount is output to the gaze analysis unit 11. Further, the line-of-sight movement amount data is output to the line-of-sight movement amount database 22.

  The line-of-sight movement amount database 22 stores the amount of line-of-sight movement for each driver of the vehicle. Specifically, the driver's line-of-sight movement detected by the line-of-sight movement detector 21 (hereinafter referred to as Sn) is acquired, and the acquired data of the line-of-sight movement is accumulated.

  The line-of-sight analysis unit 11 calculates an average value of line-of-sight movement amount (line-of-sight movement amount average value) from the line-of-sight movement amount detected by the line-of-sight movement detection unit 21 and the driver's past line-of-sight movement amount, and this average It is determined whether or not the driver's line of sight is moving greatly from the difference between the value and the detected line-of-sight movement amount.

  The brain activity detection unit 23 detects a driver's brain wave. Specifically, a dedicated probe is attached to the driver's head, and brain waves detected by the probe are acquired. Then, a visual processing depth (hereinafter referred to as VPn) is obtained from the acquired electroencephalogram. “Visual processing depth” is a numerical value that quantitatively indicates the degree of recognition of objects and situations existing in the driver's field of view, and detects the action of the visual cortex in the driver's brain based on brain waves. Can be recognized. In the present embodiment, “visual processing depth” obtained from “electroencephalogram” is described as an example of brain activity, but it is also possible to detect brain activity using “NIRS” or the like, for example. .

  The brain activity database 24 cumulatively stores data relating to the driver's brain waves. That is, data relating to the visual processing depth detected by the brain activity detection unit 23 is accumulated.

  The brain activity analysis unit 12 calculates the average value of visual processing depth (visual processing average value) of each driver stored in the brain activity database 24. When the brain activity detection unit 23 acquires visual processing depth data for a driver, a difference between the data and the average visual processing depth for the driver is obtained. Based on this difference, the driver's visual processing depth is determined.

  The driving behavior database 25 cumulatively stores past driving behavior data (operation behavior data) of each driver. For example, driving behavior data such as at which position the lane is changed or at which intersection the vehicle turns right or left when a certain driver is traveling on a frequently used traveling route is stored.

  The ambient environment detection unit 26 detects the ambient environment around the vehicle. Specifically, other vehicles traveling in the vicinity of the vehicle and other obstacles are detected based on the outside image captured by a camera (not shown) installed in the vehicle. And the information regarding surrounding environment is acquired and it outputs to the driving intention detection part 13 as surrounding environment data.

  The driving intention detection unit 13 determines the driver's attention level (this is A1) based on the data of the driver's line-of-sight movement amount, visual processing depth, the driver's past driving behavior, and the surrounding environment. ) And the driver's intention to operate is detected based on the attention level A1. In other words, it predicts what action the driver will take in the future. Then, data regarding the operation intention is output to the environment determination control unit 27.

  The environment determination control unit 27 outputs a support command to the subsequent support device when the driving intention detection unit 13 executes a support process for the driving behavior of the driver. For example, when it is detected by the driving intention detection unit 13 that the driver changes lanes, that is, when it is detected that there is an operation intention regarding driving behavior, an image display unit (support device not shown) is omitted. ) To output a command for displaying a surrounding image of the vehicle. That is, a support command for the operation action is output.

  Hereinafter, a specific processing procedure of the support device 100 according to the present embodiment will be described with reference to a flowchart shown in FIG. Now, as shown in FIG. 3, a case will be described in which the vehicle is traveling in the right lane of the two-lane traveling lane X1 and the lane is changed to the left lane after a few seconds.

  First, in step S11 of FIG. 2, the driving intention detection unit 13 acquires the driver's line-of-sight movement amount Sn. Thereafter, in step S12, a difference “Sn−Sb” (second difference) from the average value Sb of the driver's line-of-sight movement amount (average line-of-sight movement amount) is calculated.

  When the driver changes lanes, he / she carefully looks to the left and right just before actually changing lanes. That is, the line-of-sight movement amount Sn detected by the line-of-sight movement detection unit 21 increases. Therefore, the difference “Sn−Sb” between the line-of-sight movement amount Sn and the average value Sb of the driver's line-of-sight movement amount stored in the line-of-sight movement amount database 22 increases.

  Next, in step S13, the driving intention detection unit 13 acquires the visual processing depth VPn. Thereafter, in step S14, a difference “VPn−VPb” (first difference) from the average value VPb (visual processing average value) of the visual processing depth of the driver is calculated. When the lane is changed, the visual processing depth VPn acquired from the brain wave detected by the brain activity detection unit 23 increases.

  As described above, the “visual processing depth” indicates the degree of recognition of an object or situation included in the driver's visual field. In other words, just because the driver's line of sight is on an arbitrary object does not necessarily mean that the object is being watched. The visual processing depth is small when an object or situation in the field of view is viewed loosely (when not gazing). On the other hand, when an object or situation in the field of view is concentrated, the visual processing depth is large. In the case of a lane change, since the driver is gazing at other vehicles and obstacles existing around, the visual processing depth VPn is increased. For this reason, the difference “VPn−VPb” between the visual processing depth VPn and the average value VPb of the visual processing depth increases.

In step S15, the driving intention detection unit 13 obtains the multiplication value of the difference “Sn−Sb” and “VPn−VPb” as the attention degree A1. That is, the attention degree A1 is calculated by the following equation (1).
A1 = (Sn−Sb) * (VPn−VPb) (1)

  Then, the driving intention detection unit 13 compares the attention level A1 calculated by the above equation (1) with a threshold value Lth (predetermined threshold value) set in the processing described later, and the attention level A1 exceeds the threshold value Lth. If this happens, this fact is output to the environment judgment control unit 27. The environment determination control unit 27 outputs a support command signal for executing support processing. In this embodiment, as an example of the support process, when it is estimated that the driver changes the lane (operation behavior), a vehicle-mounted display (illustrated) displays a surrounding image necessary for the lane change at a time earlier than the estimated time. (Omitted).

  In steps S16 to S22, the threshold value Lth is set to an appropriate value based on the presence / absence of the driver's action history and the determination result of the possibility of action execution. Here, the history of whether or not the driver has changed lanes in the past is adopted as the action history, and the possibility of action execution will be described by taking the number of other vehicles around the vehicle as an example. .

  In step S16, the driving intention detection unit 13 determines whether or not there is an action history. In this process, it is determined whether or not there is a history of lane changes in the past on the travel route on which the vehicle is currently traveling. This process can be determined based on the data stored in the driving behavior database 25 shown in FIG. If there is a history, the process proceeds to step S17. If there is no history, the process proceeds to step S18.

  In step S <b> 17, the driving intention detection unit 13 determines whether there is a possibility of action execution. Specifically, it is determined whether there are many other vehicles around the vehicle. If there are few other vehicles, it is determined that the action can be executed, that is, the lane can be changed. If it is determined that action execution is possible (YES in step S17), the threshold value Lth is set to the first threshold value Lth1 in step S20. On the other hand, if it is determined that action execution is not possible (NO in step S17), in step S19, the threshold value Lth is set to the second threshold value Lth2 (where Lth2> Lth1).

  On the other hand, in step S18, the driving intention detection unit 13 determines whether there is a possibility of action execution. This process is the same as the process of step S17 described above. If it is determined that action execution is possible (YES in step S18), the threshold value Lth is set to the third threshold value Lth3 in step S21. On the other hand, if it is determined that action execution is not possible (NO in step S18), the threshold value Lth is set to the fourth threshold value Lth4 in step S22. Here, the threshold values have a relationship of Lth4> Lth3> Lth2> Lth1.

The processing of steps S16 to S22 described above is summarized as shown in the following (A) to (D).
(A) When there is an action history and action execution is possible, the threshold value Lth is set to “Lth1” which is the lowest numerical value. If the driver has frequently changed lanes at that point in the past, and there are few other vehicles traveling around, it is determined that the driver is likely to change lanes near the current driving point. Therefore, the threshold value Lth is set to the first threshold value Lth1, which is a low numerical value. That is, the threshold value Lth is set so that it is easy to determine that there is an intention to change lanes.

(B) When there is an action history and action execution is not possible, the threshold value Lth is set to the second threshold value Lth2, which is the second lowest numerical value. In the past, when the driver has frequently changed lanes at that point, and when many other vehicles are traveling around, the threshold Lth is set to the second threshold Lth2 higher than the first threshold Lth1. .

(C) If there is no action history and action execution is possible, the threshold value Lth is set to the third threshold value Lth3, which is the third lowest value. If there is no history that the driver has changed lanes at that point in the past and there are few other vehicles traveling around, the threshold Lth is set to a third threshold Lth3 that is higher than the second threshold Lth2.

(D) If there is no action history and action execution is not possible, the threshold value Lth is set to the fourth threshold value Lth4 which is the highest numerical value. If there is no history that the driver has changed lanes at that point in the past, and there are many other vehicles traveling around, it is determined that the driver is unlikely to change lanes near the current point. Therefore, the threshold value Lth is set to a fourth threshold value Lth4 that is higher than the third threshold value Lth3.

  Thus, since the threshold value Lth for judging the magnitude of the attention degree A1 is set based on the presence / absence of the driver's behavior history and the action feasibility, the past driving behavior and the surrounding environment An appropriate threshold value Lth can be set accordingly.

  Next, in step S23 of FIG. 2, the driving intention detection unit 13 compares the attention level A1 calculated by the above equation (1) with the threshold value Lth set in each process of steps S19 to S22. If the attention level A1 exceeds the threshold value Lth (YES in step S23), in step S24, the driver determines that there is an intention of action (in this example, an intention of changing lanes). When it is determined that there is an intention of action, information indicating that is output to the environment determination control unit 27. In step S25, the environment determination control unit 27 outputs a support command signal. As a result, for example, a support process such as enlarging and displaying the left and right images of the vehicle on a display mounted in the vehicle is executed. Therefore, the driver can recognize the situation of the left and right of the vehicle in more detail, which can be used for lane change.

  In step S26, the driving intention detection unit 13 determines whether or not the action is completed. Specifically, it is determined whether or not the lane change is completed. In this process, for example, by detecting short-term odometry, it is possible to detect that the lane change is completed by recognizing that the vehicle straddles a partition line (white line or the like) on the road. It is also possible to detect the end of lane change by using GPS data.

  If the lane change is completed, the output of the support command is terminated in step S27. On the other hand, if the attention level A1 does not exceed the threshold Lth in the process of step S23, the process from step S11 is repeated.

  As described above, in the present embodiment, the driver's attention level A1 is calculated based on the driver's brain activity and the amount of eye movement, and when the attention level A1 exceeds a preset threshold value Lth, Perform support processing. That is, since the driver who wants to change the lane confirms the surrounding situation, the line-of-sight movement amount Sn is large, and the visual processing depth VPn is large. And based on these, it becomes possible to perform a support process in the suitable timing before a driver | operator changes a lane. At this time, since the driver does not need to perform any operation input, the support process can be executed without making the driver feel bothered.

  Next, a process for setting the timing for executing the support process will be described. By the above processing, the intention of changing the lane is detected. For example, when it is estimated that the lane changing operation is executed after 5 seconds, a time point that is a predetermined time before the estimated time (for example, 5 seconds) Support processing can be executed at the previous time). However, the timing at which the support process is required is different for each driver. For example, if the driver is a beginner, early support processing is required, and if the driver is an expert, if the support processing is executed early, it will be bothersome. Specifically, when a process for enlarging a surrounding image of a vehicle and displaying it on a display (not shown) or the like is executed as support processing for changing lanes, such an image display is displayed early for a skilled person. You may feel uncomfortable.

  In the present embodiment, it is measured how far before the estimated time when the lane is changed for each driver, and the movement of the line of sight increases. Based on the degree of the line of sight movement, each driver feels uncomfortable. Set the timing to execute the support process without feeling. Then, this data is stored in the line-of-sight movement amount database 22. This will be specifically described below.

  FIG. 4 is a graph showing the dominance of line-of-sight movement when the line-of-sight movement amount is measured for two drivers M1 and M2. FIG. 4A shows the superiority data for the driver M1, and FIG. 4B shows the superiority data for the driver M2. 4A and 4B, the vertical axis indicates the measurement start time when the time when the lane change is executed is “0”, and the horizontal axis indicates the measurement end time. And the "+" mark shown in a figure has shown the magnitude | size of superiority. More specifically, the greater the number of “+” marks, the higher the superiority.

  In the case of the driver M1 shown in FIG. 4A, the line-of-sight movement is performed in a time period (see reference numeral Q1) from 6 seconds before the lane change start time to 2 seconds after the lane change execution. It is understood that the superiority of is increasing. That is, for the driver M1, the support process is executed about 6 seconds before the estimated time when the lane change is executed, and the support process is finished 2 seconds after the start of the lane change. It is determined that it is preferable.

  On the other hand, in the case of the driver M2 shown in FIG. 4B, the line-of-sight movement is performed in a time zone (see reference sign Q2) from 2 seconds before the lane change start time to 2 seconds after the lane change execution. It is understood that the superiority of is increasing. That is, for the driver M2, the support process is executed about 2 seconds before the time when the lane change is estimated to be executed, and the support process is finished 2 seconds after the lane change is started. It is determined that it is preferable.

  In the present embodiment, the start time and end time of the support process are set in consideration of this data. By doing so, it is possible to give the driver a sense of security by performing support processing at an early point for a beginner who is immature in driving. Further, it is possible to avoid annoyance caused by executing the support process at an early point for a highly skilled driver.

  The above contents will be described with reference to FIG. A region L3 of points Z1 to Z6 shown in FIG. 3 indicates points where the driver has changed lanes in the past. Then, when it is estimated that the vehicle V1 is traveling on the travel route X1 and the lane is changed at the point Z4, the vehicle V1 is located at a point Z3 slightly ahead of the point Z4 for a highly skilled driver. When it reaches, the surrounding image may be displayed. Further, after the lane change is executed at the point Z4, the display of the surrounding image is ended when the point Z5 is reached. That is, the surrounding image is displayed in the region L2.

  When the driver is a beginner, the surrounding image is displayed at a point Z2 before the point Z3. That is, the surrounding image is displayed in the region L1. In this case, since the surrounding image is displayed at a point a long distance before the point where the lane change is actually performed, it is possible to give the driver a sense of security.

  On the other hand, in the conventional method that does not employ this embodiment, the driver's intention is not detected, and therefore, for example, the support process is executed based on the past driving history. For this reason, for example, when the history of the lane change is the region L3, the surrounding image is displayed while the vehicle V1 is traveling in the region L3. Therefore, when the present embodiment is adopted, the display of the surrounding image is omitted in an unnecessary area, so that the driver does not feel bothered.

  In this way, the support device 100 according to the present embodiment analyzes the driver's brain activity and line of sight, and based on the analysis results and the driver's past operation behavior, the driver's operation intention That is, the intention of changing lanes is detected. Therefore, it is possible to execute the support process related to the driving operation before the lane change. In other words, the timing to change the lane is estimated before the driver actually performs the lane change operation, and processing such as enlarging the surrounding image is executed before the lane change operation is performed. Therefore, it is possible to make settings so that the driver can easily change the lane.

  Further, as the driver's brain activity, the visual processing depth of the driver is analyzed, and the intention of the driver to change the lane is detected based on the analysis result. Therefore, since the driver's intention to change lanes is detected based on the degree of careful recognition of the surroundings, it is possible to execute a support process for recognition of the operating environment with higher accuracy.

  Furthermore, the visual processing average value (VPb) which is the average value of the visual processing depth of the driver is calculated, and further, the visual processing depth (VPn) acquired for the driver and the visual processing average value (VPb). A first difference that is a difference is calculated, and the acquired visual processing depth is analyzed based on the first difference. Therefore, the analysis based on the comparison with the visual processing depth of the driver at the normal time becomes possible, so that it is possible to detect the operation intention more suitable for the characteristics of the individual driver.

  Further, as the driver's line-of-sight analysis, a line-of-sight movement amount per predetermined time is acquired, and the intention of the driver to change the lane is detected based on the line-of-sight movement amount. Therefore, since the driver's intention to change lanes is detected based on the degree of recognition of the surroundings while moving the line of sight, it is possible to execute support processing for recognition of the operation environment with higher accuracy. .

  Further, the driver's eye movement amount average value Sb is calculated, and further, a second difference that is a difference between the eye movement amount Sn acquired for the driver and the eye movement amount average value Sb is calculated. Based on the two differences, the acquired eye movement amount is analyzed. Therefore, the analysis based on the comparison with the amount of movement of the driver's line of sight in the normal time becomes possible, so that it is possible to detect the operation intention more suitable for the characteristics of the individual driver.

  Also, the driver's attention level A1 is calculated by multiplying the first difference and the second difference, and the support process is executed when the attention level A1 exceeds a predetermined threshold value. Therefore, when the driver needs support, the support process can be appropriately executed.

  Further, the driver's past operation behavior is acquired from the driving behavior database 25, and the predetermined threshold is changed based on the operation behavior data. Therefore, it is possible to set a predetermined threshold value suitable for the driver's past operation behavior, and it is possible to determine with high accuracy whether or not the support process can be executed.

  In addition, ambient environment data around the vehicle is acquired from the ambient environment detection unit 26, and the predetermined threshold is changed based on the ambient environment data. Therefore, it is possible to set a predetermined threshold value according to the surrounding environment, for example, the number of other vehicles, and it is possible to determine whether or not the support process can be performed with high accuracy.

  Further, when the driver's past operation behavior data cannot be acquired and the attention degree A1 is larger than the predetermined threshold Lth, a support command is output, so that support processing is not executed when support is required. The occurrence of problems can be avoided.

  Further, the support time required by the driver is calculated from the past operation behavior of the driver. Then, the support process is started at a time point before the above support time from the time point when the driver is assumed to start the lane change. As a result, the support process can be executed at a timing suitable for the driving skill of the driver. That is, since the support process is executed early for beginners, it is possible to give a sense of security, and by omitting unnecessary support processes for the skilled person, it is possible to prevent annoyance from being felt.

  In the above-described embodiment, the operation when the vehicle changes lanes as the operation behavior has been described as an example. However, for example, the operation when the vehicle turns right or left at the intersection can also be employed. Specifically, when it is estimated by the driving intention detection unit 13 that the driver turns left at the next intersection, the left turn operation by the driver is supported by enlarging and displaying the left image of the vehicle. can do.

  That is, since the support process is executed for the driver who drives the vehicle, the surrounding image can be displayed when the driver changes lanes, or when the driver turns right or left, and the surrounding visibility is improved. The driving operation can be executed smoothly.

[Description of modification]
In the above-described embodiment, the vehicle has been described as an example of the operation device. However, the present invention is not limited to a vehicle, and can be applied to other operating devices. Specifically, the present invention can be applied when a player (target person) operates a game machine or when a factory worker performs a flow work on a production line.

  For example, in the case of a game machine, when a player operates the game machine to execute a game, a brain wave detection probe is attached to the player's head. Further, the movement of the player's eyeball is detected using a camera or the like. Then, when the player is playing the game, the player's line-of-sight movement amount and visual processing depth are detected. And from these information, the difficulty level at the time of a player performing a game machine can be recognized. For example, when the movement amount of the line of sight is small and the visual processing depth is small, it is estimated that the player does not feel difficulty in the content of the game.

  In such a case, it is also possible to increase the difficulty level of the game by transmitting this attention degree data to the game machine side. On the other hand, if the movement amount of the player's line of sight is large and the visual processing depth is large, it can be estimated that the player feels difficulty in the content of the game. In such a case, the difficulty level of the game is reduced.

  That is, the support control unit outputs a support command for an operation action when the operation intention detection unit detects that the player (target person) has an operation intention. Further, the difficulty level of the game machine is changed based on at least one of the brain activity analyzed by the brain activity analysis unit and the gaze movement amount analyzed by the gaze analysis unit. That is, since it is possible to detect whether the difficulty level felt by the game that the player is currently operating by operating the game machine is difficult or simple, the difficulty level of the game is changed accordingly. That is, the difficulty level can be set according to the player's game level. As a result, it is possible to increase the player's willingness to execute the game, and to execute the game while having more fun.

  In the support device according to the present invention, it is also possible to execute support processing for an operator (target person) who operates the processing of the article by the manufacturing apparatus for manufacturing the article. It is possible to detect the movement amount of the worker's line of sight and the visual processing depth, and to recognize the magnitude of the burden when performing the work based on the information. And when an operator's eyes | visual_axis movement amount and visual processing depth are large, it judges that the speed of a processing process is quick and can reduce the speed of a processing process. On the other hand, when the movement amount of the operator's line of sight and the visual processing depth are small, the processing speed is changed to a higher speed.

  That is, the support control unit outputs a support command for the operation action when the operation intention detection unit detects that the operation intention by the subject is present. Further, the processing speed of the article in the manufacturing apparatus is changed based on at least one of the brain activity analyzed by the brain activity analysis unit and the gaze movement amount analyzed by the gaze analysis unit.

  Thus, by measuring the worker's line-of-sight movement amount and visual processing depth, it is possible to recognize the ease of work by the worker, so by changing the speed of the flow work, It is possible to set a flow velocity suitable for the capacity and improve work efficiency.

  The support device and the support method of the present invention have been described based on the illustrated embodiment. However, the present invention is not limited to this, and the configuration of each unit is an arbitrary configuration having the same function. Can be replaced.

DESCRIPTION OF SYMBOLS 11 Gaze analysis part 12 Brain activity analysis part 13 Driving intention detection part 21 Gaze movement detection part 22 Gaze movement amount database 23 Brain activity detection part 24 Brain activity database 25 Driving action database 26 Ambient environment detection part 27 Environment judgment control part (support control) Part)
100 Support equipment

Claims (15)

In the support device that supports the operation of the operating device by the target person,
A brain activity analysis unit for analyzing the brain activity of the subject;
A line-of-sight analysis unit for analyzing the line of sight of the subject;
Based on the subject's brain activity analyzed by the brain activity analysis unit, the gaze movement amount of the subject analyzed by the gaze analysis unit, and past operation behavior of the subject, An operation intention detection unit for detecting an operation intention;
A support control unit that outputs a support command for the operation behavior when the operation intention detection unit detects that the target person has an operation intention;
A support device comprising: The brain activity analysis unit analyzes the visual processing depth of the target person based on the brain activity of the target person, and the operation intention detection unit determines the operation intention of the target person based on the analysis result of the visual processing depth. The support device according to claim 1, wherein the support device is detected. A brain activity data storage unit for storing data relating to the visual processing depth of the subject;
The brain activity analysis unit calculates a visual processing average value that is an average value of the visual processing depth of the subject from the data stored in the brain activity data storage unit, and further, the visual processing depth of the subject is calculated. The support according to claim 2, wherein when acquired, the visual processing depth is analyzed based on a first difference that is a difference between the acquired visual processing depth and the average value of the visual processing. apparatus. The gaze analysis unit acquires a gaze movement amount per predetermined time of the subject, and the operation intention detection unit detects the operation intention of the subject based on the gaze movement amount. The support apparatus of any one of 1-3. A visual line movement amount storage unit for storing the visual line movement amount of the subject,
The line-of-sight analysis unit calculates an average line-of-sight movement amount that is an average value of the line-of-sight movement amount of the subject from the data stored in the line-of-sight movement storage unit, and further obtains the amount of line-of-sight movement of the subject 5. The line of sight of the subject is analyzed based on a second difference that is a difference between the acquired line-of-sight movement amount and the average line-of-sight movement amount. Support device. A brain activity data storage unit that stores data related to the visual processing depth of the subject, and a gaze movement amount storage unit that stores the gaze movement amount of the subject,
The brain activity analysis unit calculates a visual processing average value that is an average value of the visual processing depth of the subject from the data stored in the brain activity data storage unit, and further, the visual processing depth of the subject is calculated. When acquired, a first difference that is a difference between the acquired visual processing depth and the average value of the visual processing is calculated,
and,
The line-of-sight analysis unit calculates an average line-of-sight movement amount that is an average value of the line-of-sight movement amount of the subject from the data stored in the line-of-sight movement storage unit, and further obtains the amount of line-of-sight movement of the subject When this is done, a second difference that is a difference between the acquired line-of-sight movement amount and the average line-of-sight movement amount is calculated,
The operation intention detection unit determines that there is an operation intention by a target person when a degree of attention obtained from the first difference and the second difference is larger than a predetermined threshold value set in advance. Item 6. The support device according to any one of Items 1 to 5. The support device according to claim 6, wherein the operation intention detection unit acquires operation behavior data of the subject in the past and changes the predetermined threshold based on the operation behavior data. The support device according to claim 6, wherein the operation intention detection unit acquires ambient environment data, and changes the predetermined threshold based on the ambient environment data. The said support control part outputs the said support instruction | indication, when the said operation degree data cannot be acquired in the past of the said subject and the said attention level is larger than the said predetermined threshold value. 9. The support device according to any one of 8 above. An operation behavior data storage unit that stores past operation behavior of the subject is further provided,
The operation intention detection unit calculates a support time required by the subject based on the past operation behavior of the subject stored in the operation behavior data storage unit,
The said support control part outputs the said support instruction | indication in the time before the said support time from the time estimated that a subject person starts operation, The any one of Claims 1-9 characterized by the above-mentioned. The support device described.   The support device according to claim 1, wherein the operating device is a vehicle.   The support device according to claim 11, wherein the operation by the subject is a lane change of a vehicle, or a right turn or a left turn operation at an intersection. The operating device is a game machine,
The support control unit, when the operation intention detection unit detects that there is an operation intention by the subject, outputs a support command for the operation action,
The difficulty level of the game machine is changed based on at least one of the brain activity analyzed by the brain activity analysis unit and the gaze movement amount analyzed by the gaze analysis unit. The support device according to any one of 10 to 10. The operating device is a manufacturing apparatus that manufactures an article, and the target person is an operator who operates the processing of the article in the manufacturing apparatus,
The support control unit, when the operation intention detection unit detects that there is an operation intention by the subject, outputs a support command for the operation action,
The processing speed of the article in the manufacturing apparatus is changed based on at least one of the brain activity analyzed by the brain activity analysis unit and the line-of-sight movement amount analyzed by the line-of-sight analysis unit. Item 11. The support device according to any one of Items 1 to 10. In the support method that supports the operation of the operation device by the target person,
Analyzing the brain activity of the subject;
Analyzing the line of sight of the subject;
Detecting the operation intention of the subject based on the brain activity of the subject, the amount of eye movement of the subject, and the past operation behavior of the subject;
A step of outputting a support command for the operation action when it is detected that the target person has an operation intention;
A support method characterized by comprising:

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