JP6330638B2 - Training support apparatus and program - Google Patents

Description

  The present invention relates to a training support apparatus and a training support method.

  People with developmental disabilities often have a characteristic that the point of gaze is biased or causality is difficult to grasp. Knowing where to obtain information to understand causality and whether you can not understand causality despite glance is important in providing training for people with developmental disabilities It becomes a point.

JP 2011-206542 A

Pierce K et al., “Preference for Geometric Patterns Early in Life as a Risk Factor for Autism.”, Arch Gen Psychiatry. 2011 Jan; 68 (1): 101-109.

  However, in the conventional method, it was impossible to know where to obtain information to understand the causal relationship, or whether the causal relationship cannot be understood despite the attention. Conventionally, treatment or training is performed using illustrations and photographs, but it is unknown where the subject is looking, it is impossible to provide quantitative feedback, and effective training is I could not say.

  The present invention has been made in view of the above, and an object thereof is to provide a training support apparatus and a training support method that can support training more effectively.

  In order to solve the above-described problems and achieve the object, the present invention represents a display unit, a diagnostic image representing a cause of a predetermined event and the event, and a causal relationship between the cause and the event. An output control unit for displaying an explanation image on the display unit.

  The training support device and the training support method according to the present invention have an effect that training can be supported more effectively.

FIG. 1 is a diagram illustrating an example of the arrangement of a display unit, a stereo camera, an infrared light source, and a subject according to the present embodiment. FIG. 2 is a diagram illustrating an example of the arrangement of the display unit, the stereo camera, the infrared light source, and the subject according to the present embodiment. FIG. 3 is a diagram illustrating an outline of functions of the diagnosis support apparatus. FIG. 4 is a block diagram illustrating an example of detailed functions of each unit illustrated in FIG. 3. FIG. 5 is a diagram for explaining an overview of processing executed by the diagnosis support apparatus of this embodiment. FIG. 6 is an explanatory diagram showing the difference between the method using two light sources and the present embodiment using one light source. FIG. 7 is a diagram for explaining calculation processing for calculating the distance between the pupil center position and the corneal curvature center position. FIG. 8 is a flowchart illustrating an example of calculation processing according to the present embodiment. FIG. 9 is a diagram showing a method of calculating the position of the corneal curvature center using the distance obtained in advance. FIG. 10 is a flowchart illustrating an example of a line-of-sight detection process according to the present embodiment. FIG. 11 is a diagram for explaining the calculation process of the first modification. FIG. 12 is a flowchart illustrating an example of calculation processing according to the first modification. FIG. 13 is a diagram illustrating an example of a diagnostic image used in the present embodiment. FIG. 14 is a diagram illustrating an example of a diagnostic image used in the present embodiment. FIG. 15 is a diagram illustrating an example of a diagnostic image used in the present embodiment. FIG. 16 is a diagram illustrating an example of a diagnostic image used in the present embodiment. FIG. 17 is a flowchart illustrating an example of the diagnosis support process of the present embodiment. FIG. 18 is a diagram illustrating an example of a selection screen for selecting a primary answer. FIG. 19 is a diagram illustrating an example of a correct answer screen for displaying a correct answer. FIG. 20 is a diagram illustrating an example of the explanation screen. FIG. 21 is a flowchart illustrating an example of a gazing point detection process. FIG. 22 is a flowchart illustrating an example of analysis processing. FIG. 23 is a diagram illustrating an example of the causal relationship. FIG. 24 is a flowchart illustrating an example of a verification process for verifying and displaying the effect of medical treatment. FIG. 25 is a diagram for explaining an example of a method for determining changes in measurement data. FIG. 26 is a flowchart illustrating an example of analysis processing during moving image display. FIG. 27 is a flowchart illustrating an example of training support processing according to the second modification. FIG. 28 is a diagram illustrating an example of a menu screen according to the second modification. FIG. 29 is a diagram illustrating an example of the still image 1 displayed in the second modification. FIG. 30 is a diagram illustrating an example of the still image 2 displayed in the second modification. FIG. 31 is a diagram illustrating an example of a selection screen according to the second modification. FIG. 32 is a diagram illustrating an example of a correct answer screen according to the second modification. FIG. 33 is a diagram illustrating an example of the explanation screen of the second modification. FIG. 34 is a diagram illustrating an example of a playback screen according to the second modification. FIG. 35 is a diagram for explaining an example in which a training support apparatus is realized by a notebook PC.

  Hereinafter, embodiments of a training support apparatus and a training support method according to the present invention will be described in detail with reference to the drawings. In addition, this invention is not limited by this embodiment. In the following, an example will be described in which the training support device is applied to a diagnosis support device that supports the diagnosis of developmental disabilities and the like using the line-of-sight detection results, and can also be used for training. It is not something that can be done.

  The diagnosis support apparatus according to the present embodiment displays images (videos) showing before and after a certain event, measures the stop time at the position of the gazing point, and performs an evaluation calculation. Further, a continuous moving image including a scene representing the cause and a scene representing the front and back of the event is displayed as an explanatory image representing the causal relationship between the cause and the event. This realizes efficient training support that is easy for the educated person to understand.

  In addition, the diagnosis support apparatus according to the present embodiment detects a line of sight using an illumination unit installed at one location. Note that the diagnosis support apparatus is not limited to the above-described embodiment. In addition, the diagnosis support apparatus according to the present embodiment calculates the corneal curvature center position with high accuracy by using a result obtained by gazing at one point on the subject before the line-of-sight detection.

  In addition, an illumination part is an element which can irradiate light to a test subject's eyeball including a light source. The light source is an element that generates light, such as an LED (Light Emitting Diode). A light source may be comprised from one LED, and may be comprised by combining several LED and arrange | positioning in one place. Hereinafter, the “light source” may be used as a term representing the illumination unit in this way.

  1 and 2 are diagrams illustrating an example of the arrangement of a display unit, a stereo camera, an infrared light source, and a subject according to the present embodiment.

  As illustrated in FIG. 1, the diagnosis support apparatus 100 according to the present embodiment includes a display unit 101, a stereo camera 102 corresponding to an imaging unit, and an LED light source 103. The stereo camera 102 is disposed below the display unit 101. The LED light source 103 is disposed at the center position of two cameras included in the stereo camera 102. The LED light source 103 is a light source that irradiates near infrared rays having a wavelength of 850 nm, for example. FIG. 1 shows an example in which the LED light source 103 (illumination unit) is configured by nine LEDs. The stereo camera 102 uses a lens that can transmit near-infrared light having a wavelength of 850 nm.

  As shown in FIG. 2, the stereo camera 102 includes a right camera 202 and a left camera 203. The LED light source 103 irradiates near-infrared light toward the eyeball 111 of the subject. In the image acquired by the stereo camera 102, the pupil 112 is reflected and darkened with low brightness, and the corneal reflection 113 generated as a virtual image in the eyeball 111 is reflected and brightened with high brightness. Accordingly, the positions of the pupil 112 and the corneal reflection 113 on the image can be acquired by each of the two cameras (the right camera 202 and the left camera 203).

  Further, the three-dimensional world coordinate values of the positions of the pupil 112 and the corneal reflection 113 are calculated from the positions of the pupil 112 and the corneal reflection 113 obtained by the two cameras. In the present embodiment, as the three-dimensional world coordinates, the center position of the screen of the display unit 101 is set as the origin, the top and bottom are Y coordinates (up is +), the side is X coordinates (right is +), and the depth is Z coordinates ( The front is +).

  FIG. 3 is a diagram illustrating an outline of functions of the diagnosis support apparatus 100. FIG. 3 shows a part of the configuration shown in FIGS. 1 and 2 and a configuration used for driving the configuration. As illustrated in FIG. 3, the diagnosis support apparatus 100 includes a right camera 202, a left camera 203, an LED light source 103, a speaker 205, a drive / IF (interface) unit 313, a control unit 300, and a storage unit 150. And the display unit 101. In FIG. 3, the display screen 201 shows the positional relationship between the right camera 202 and the left camera 203 in an easy-to-understand manner, but the display screen 201 is a screen displayed on the display unit 101. The drive unit and the IF unit may be integrated or separate.

  The speaker 205 functions as an audio output unit that outputs an audio or the like for alerting the subject during calibration.

  The drive / IF unit 313 drives each unit included in the stereo camera 102. The drive / IF unit 313 serves as an interface between each unit included in the stereo camera 102 and the control unit 300.

  The control unit 300 is, for example, a communication I / F that communicates with a control device such as a CPU (Central Processing Unit) and a storage device such as a ROM (Read Only Memory) and a RAM (Random Access Memory) by connecting to a network. And a computer equipped with a bus for connecting each unit.

  The storage unit 150 stores various information such as a control program, a measurement result, and a diagnosis support result. The storage unit 150 stores, for example, an image to be displayed on the display unit 101. The display unit 101 displays various information such as a target image for diagnosis.

  FIG. 4 is a block diagram illustrating an example of detailed functions of each unit illustrated in FIG. 3. As shown in FIG. 4, the display unit 101 and the drive / IF unit 313 are connected to the control unit 300. The drive / IF unit 313 includes camera IFs 314 and 315, an LED drive control unit 316, and a speaker drive unit 322.

  The right camera 202 and the left camera 203 are connected to the drive / IF unit 313 via the camera IFs 314 and 315, respectively. The driving / IF unit 313 drives these cameras to image the subject.

  The speaker driving unit 322 drives the speaker 205. The diagnosis support apparatus 100 may include an interface (printer IF) for connecting to a printer as a printing unit. In addition, the printer may be provided inside the diagnosis support apparatus 100.

  The control unit 300 controls the entire diagnosis support apparatus 100. The control unit 300 includes a first calculation unit 351, a second calculation unit 352, a third calculation unit 353, a gaze detection unit 354, a viewpoint detection unit 355, an output control unit 356, and an evaluation unit 357. I have. Note that the gaze detection support apparatus that detects the gaze only needs to include at least the first calculation unit 351, the second calculation unit 352, the third calculation unit 353, and the gaze detection unit 354.

  Each element included in the control unit 300 (the first calculation unit 351, the second calculation unit 352, the third calculation unit 353, the line-of-sight detection unit 354, the viewpoint detection unit 355, the output control unit 356, and the evaluation unit 357) It may be realized by software (program), may be realized by a hardware circuit, or may be realized by using software and a hardware circuit in combination.

  When implemented by a program, the program is a file in an installable or executable format, such as a CD-ROM (Compact Disk Read Only Memory), a flexible disk (FD), a CD-R (Compact Disk Recordable), a DVD ( Digital Versatile Disk) is recorded on a computer-readable recording medium and provided as a computer program product. The program may be provided by being stored on a computer connected to a network such as the Internet and downloaded via the network. The program may be provided or distributed via a network such as the Internet. The program may be provided by being incorporated in advance in a ROM or the like.

  The first calculator 351 calculates the position (first position) of the pupil center indicating the center of the pupil from the eyeball image captured by the stereo camera 102. The second calculator 352 calculates the position of the corneal reflection center (second position) indicating the center of corneal reflection from the captured image of the eyeball. The first calculation unit 351 and the second calculation unit 352 correspond to a position detection unit that detects a first position indicating the center of the pupil and a second position indicating the center of corneal reflection.

  The third calculator 353 calculates the corneal curvature center (fourth position) from the straight line (first straight line) connecting the LED light source 103 and the corneal reflection center. For example, the third calculation unit 353 calculates a position on the straight line where the distance from the corneal reflection center is a predetermined value as the corneal curvature center. As the predetermined value, a value determined in advance from a general radius of curvature of the cornea or the like can be used.

  Since there may be individual differences in the radius of curvature of the cornea, there is a possibility that an error will increase if the center of corneal curvature is calculated using a predetermined value. Therefore, the third calculation unit 353 may calculate the corneal curvature center in consideration of individual differences. In this case, the third calculation unit 353 first uses a pupil center and a corneal reflection center that are calculated when the subject gazes at the target position (third position), and a straight line connecting the pupil center and the target position (the first position). 2), and the intersection of the corneal reflection center and the LED light source 103 (first straight line) is calculated. The third calculation unit 353 calculates a distance (first distance) between the pupil center and the calculated intersection, and stores the calculated distance in the storage unit 150, for example.

  The target position may be a position that is determined in advance and can calculate a three-dimensional world coordinate value. For example, the center position of the display screen 201 (the origin of the three-dimensional world coordinates) can be set as the target position. In this case, for example, the output control unit 356 displays an image (target image) or the like that causes the subject to gaze at the target position (center) on the display screen 201. Thereby, a test subject can be made to gaze at a target position.

  The target image may be any image as long as it can attract the attention of the subject. For example, an image in which a display mode such as luminance or color changes, an image in which the display mode is different from other regions, or the like can be used as the target image.

  The target position is not limited to the center of the display screen 201, and may be an arbitrary position. If the center of the display screen 201 is set as the target position, the distance from an arbitrary end of the display screen 201 is minimized. For this reason, it becomes possible to make the measurement error at the time of gaze detection smaller, for example.

  The processing up to the calculation of the distance is executed in advance, for example, before the actual gaze detection is started. At the time of actual line-of-sight detection, the third calculation unit 353 calculates, on the straight line connecting the LED light source 103 and the corneal reflection center, a position where the distance from the pupil center is a previously calculated distance as the corneal curvature center. . The third calculation unit 353 determines the center of corneal curvature from the position of the LED light source 103, the predetermined position (third position) indicating the target image on the display unit 101, the position of the pupil center, and the position of the corneal reflection center. This corresponds to a calculation unit that calculates (fourth position).

  The gaze detection unit 354 detects the gaze of the subject from the pupil center and the corneal curvature center. For example, the gaze detection unit 354 detects the direction from the corneal curvature center to the pupil center as the gaze direction of the subject.

  The viewpoint detection unit 355 detects the viewpoint of the subject using the detected gaze direction. The viewpoint detection unit 355 detects, for example, a viewpoint (gaze point) that is a point on the display screen 201 where the subject gazes. The viewpoint detection unit 355 detects, for example, the intersection of the line-of-sight vector represented in the three-dimensional world coordinate system as shown in FIG. 2 and the XY plane as the gaze point of the subject.

  The output control unit 356 controls the output of various information to the display unit 101, the speaker 205, and the like. For example, the output control unit 356 causes the target image to be output to the target position on the display unit 101. Further, the output control unit 356 controls the output to the display unit 101 such as a diagnostic image and an evaluation result by the evaluation unit 357.

  The diagnostic image may be an image corresponding to the evaluation process based on the line-of-sight (viewpoint) detection result. For example, in the case of diagnosing a developmental disorder, a diagnostic image including an image (such as a geometric pattern image) preferred by a subject with a developmental disorder and other images (such as a person image) may be used.

  The evaluation unit 357 performs an evaluation process based on the diagnostic image and the gazing point detected by the viewpoint detection unit 355. For example, in the case of diagnosing a developmental disorder, the evaluation unit 357 analyzes the diagnostic image and the gazing point, and evaluates whether or not the image preferred by the subject with the developmental disorder has been gazed. For example, the evaluation unit 357 calculates an evaluation value based on the position of the gaze point of the subject when the diagnostic images as illustrated in FIGS. 13 and 16 described later are displayed. A specific example of the evaluation value calculation method will be described later. The evaluation unit 357 may calculate the evaluation value based on the diagnostic image and the gazing point, and the calculation method is not limited to the present embodiment.

  FIG. 5 is a diagram illustrating an outline of processing executed by the diagnosis support apparatus 100 of the present embodiment. The elements described in FIGS. 1 to 4 are denoted by the same reference numerals and description thereof is omitted.

  The pupil center 407 and the corneal reflection center 408 respectively represent the center of the pupil and the center of the corneal reflection point detected when the LED light source 103 is turned on. The corneal curvature radius 409 represents the distance from the corneal surface to the corneal curvature center 410.

  FIG. 6 is an explanatory diagram showing a difference between a method using two light sources (illuminating units) (hereinafter referred to as method A) and the present embodiment using one light source (illuminating unit). The elements described in FIGS. 1 to 4 are denoted by the same reference numerals and description thereof is omitted.

  Method A uses two LED light sources 511 and 512 instead of the LED light source 103. In the method A, a straight line 515 connecting the cornea reflection center 513 and the LED light source 511 when the LED light source 511 is irradiated, and a straight line 516 connecting the cornea reflection center 514 and the LED light source 512 when the LED light source 512 is irradiated. An intersection is calculated. This intersection is the corneal curvature center 505.

  In contrast, in the present embodiment, a straight line 523 connecting the cornea reflection center 522 and the LED light source 103 when the LED light source 103 is irradiated is considered. A straight line 523 passes through the corneal curvature center 505. It is also known that the radius of curvature of the cornea is almost constant with little influence from individual differences. From this, the corneal curvature center when the LED light source 103 is irradiated exists on the straight line 523, and can be calculated by using a general curvature radius value.

  However, if the viewpoint is calculated using the position of the center of corneal curvature calculated using a general radius of curvature, the viewpoint position may deviate from the original position due to individual differences in the eyeballs, and accurate viewpoint position detection cannot be performed. There is.

  FIG. 7 is a diagram for explaining calculation processing for calculating the corneal curvature center position and the distance between the pupil center position and the corneal curvature center position before performing viewpoint detection (line-of-sight detection). The elements described in FIGS. 1 to 4 are denoted by the same reference numerals and description thereof is omitted.

  The target position 605 is a position for displaying a target image or the like at one point on the display unit 101 and causing the subject to stare. In this embodiment, the center position of the screen of the display unit 101 is set. A straight line 613 is a straight line connecting the LED light source 103 and the corneal reflection center 612. A straight line 614 is a straight line connecting the target position 605 (gaze point) that the subject looks at and the pupil center 611. A corneal curvature center 615 is an intersection of the straight line 613 and the straight line 614. The third calculation unit 353 calculates and stores the distance 616 between the pupil center 611 and the corneal curvature center 615.

  FIG. 8 is a flowchart illustrating an example of calculation processing according to the present embodiment.

  First, the output control unit 356 reproduces the target image at one point on the screen of the display unit 101 (step S101), and causes the subject to gaze at the one point. Next, the control unit 300 turns on the LED light source 103 toward the eyes of the subject using the LED drive control unit 316 (step S102). The control unit 300 images the eyes of the subject with the left and right cameras (the right camera 202 and the left camera 203) (step S103).

  The pupil part is detected as a dark part (dark pupil) by irradiation of the LED light source 103. Further, a corneal reflection virtual image is generated as a reflection of LED irradiation, and a corneal reflection point (corneal reflection center) is detected as a bright portion. That is, the first calculation unit 351 detects a pupil portion from the captured image, and calculates coordinates indicating the position of the pupil center. For example, the first calculation unit 351 detects, as a pupil part, an area having a predetermined brightness or less including the darkest part in a certain area including the eyes, and reflects an area having a predetermined brightness or more including the brightest part to the cornea. Detect as. The second calculation unit 352 detects a corneal reflection portion from the captured image and calculates coordinates indicating the position of the corneal reflection center. The first calculation unit 351 and the second calculation unit 352 calculate each coordinate value for each of the two images acquired by the left and right cameras (step S104).

  Note that the left and right cameras are pre-calibrated with a stereo calibration method to obtain three-dimensional world coordinates, and conversion parameters are calculated. As the stereo calibration method, any conventionally used method such as a method using Tsai's camera calibration theory can be applied.

  The first calculation unit 351 and the second calculation unit 352 convert the coordinates of the left and right cameras into the three-dimensional world coordinates of the pupil center and the corneal reflection center using the conversion parameters (step S105). The 3rd calculation part 353 calculates | requires the straight line which connects the world coordinate of the calculated | required corneal reflection center, and the world coordinate of the center position of the LED light source 103 (step S106). Next, the third calculation unit 353 calculates a straight line connecting the world coordinates of the center of the target image displayed at one point on the screen of the display unit 101 and the world coordinates of the pupil center (step S107). The third calculation unit 353 obtains an intersection point between the straight line calculated in step S106 and the straight line calculated in step S107, and sets this intersection point as the corneal curvature center (step S108). The third calculation unit 353 calculates the distance between the pupil center and the corneal curvature center at this time and stores it in the storage unit 150 or the like (step S109). The stored distance is used to calculate the corneal curvature center at the time of subsequent detection of the viewpoint (line of sight).

  The distance between the pupil center and the corneal curvature center when looking at one point on the display unit 101 in the calculation process is kept constant within a range in which the viewpoint in the display unit 101 is detected. The distance between the center of the pupil and the center of corneal curvature may be obtained from the average of all the values calculated during playback of the target image, or from the average of several values of the values calculated during playback. You may ask for it.

  FIG. 9 is a diagram illustrating a method of calculating the corrected position of the corneal curvature center using the distance between the pupil center and the corneal curvature center that is obtained in advance when performing viewpoint detection. A gazing point 805 represents a gazing point obtained from a corneal curvature center calculated using a general curvature radius value. A gazing point 806 represents a gazing point obtained from a corneal curvature center calculated using a distance obtained in advance.

  The pupil center 811 and the corneal reflection center 812 indicate the position of the pupil center and the position of the corneal reflection center calculated at the time of viewpoint detection, respectively. A straight line 813 is a straight line connecting the LED light source 103 and the corneal reflection center 812. The corneal curvature center 814 is the position of the corneal curvature center calculated from a general curvature radius value. The distance 815 is the distance between the pupil center and the corneal curvature center calculated by the prior calculation process. The corneal curvature center 816 is the position of the corneal curvature center calculated using the distance obtained in advance. The corneal curvature center 816 is obtained from the fact that the corneal curvature center exists on the straight line 813 and the distance between the pupil center and the corneal curvature center is the distance 815. Thus, the line of sight 817 calculated when a general radius of curvature value is used is corrected to the line of sight 818. Also, the gazing point on the screen of the display unit 101 is corrected from the gazing point 805 to the gazing point 806.

  FIG. 10 is a flowchart illustrating an example of a line-of-sight detection process according to the present embodiment. For example, the line-of-sight detection process of FIG. 10 can be executed as the process of detecting the line of sight in the diagnostic process using the diagnostic image. In the diagnostic process, in addition to the steps in FIG. 10, a process for displaying a diagnostic image, an evaluation process by the evaluation unit 357 using the detection result of the gazing point, and the like are executed.

  Steps S201 to S205 are the same as steps S102 to S106 in FIG.

  The third calculation unit 353 calculates, as the corneal curvature center, a position on the straight line calculated in step S205 and whose distance from the pupil center is equal to the distance obtained by the previous calculation process (step S206).

  The line-of-sight detection unit 354 determines a vector (line-of-sight vector) connecting the pupil center and the corneal curvature center (step S207). This vector indicates the line-of-sight direction viewed by the subject. The viewpoint detection unit 355 calculates the three-dimensional world coordinate value of the intersection between the line-of-sight direction and the screen of the display unit 101 (step S208). This value is a coordinate value representing one point on the display unit 101 that the subject gazes in world coordinates. The viewpoint detection unit 355 converts the obtained three-dimensional world coordinate value into a coordinate value (x, y) represented in the two-dimensional coordinate system of the display unit 101 (step S209). Thereby, the viewpoint (gaze point) on the display part 101 which a test subject looks at can be calculated.

(Modification 1)
The calculation process for calculating the distance between the pupil center position and the corneal curvature center position is not limited to the method described with reference to FIGS. Hereinafter, another example of the calculation process will be described with reference to FIGS. 11 and 12.

  FIG. 11 is a diagram for explaining the calculation process of the present modification. The elements described in FIGS. 1 to 4 and 7 are denoted by the same reference numerals and description thereof is omitted.

  A line segment 1101 is a line segment (first line segment) connecting the target position 605 and the LED light source 103. A line segment 1102 is a line segment (second line segment) that is parallel to the line segment 1101 and connects the pupil center 611 and the straight line 613. In this modification, the distance 616 between the pupil center 611 and the corneal curvature center 615 is calculated and stored using the line segment 1101 and the line segment 1102 as follows.

  FIG. 12 is a flowchart illustrating an example of calculation processing according to this modification.

  Steps S301 to S307 are the same as steps S101 to S107 in FIG.

  The third calculation unit 353 calculates and calculates a line segment (the line segment 1101 in FIG. 11) connecting the center of the target image displayed at one point on the screen of the display unit 101 and the center of the LED light source 103. The length of the line segment (L1101) is calculated (step S308).

  The third calculation unit 353 calculates a line segment (line segment 1102 in FIG. 11) that passes through the pupil center 611 and is parallel to the line segment calculated in step S308, and calculates the length of the calculated line segment (L1102). Is calculated (step S309).

  The third calculation unit 353 has a triangle with the corneal curvature center 615 as the apex and the line segment calculated in step S308 as the lower side, and a triangle with the corneal curvature center 615 as the apex and the line segment calculated in step S309 as the lower side. Is a similar relationship, a distance 616 between the pupil center 611 and the corneal curvature center 615 is calculated (step S310). For example, the third calculation unit 353 causes the ratio of the length of the line segment 1102 to the length of the line segment 1101 and the ratio of the distance 616 to the distance between the target position 605 and the corneal curvature center 615 to be equal. The distance 616 is calculated.

The distance 616 can be calculated by the following equation (1). L614 is the distance from the target position 605 to the pupil center 611.
Distance 616 = (L614 × L1102) / (L1101−L1102) (1)

  The third calculation unit 353 stores the calculated distance 616 in the storage unit 150 or the like (step S311). The stored distance is used to calculate the corneal curvature center at the time of subsequent detection of the viewpoint (line of sight).

  Next, details of the diagnosis support processing will be described. In the present embodiment, an image representing a cause of a predetermined event and the event is used as a diagnostic image. Then, diagnosis support is performed by measuring the stop time of the gazing point for the region set in the diagnosis image. Accordingly, it is possible to support diagnosis such as where to pay attention to obtain information in order to understand the causal relationship, or whether the causal relationship cannot be understood despite the attention. That is, diagnosis support with higher accuracy than before can be performed.

  13 to 16 are diagrams illustrating examples of diagnostic images used in the present embodiment. Each of the diagnostic images in FIGS. 13 to 16 is an example of an image showing one scene included in a series of continuous moving images. The continuous moving image may be a moving image that includes cut cuts (such as screen switching) in the middle.

  FIG. 13 is an image showing a scene where a person walks on a road in front of a fence. Several stones have fallen at the feet. A region is set for this image. In the example of FIG. 13, a region M including a person, a region H including a head, a region C including a stone (an example of a first object) that causes a person to fall, and a person (an example of a second object) A region S including a stone that is not related to a falling event is set. Each of the images in FIGS. 13 to 16 has a coordinate system in which the upper left corner of the image is the origin (0, 0) and the lower right coordinates are (Xmax, Ymax).

  FIG. 14 is an image at the moment when a person trips over a stone in the area C and is about to fall. FIG. 15 is an image at the moment when a person trips over a stone in the region C and falls. FIG. 16 is an image showing a scene after a person trips over a stone in the area C and falls. Regions similar to those in FIG. 13 are set for each of the images in FIGS.

  In this embodiment, as one of the diagnostic images, a still image obtained by capturing a part of a moving image before an event occurs or a still image equivalent to the still image, and a part of the moving image after an event occurs. Use a captured still image or a still image equivalent to this still image.

  In the example of FIGS. 13 to 16, the event is “a person falls”, and still image 1 (FIG. 13) that captures a part of the moving image before this event, and a part of the moving image after this event The still image 2 (FIG. 16) obtained by capturing is used as a diagnostic image. Note that the number of still images is not limited to two, and three or more still images may be used.

  FIG. 17 is a flowchart showing an example of a diagnosis support process when such a diagnostic image is used.

  First, the output control unit 356 displays the still image 1 on the display unit 101. The subject looks at the displayed still image 1. At this time, the viewpoint detection unit 355 detects a gazing point (step S401).

  Next, the output control unit 356 displays the still image 2 on the display unit 101. The subject looks at the displayed still image 2. At this time, the viewpoint detection unit 355 detects a gazing point (step S402). The progress from step S401 to step S402 may be executed in response to pressing of a “next button” (not shown) by the subject or the operator. The display may proceed continuously without an instruction from the subject or the operator.

  Next, the evaluation unit 357 accepts selection of a primary answer by the subject (step S403). FIG. 18 is a diagram illustrating an example of a selection screen for selecting a primary answer. The primary answer is an answer selected after displaying a diagnostic image (still image 1, still image 2). In the example of FIG. 18, the primary answer to the question Q is selected from the answer options A1 to A4. Answer options may include nouns representing at least one of a cause or an event. For example, in FIG. 18, the correct option A3 includes a noun “Ishi” representing the cause and a noun “falling” representing the event. A subject with developmental disabilities often chooses other than A3 because it is difficult to determine the causal relationship. Probabilistically, the correct answer may be selected even if the causal relationship is not understood. For this reason, a single question may not support diagnosis with high accuracy. Therefore, many types of images may be similarly examined. Thereby, the accuracy of diagnosis support can be further improved.

  The evaluation unit 357 acquires position information indicating the position touched by the subject or the operator from the display unit 101 configured as a touch panel, for example, and accepts selection of an option corresponding to the position information. The evaluation unit 357 may accept a primary answer specified by an examinee or an operator using an input device (keyboard or the like) not shown. The selection of the primary answer is not limited to the method using the selection screen as shown in FIG. For example, a method may be used in which the operator explains the question and answer options verbally and causes the subject to select the primary answer verbally.

  Returning to FIG. 17, the output control unit 356 displays, for example, a moving image (such as a series of continuous moving images corresponding to the diagnostic images in FIGS. 13 to 16) corresponding to the diagnostic image (still image 1, still image 2) on the display unit 101. indicate. At this time, the viewpoint detection unit 355 detects a gazing point (step S404).

  Note that details of the gazing point detection process in steps S401, S402, and S404 will be described later.

  Next, the evaluation unit 357 accepts selection of a secondary answer by the subject (step S405). The secondary answer is an answer selected after displaying a moving image corresponding to the diagnostic image. The selection of the secondary answer may be performed by a method similar to the selection of the primary answer, for example. The secondary answer options may be the same as or different from the primary answer options. The secondary answer is selected so that the judgment after viewing still picture 1 and still picture 2 (primary answer) can be compared with the judgment after watching continuous video (secondary answer). It is to do.

  Next, the output control unit 356 displays the correct answer of the question on the display unit 101 (step S406). Further, the output control unit 356 displays the explanation on the display unit 101 (step S407).

  FIG. 19 is a diagram illustrating an example of a correct answer screen for displaying a correct answer. In the example of FIG. 19, the option A3 indicating the correct answer is displayed in a different display mode from the other options. The method of indicating the correct answer is not limited to this. If the options for the primary answer and the options for the secondary answer are different, all the options may be displayed, and the correct answer option may be highlighted.

  For example, when the next button 2001 is pressed on this correct answer screen, an explanation screen for displaying an explanation is displayed. FIG. 20 is a diagram illustrating an example of the explanation screen. By displaying the explanation screen, the subject can understand the causal relationship of the events indicated in the diagnostic image. When the next button 2101 is pressed on the explanation screen, display of the explanation screen is terminated.

  Returning to FIG. 17, the evaluation unit 357 executes analysis processing based on the detected gaze point data (step S <b> 408). Details of the analysis process will be described later. Finally, the output control unit 356 displays the analysis result on the display unit 101 or the like (step S409).

  Next, details of the gazing point detection process will be described. FIG. 21 is a flowchart illustrating an example of a gazing point detection process. FIG. 21 illustrates an example of gaze point detection processing after displaying still image 1 in step S401 in FIG. 17; however, step S402 in FIG. 17 (after displaying still image 2) and step S404 are illustrated. The gazing point detection process (after displaying the moving image) can also be realized by the same procedure.

  First, the output control unit 356 starts reproduction (display) of the diagnostic image (still image 1) (step S501). Next, the output control unit 356 resets a timer for measuring the reproduction time (step S502). Next, the viewpoint detection unit 355 resets counters (counters ST1_M, ST1_H, ST1_C, ST1_S, ST1_OT) that are counted up when each area is watched (step S503).

Counters ST1_M, ST1_H, ST1_C, ST1_S, and ST1_OT are counters when still image 1 (ST1) is displayed. Each counter corresponds to the following area. By counting up each counter, it is possible to measure a dwell time that represents a time during which a gazing point is detected in the corresponding region.
Counter ST1_M: area M
Counter ST1_H: Area H
Counter ST1_C: region C
Counter ST1_S: area S
Counter ST1_OT: Area other than the above

  Next, the viewpoint detection unit 355 detects the gaze point of the subject (step S504). The viewpoint detection unit 355 can detect a gazing point, for example, by the procedure described in FIG. The viewpoint detection unit 355 determines whether the detection of the gazing point has failed (step S505). Gaze point detection fails when an image of the pupil and corneal reflection cannot be obtained due to blinking or the like. Further, when the gazing point does not exist in the display unit 101 (when the subject is looking at something other than the display unit 101), it may be determined as a failure.

  If the detection of the gazing point has failed (step S505: Yes), the process proceeds to step S516. If the detection of the gazing point is successful (step S505: No), the viewpoint detection unit 355 acquires the coordinates of the gazing point (gazing point coordinates) (step S506).

  The viewpoint detection unit 355 determines whether or not the acquired gazing point coordinates are within the region M (person's periphery) (step S507). When in the area M (step S507: Yes), the viewpoint detection unit 355 further determines whether or not the acquired gaze point coordinates are in the area H (around the head) (step S508). When in the area H (step S508: Yes), the viewpoint detection unit 355 increments (counts up) the counter ST1_H (step S510). When not in the area H (step S508: No), the viewpoint detection unit 355 increments (counts up) the counter ST1_M (step S509).

  If not in the region M (step S507: No), the viewpoint detection unit 355 determines whether or not the acquired gaze point coordinates are within the region C (near the object causing the causal relationship) (step S511). ). When in the area C (step S511: Yes), the viewpoint detection unit 355 increments (counts up) the counter ST1_C (step S512).

  When it is not in the area C (step S511: No), the viewpoint detection unit 355 determines whether or not the acquired gaze point coordinates are within the area S (near the object that is not the cause of the causal relationship) (step S513). When in the area S (step S513: Yes), the viewpoint detection unit 355 increments (counts up) the counter ST1_S (step S514).

  If it is not in the area S (step S513: No), since there is no gazing point in the set area, the viewpoint detection unit 355 increments (counts up) the counter ST1_OT (step S515).

  Next, the output control unit 356 confirms completion of the timer that manages the playback time of the video (step S516). If the predetermined time has not elapsed, that is, if the timer has not been completed (step S516: No), the process returns to step S504 and measurement is continued. When the timer is completed (step S516: Yes), the output control unit 356 stops the reproduction of the video (step S517).

The gaze point detection process when displaying the still image 2 (ST2) in step S402 can apply the same procedure as in FIG. 21 by replacing the counter as follows.
Counter ST1_M → Counter ST2_M
Counter ST1_H → Counter ST2_H
Counter ST1_C → Counter ST2_C
Counter ST1_S → Counter ST2_S
Counter ST1_OT → Counter ST2_OT

The gaze point detection process when the moving image (MOV) is displayed in step S404 can apply the same procedure as in FIG. 21 by replacing the counter as follows.
Counter ST1_M → Counter MOV_M
Counter ST1_H → Counter MOV_H
Counter ST1_C → Counter MOV_C
Counter ST1_S → Counter MOV_S
Counter ST1_OT → Counter MOV_OT

  Next, details of the analysis process will be described. FIG. 22 is a flowchart illustrating an example of analysis processing. Note that the analysis processing and evaluation values described below are examples and are not limited thereto. For example, the evaluation value may be changed according to the diagnostic image to be displayed.

  First, the evaluation unit 357 determines whether the selected primary answer is correct (step S601). If the answer is correct (step S601: Yes), the evaluation unit 357 calculates an evaluation value indicating that the causal relationship grasping ability is high (step S602).

  When the primary answer is not correct (step S601: No), or after step S602, the evaluation unit 357 calculates ans1 = ST1_M + ST2_M (step S603). For example, ST1_M represents the value of the counter ST1_M. Similarly, the value of the counter X may be simply expressed as “X”.

  Next, the evaluation unit 357 determines whether or not ans1 is larger than the threshold value k11 (step S604). If larger (step S604: Yes), the evaluation unit 357 calculates an evaluation value indicating that the degree of attention is high with respect to the change in the event (step S605). Ans1 is for indicating the degree to which the gazing point is included in the region M including the person. The evaluation value may be a binary value indicating whether the degree of attention is high or low with respect to a change in the event, or may be a multivalue that changes according to the magnitude of ans1, for example.

  When ans1 is less than or equal to the threshold value k11 (step S604: No), or after step S605, the evaluation unit 357 calculates ans2 = ST1_H + ST2_H (step S606). Next, the evaluation unit 357 determines whether ans2 is larger than the threshold value k12 (step S607). If larger (step S607: Yes), the evaluation unit 357 calculates an evaluation value indicating that the degree of attention is high with respect to the head including the human face and the social development is high (step S608). Ans2 is for indicating the degree to which the gazing point is included in the region H including the head.

  When ans2 is equal to or less than the threshold value k12 (step S607: No), or after step S608, the evaluation unit 357 calculates ans3 = ST1_C + ST2_C (step S609). Next, the evaluation unit 357 determines whether or not ans3 is larger than the threshold value k13 (step S610). If larger (step S610: Yes), the evaluation unit 357 calculates an evaluation value indicating that the ability to predict relevance is high and attention is paid to a target related to the causal relationship (step S611). Ans3 is for indicating the degree to which the gazing point is included in the region C including the stone that causes the person to fall.

  When ans3 is equal to or smaller than the threshold k13 (step S610: No), or after step S611, the evaluation unit 357 calculates ans4 = ST1_M + ST2_M + ST1_C + ST2_C + ST1_S + ST2_S (step S612). Next, the evaluation unit 357 determines whether or not ans4 is greater than the threshold value k14 (step S613). If larger (step S613: Yes), the evaluation unit 357 calculates an evaluation value indicating that the degree of interest is high for various objects and events (step S614). Ans4 is for indicating the degree to which a gazing point is included in a region (region M, region C, region S) including an object such as a person or a stone.

  If ans4 is equal to or less than the threshold value k14 (step S613: No), or after step S614, the analysis process is terminated. As with ans1, ans2, ans3, and ans4 may be binary or multivalued.

  Subjects with developmental disabilities often have difficulty understanding causality. Then, I focused on what caused the cause-and-effect relationship and took the information into the brain, but I couldn't understand the cause-and-effect relationship. It is desirable to change the method of treatment depending on whether the cause-and-effect relationship could not be understood. In particular, an evaluation value (step S602) indicating that the causal relationship grasping ability is high, an evaluation value (step S608) indicating that social development is high, and an evaluation value indicating that the ability to predict relevance is high. If this is not the case (step S611), there is a risk of developmental disability.

  According to the diagnosis support apparatus of this embodiment, when images before and after a certain event (for example, still image 1 and still image 2) are displayed, it is measured which part the subject has seen. For this reason, it is possible to support diagnosis with high accuracy as to whether or not the causal relationship can be understood. In addition, it is possible to determine a treatment policy with reference to the result of analysis (diagnosis).

  FIG. 23 is a diagram illustrating an example of the causal relationship. For the causes described in the left column, the results described in the right column are generated. Instead of the still image 1 and the still image 2, a still image indicating the cause described in FIG. 23 and a still image indicating a result corresponding to the cause may be used. In addition to the example shown in FIG. 23, various diagnostic images representing causes and results can be used. Further, for example, diagnostic images (such as two still images) showing the causal relationship as shown in FIG. 23 may be displayed a plurality of times, and the evaluation results for the plurality of diagnostic images may be integrated. For example, each time a plurality of diagnostic images are displayed, the value of each counter may not be reset, and the addition of the counter values to all the diagnostic images may be continued. In this case, for example, each threshold value used in the analysis processing of FIG. 22 may be changed according to the number or type of a plurality of diagnostic images to be used. Thereby, the accuracy of evaluation can be further increased.

  As shown in FIG. 22, in principle, diagnosis is supported based on the point of gaze when images before and after the event (still image 1, still image 2) are displayed. Did you understand how to display moving images, detect gazing points when displaying moving images, and display explanations (steps S404 to S407 in FIG. 17) by teaching the subject the correct answer and watching the moving images? , Etc. to make it possible to evaluate. Therefore, for example, when the purpose is only diagnosis support, these processes (steps S404 to S407 in FIG. 17) may be omitted.

  For example, if an evaluation value (step S611) indicating whether the ability to predict relevance is high is calculated, it is possible to support diagnosis of understanding of the causal relationship. In this case, it is not necessary to execute the display of the correct answer screen and the selection reception of the primary answer. This is because the evaluation value as in step S611 can be calculated only by the detection result of the gazing point when the diagnostic image is displayed.

  In FIG. 22, each evaluation value is calculated independently. You may comprise so that an evaluation value may be calculated | required combining 2 or more among each conditions of FIG. For example, if the primary answer is correct (step S601: Yes) and ans3 is greater than the threshold value k13 (step S610: Yes), an evaluation value indicating that the ability to predict relevance is high may be calculated. Good.

  The diagnosis support process of FIG. 17 includes an explanation image (moving image) display (step S404), a correct answer display (step S406), and an explanation display (step S407) for explaining the causal relationship. Therefore, diagnosis can be supported and training can be supported. Further, for example, if the process of FIG. 17 is repeated for the same diagnostic image or a plurality of different diagnostic images, more effective training support can be provided.

  FIG. 24 is a flowchart illustrating an example of a verification process for verifying and displaying the effect of medical treatment.

  First, the evaluation unit 357 stores the subject information such as the name of the subject and the measurement date before the measurement, for example, in the storage unit 150 (step S701). Next, a diagnosis support process (measurement) as shown in FIG. 17 is executed (step S702). Next, the evaluation unit 357 determines whether past measurement data of the same subject is stored (step S703). The measurement data is, for example, each counter value (stop time), ans1 to ans4 calculated from the counter value, and part or all of the evaluation value.

  When past measurement data is stored (step S703: Yes), the output control unit 356 displays the past measurement data and information indicating the change of the current measurement data with respect to the past measurement data on the display unit 101. (Step S704).

  FIG. 25 is a diagram for explaining an example of a method for determining changes in measurement data. FIG. 25 shows an example in which the change of the current measurement data with respect to the previous measurement data is determined separately for still images and moving images.

  For example, it is indicated that there is no change in the evaluation value “high ability to grasp causality”. ansn_old (n is 1 to 4) indicates the value of the previous measurement data. ansn_new (n is 1 to 4) indicates the value of the current measurement data. As shown in FIG. 25, the change in the measurement data can be determined by the difference between ansn_new and ansn_old, for example.

  The output control unit 356 displays information (a value indicating a difference) indicating the change in the measurement data determined in this way on the display unit 101, for example. The output control unit 356 may output the measurement data and information indicating the change to another device (an external communication device or a printer connected via a network) instead of the display unit 101.

  Returning to FIG. 24, when past measurement data is not stored (step S703: No), or after the display process of step S704, the output control unit 356 displays the current measurement data on the display unit 101 or the like. (Step S705). When there is past measurement data, the output control unit 356 may simultaneously display the previous measurement data, the current measurement data, and information indicating changes.

  As described above, it is possible to improve the ability to grasp the causal relationship by confirming the diagnosis image several times and to confirm the effect of the training.

Analysis processing may also be executed when a moving image is displayed. FIG. 26 is a flowchart illustrating an example of analysis processing during moving image display. Compared with FIG. 22, in FIG. 26, the calculation methods and threshold values of ans 1 to ans 4 are changed as follows. Since the other processing flow is the same as that shown in FIG.
ans1 = MOV_M
ans2 = MOV_H
ans3 = MOV_C
ans4 = MOV_M + MOV_C + MOV_S
k11 → k21
k12 → k22
k13 → k23
k14 → k24

  With the analysis processing as shown in FIG. 26, it is possible to evaluate improvement in understanding due to viewing of a moving image. Further, for example, the evaluation result of FIG. 22 may be further integrated with the evaluation result of the moving image. Thereby, the accuracy of evaluation can be further increased.

  A training method (nursing policy) recommended according to the diagnosis result may be displayed on the display unit 101 or the like so as to support the determination of the nursing policy. For example, the evaluation unit 357 may compare the measurement data with a predetermined policy decision threshold value, and the output control unit 356 may display different training methods depending on whether the threshold value is smaller than the threshold value or more. . Further, the output control unit 356 varies depending on combinations of values of different measurement data (for example, an evaluation value indicating that the ability to grasp causality is high and an evaluation value indicating that the ability to predict relevance is high). The training method may be displayed. As a training method, there are a training method using the diagnosis support apparatus 100, a training method using illustrations and photographs, and any other training method may be used.

  In the above example, a moving image including the diagnostic images (still image 1 and still image 2) used at the time of diagnosis is used as the explanatory image. However, the explanatory image is not limited to such a moving image. Any image may be used as long as it represents the cause-and-effect relationship between the cause and the event and serves as training support. For example, an explanatory image including one or more still images different from the diagnostic image may be used.

(Modification 2)
In the above embodiment, an example has been described in which a diagnosis support apparatus that supports diagnosis of developmental disabilities is also used as a training support apparatus. For example, as long as the device can display an explanation image, a correct answer, an explanation, and the like, the training support device of the above embodiment can be applied in addition to the diagnosis support device. In this modification, an example in which a mobile terminal such as a tablet, a smartphone, and a notebook PC (personal computer) is used as a training support device will be described. In addition, an information processing apparatus such as a normal personal computer can be applied to the training support apparatus.

  For example, the gaze point detection process executed in steps S401, S402, and S404 of the diagnosis support process of FIG. 17 is mainly used to calculate an evaluation value used for diagnosis support for developmental disabilities. Therefore, when aiming at training support, the gazing point detection process may not be executed. Hereinafter, an example of the training support process that does not include the gazing point detection process will be described. FIG. 27 is a flowchart illustrating an example of training support processing according to the present modification.

  For example, when a training support program is started, the output control unit 356 displays a menu screen (step S901).

  FIG. 28 is a diagram illustrating an example of a menu screen according to the second modification. As shown in FIG. 28, the menu screen includes selection buttons 2801 to 2806 for selecting a problem, and an end button 2811. When one of the selection buttons 2801 to 2806 is pressed, a corresponding problem image is displayed. FIG. 28 shows an example in which six types of problems (problem 1 to problem 6) can be selected. The number of problems and the method for selecting problems are not limited to the example of FIG. When the end button 2811 is pressed, the program ends.

  Returning to FIG. 27, the output control unit 356 determines whether or not the end button 2811 has been pressed (step S902). When the end button 2811 is pressed (step S902: Yes), the output control unit 356 ends the training support process.

  When the end button 2811 has not been pressed (step S902: No), the output control unit 356 determines whether the selection buttons 2801 to 2806 have been pressed (step S903). When the selection buttons 2801 to 2806 are not pressed (step S903: No), the process returns to step S902 and the process is repeated.

  When the selection buttons 2801 to 2806 are pressed (step S903: Yes), the output control unit 356 accepts selection of a problem corresponding to the pressed button among the selection buttons 2801 to 2806 (step S904). The output control unit 356 displays the still image 1 indicating the cause of the event among the images corresponding to the accepted problem (step S905). For example, it is assumed that the user (educated person) presses the selection button 2801 in FIG. FIG. 29 is a diagram illustrating an example of the still image 1 displayed at this time. FIG. 29 shows an example of a still image including a causal object (stone).

  Returning to FIG. 27, after displaying the still image 1 for a predetermined time (for example, 10 seconds), the output control unit 356 displays the still image 2 indicating the event for a predetermined time (for example, 10 seconds) (step S906). The display time of each still image may be the same or different. FIG. 30 is a diagram illustrating an example of the still image 2 displayed at this time. FIG. 30 is an example of a still image showing the result (turned) caused by the causal object (stone).

  Next, the output control unit 356 receives an answer selection (step S907). FIG. 31 is a diagram illustrating an example of a selection screen for selecting an answer. FIG. 31 shows an example of a selection screen including a question Q and answer options A1 to A4 together with two still images (still image 1 and still image 2). The user selects an answer to the question Q from the answer options A1 to A4. The output control unit 356 receives the answer selected in this way by the user.

  Returning to FIG. 27, the output control unit 356 displays the correct answer of the question on the display unit 101 (step S908). The output control unit 356 displays the explanation on the display unit 101 (step S909).

  FIG. 32 is a diagram illustrating an example of a correct answer screen for displaying a correct answer. In the example of FIG. 32, together with the answer result (“I did well”), the option A3 indicating the correct answer is displayed in a display mode (not grayed out) different from the other options. For example, when the next button 2001 is pressed on this correct answer screen, an explanation screen for displaying an explanation is displayed. FIG. 33 is a diagram illustrating an example of an explanation screen. By displaying the explanation screen, the subject can understand the causal relationship of the events indicated in the diagnostic image. When the button 3301 is pressed on the explanation screen, a playback screen for playing back a moving image (answer video) showing an answer is displayed.

  When the button 3301 on the explanation screen is pressed, the output control unit 356 displays a reproduction screen on the display unit 101 (step S910). On the playback screen, for example, a moving image including a process from still image 1 to still image 2 is displayed. FIG. 34 is a diagram illustrating an example of a playback screen. This reproduction screen is an image at a certain point in time of a moving image to be reproduced, and shows an example in which an image including an explanatory text (“stumbling on a stone”) is displayed. By displaying such a moving image, the user can further understand the causal relationship between events.

  When the display of the reproduction screen is finished, the display returns to the menu display (step S901).

  By such processing, it is possible to perform training even with an inexpensive device such as a tablet that is not equipped with a gazing point detection device. However, doctors and the like cannot evaluate or give guidance based on the gaze points during training.

  FIG. 35 is a diagram for explaining an example in which a training support apparatus is realized by a notebook PC. FIG. 35 shows an example in which the still image 1 corresponding to FIG. 29 is displayed on the display (corresponding to the display unit 101) of the notebook PC.

  If the user's answer is correct, points or the like may be given each time the user answers correctly. Thereby, a user's willingness to implement training can be increased and training can be supported more effectively.

As described above, according to the present embodiment, for example, the following effects can be obtained.
(1) Causality can be effectively understood by viewing images for training many times.
(2) The effect of training can be measured.
(3) It is possible to know whether or not the subject has watched something related to the causal relationship.
(4) Nursing points and directions can be set.
(5) Self-analysis is possible.
(6) Can confirm social development.
(7) It is not necessary to arrange the light source (illuminating unit) at two places, and it becomes possible to detect the line of sight with the light source arranged at one place.
(8) Since there is only one light source, the apparatus can be made compact and the cost can be reduced.

DESCRIPTION OF SYMBOLS 100 Diagnosis support apparatus 101 Display part 102 Stereo camera 103 LED light source 150 Storage part 201 Display screen 202 Right camera 203 Left camera 205 Speaker 300 Control part 313 Drive / IF part 316 LED drive control part 322 Speaker drive part 351 1st calculation part 352 Second calculation unit 353 Third calculation unit 354 Gaze detection unit 355 View point detection unit 356 Output control unit 357 Evaluation unit

Claims (12)

A display unit;
An imaging unit for imaging a subject;
From a captured image captured by the imaging unit, a gaze detection unit that detects the gaze direction of the subject,
A viewpoint detection unit that detects the viewpoint of the subject in the display area of the display unit based on the line-of-sight direction;
A diagnostic image representing the cause of the predetermined event and the event; and an output control unit for causing the display unit to display an explanatory image representing the cause-and-effect relationship between the cause and the event;
An evaluation unit that calculates an evaluation value of the subject based on the viewpoint detected by the viewpoint detection unit when the diagnostic image is displayed;
A training support apparatus comprising: The diagnostic image includes a still image representing the cause and a still image representing the event,
The explanatory image is a moving image including a scene representing the cause and a scene representing the event;
The training support apparatus according to claim 1.   The training support apparatus according to claim 1, wherein the output control unit displays the explanation image after the diagnostic image is displayed on the display unit. The output control unit causes the display unit to display a different training method according to the evaluation value;
The training support device according to any one of claims 1 to 3, wherein The output control unit outputs information indicating a change in the evaluation value of the subject based on the evaluation value for the first diagnostic image and the evaluation value for the second diagnostic image displayed before the first diagnostic image. To do,
The training support device according to any one of claims 1 to 4, wherein The output control unit further causes a question regarding the event to be output,
The evaluation unit calculates an evaluation value of the subject based on the viewpoint and an answer to the question;
The training support device according to any one of claims 1 to 5, wherein The evaluation unit is included in the diagnostic image and a first stop time that represents a time during which the viewpoint is detected in a first region including the first target that is the cause among the regions included in the diagnostic image. The evaluation value is calculated based on at least one of a second stop time that represents a time during which the viewpoint is detected in a second region that includes a second target that is a target in which the event occurs. about,
The training support device according to any one of claims 1 to 6, wherein The evaluation unit further calculates the evaluation value based on a third stop time that represents a time during which the viewpoint is detected in a region other than the first region and the second region among regions included in the diagnostic image. To calculate,
The training support apparatus according to claim 7. The output control unit displays a plurality of diagnostic images,
The evaluation unit calculates an evaluation value of the subject based on the viewpoint detected by the viewpoint detection unit when a plurality of diagnostic images are displayed;
The training support device according to any one of claims 1 to 8, wherein An illumination unit including a light source that emits light;
A position detection unit that detects a first position indicating the center of the pupil and a second position indicating the center of corneal reflection from the image of the eyeball of the subject imaged by the imaging unit and irradiated with light; ,
A calculation unit that calculates a fourth position indicating the center of curvature of the cornea based on the position of the light source, the third position on the display unit, the first position, and the second position; Prepared,
The training support apparatus according to claim 1, wherein the line-of-sight detection unit detects the line of sight of the subject based on the first position and the fourth position. The output control unit further outputs a question regarding the event and an answer option for the question including a noun representing at least one of the cause or the event,
The training support device according to any one of claims 1 to 10, wherein Computer
An imaging unit for imaging a subject;
From a captured image captured by the imaging unit, a gaze detection unit that detects a gaze direction of the subject,
A viewpoint detection unit that detects the viewpoint of the subject in the display area of the display unit based on the line-of-sight direction;
A diagnostic image representing the cause of the predetermined event and the event, and an output control unit for displaying an explanation image representing the causal relationship between the cause and the event on the display unit,
And an evaluation unit that calculates an evaluation value of the subject based on the viewpoint detected by the viewpoint detection unit when the diagnostic image is displayed,
Program to function as.