JP2021180716A - Information processing device, specification method and program - Google Patents

Abstract

To provide an information processing device capable of specifying a type of a nervous disease, a specification method and a program.SOLUTION: According to an embodiment of the present invention, an information processing device includes an acquisition part and a specification part. The acquisition part is configured to be able to acquire the movement of the visual line of a subject reading a document, and the specification part is configured to be able specify which case the acquired movement of the visual line corresponds to in visual line motion data, and the visual line motion data is provided by storing the movement of the visual line of the subject reading the document in each of a plurality of cases.SELECTED DRAWING: Figure 1

Description

The present invention relates to an information processing device, a specific method and a program.

There are many methods for a patient to identify the presence or absence of a neurological disease or the type of a neurological disease, such as head MRI.

Patent Document 1 discloses a method of presenting a visual stimulus to a patient from a display unit and determining whether or not the patient has a neurological disease based on the information of the patient's gaze point to the stimulus.

Japanese Patent Application No. 2016-552133

By the way, it is desired that the type of neurological disease can be easily identified.

In view of the above circumstances, the present invention has decided to provide an information processing device, a specific method and a program capable of specifying the type of neurological disease.

According to one aspect of the present invention, the information processing apparatus includes an acquisition unit and a specific unit, and the acquisition unit is configured to be able to acquire the movement of the line of sight of a subject reading a document. The specific unit is configured to be able to identify which case the acquired gaze movement corresponds to in the gaze movement data, and the gaze movement data is the gaze of a person when reading a document. The movement is memorized for each of a plurality of cases, and the thing is provided.

With such an information processing device, a patient with a neurological disorder can be easily identified.

It is a schematic diagram of the system 100 including the information processing apparatus 3 and the apparatus associated with it. It is a block diagram which shows the hardware composition of an information processing apparatus 3. It is a functional block diagram which shows the function which the control unit 33 in an information processing apparatus 3 has. This is an example of the line-of-sight motion 6 and the line-of-sight motion data G acquired from the line-of-sight movement of the subject 2. This is an example of the test document D1 read by the subject 2. It is the result of the control experiment using the test document D1. FIG. 6A is a diagram showing a control image DC which is a control group. FIG. 6B is a diagram showing a multiple system atrophy image DM, which is an experimental group. It is a feature quantity list F which describes the feature quantity FQ and the feature quantity FT which specify a neurological disorder. This is an example of the first line-of-sight data GD1. This is an example of the second line-of-sight data GD2. This is an example of pretreatment used in a method for identifying a neurological disorder. This is an example of the test document D2 read by the subject 2. It is a figure which shows the test result of a healthy person and multiple system atrophy. FIG. 12A is a diagram showing a control image EC of a healthy person. FIG. 12B is a diagram showing a multiple system atrophy image EM. It is a figure which shows the test result of Parkinson's disease and progressive supranuclear palsy. FIG. 13A is a diagram showing the Parkinson's disease image ED. FIG. 13B is a diagram showing a progressive supranuclear palsy image ES. It is an activity diagram which shows an example of the method of identifying a neurological disorder which concerns on embodiment. It is a sub-activity diagram which shows an example of the method of identifying a neurological disorder which concerns on embodiment. FIG. 15A is a sub-activity diagram of the data acquisition method (A01) relating to the movement of the line of sight. FIG. 15B is a sub-activity diagram of the feature acquisition method (A02).

Hereinafter, embodiments of the present invention will be described with reference to the drawings. The various features shown in the embodiments shown below can be combined with each other.

By the way, the program for realizing the software appearing in the present embodiment may be provided as a non-temporary recording medium readable by a computer, may be provided as a downloadable form from an external server, or may be provided. It may be provided to start the program on an external computer and realize the function on the client terminal (so-called cloud computing).

Further, in the present embodiment, the "part" may include, for example, a combination of hardware resources implemented by a circuit in a broad sense and information processing of software specifically realized by these hardware resources. .. In addition, various information is handled in this embodiment, and these information are, for example, physical values of signal values representing voltage and current, and signal values as a bit aggregate of a binary number composed of 0 or 1. It is represented by high-low or quantum superposition (so-called qubit), and communication / operation can be executed on a circuit in a broad sense.

Further, the circuit in a broad sense is a circuit realized by at least appropriately combining a circuit, a circuit, a processor, a memory, and the like. That is, an integrated circuit for a specific application (Application Specific Integrated Circuit: ASIC), a programmable logic device (for example, a simple programmable logic device (Simple Programmable Logic Device: SPLD), a composite programmable logic device (Complex Programg)). It includes a programmable gate array (Field Programmable Gate Array: FPGA) and the like.

1. 1. Overall Configuration This chapter describes the information processing device according to this embodiment. FIG. 1 is a schematic diagram of a system 100 including an information processing apparatus 3 and an associated apparatus thereof.

As shown in FIG. 1, the information processing device 3 is used together with the line-of-sight measuring device 4 and the microphone 5. The line-of-sight measuring device 4 and the microphone 5 may be external devices or may be built in the information processing device 3. Using these devices, the movement of the line of sight of the subject 2 is analyzed to perform a neurological disorder test. Targets of neurological disorders include, but are not limited to, Parkinson's disease, multiple system atrophy / spinocerebellar degeneration, progressive supranuclear palsy, and corticobasal syndrome. Hereinafter, the components of the information processing apparatus 3 will be described in more detail.

FIG. 2 is a block diagram showing a hardware configuration of the information processing apparatus 3. FIG. 3 is a functional block diagram showing a function carried out by the control unit 33 in the information processing apparatus 3. The information processing device 3 has a communication unit 31, a storage unit 32, a control unit 33, a display unit 34, and an input unit 35, and these components provide a communication bus 30 inside the information processing device 3. It is electrically connected via. Hereinafter, each component will be further described.

(Communication unit 31)
Although wired communication means such as USB, IEEE1394, Thunderbolt, and wired LAN network communication are preferable, the communication unit 31 can perform wireless LAN network communication, mobile communication such as LTE / 3G, Bluetooth (registered trademark) communication, and the like as necessary. May be included. That is, it is more preferable to carry out as a set of these plurality of communication means. In particular, it is preferable that the external line-of-sight measuring device 4 and the microphone 5 are configured to be communicable according to a predetermined communication standard.

(Memory unit 32)
The storage unit 32 stores various information defined by the above description. This is, for example, as a storage device such as a solid state drive (SSD), or a random access memory (Random Access Memory:) that stores temporarily necessary information (arguments, arrays, etc.) related to program operations. It can be implemented as a memory such as RAM). Further, these combinations may be used. In particular, the storage unit 32 stores the data detected by the line-of-sight measuring device 4 and the microphone 5. Further, the storage unit 32 stores an acquisition program, a specific program, a feature setting program, a feature acquisition program, a time measurement program, a generation program, a conversion program, a preprocessing program, a result storage program, and a display control program. In addition to this, the storage unit 32 stores various programs related to the information processing device 3 executed by the control unit 33.

The storage unit 32 stores the line-of-sight motion data G. Here, the line-of-sight movement data G stores the movement of the line-of-sight of a person when reading a document for each of a plurality of cases. In other words, the line-of-sight motion data G is obtained by extracting coordinate points from the data for tracking the line-of-sight movement, and is related data including the line-of-sight movement classified for each case. Gaze movement data G is collected from healthy subjects and patients with neurological disorders. The line-of-sight motion data G may be data received from the line-of-sight measuring device 4 via the communication unit 31, or may be data stored in a storage medium acquired by another device. FIG. 4 is an example of the line-of-sight motion 6 and the line-of-sight motion data G acquired from the line-of-sight movement of the subject 2. The line-of-sight motion 6 tracks the viewpoint when the subject 2 is looking at the display unit 34. The line-of-sight motion 6 has a gazing point 61 and a connecting line 62. The gazing point 61 is a point at which the line of sight is stopped for a predetermined time or longer while the subject 2 is reading a document, the center coordinates of a region where the line of sight is stopped for a predetermined time or longer, or coordinates corresponding to the line of sight at predetermined time intervals. be. The connecting line 62 is a line connecting two continuous gazing points 61. In other words, the connecting line 62 is a locus of the subject 2 moving his / her line of sight. In FIG. 4, the line-of-sight motion 6 being inspected is displayed on the display unit 34, but it may not be displayed.

No. included in the line-of-sight motion data G is an order in which the gazing points 61 are arranged in chronological order. X and Y included in the line-of-sight motion data G are coordinate points on the display unit 34 of the gazing point 61. Here, the coordinate system may be an image coordinate system or a physical coordinate system. The coordinate system is not limited.

(Control unit 33)
The control unit 33 processes and controls the overall operation related to the information processing device 3. The control unit 33 is, for example, a central processing unit (CPU) (not shown). The control unit 33 realizes various functions related to the information processing device 3 by reading out a predetermined program stored in the storage unit 32. Specifically, it corresponds to an acquisition function, a specific function, a feature setting function, a feature acquisition function, a time measurement function, a generation function, a conversion function, a preprocessing function, a result storage function, and a display control function. That is, the information processing by the software (stored in the storage unit 32) is specifically realized by the hardware (control unit 33), so that the acquisition unit 331, the specific unit 332, the feature setting unit 333, and the feature acquisition unit are realized. It can be executed as 334, time measurement unit 335, generation unit 336, conversion unit 337, preprocessing unit 338, result storage unit 339, and display control unit 33a. Although it is described as a single control unit 33 in FIG. 2, it is not limited to this, and it may be implemented so as to have a plurality of control units 33 for each function. Further, it may be a combination thereof.

(Display unit 34)
The display unit 34 may be included in the housing of the information processing apparatus 3, or may be externally attached, for example. The display unit 34 displays a screen of a graphical user interface (GUI) that can be operated by the user. For example, it is preferable to use display devices such as a CRT display, a liquid crystal display, an organic EL display, and a plasma display properly according to the type of the information processing device 3. The display device can selectively display the display screen in response to the control signal of the display control unit 33a in the control unit 33. For example, the test document D1, the inspection result (not shown), and the like can be visually displayed by the user.

(Input unit 35)
The input unit 35 may be included in the housing of the information processing device 3 or may be externally attached. For example, the input unit 35 may be implemented as a touch panel integrally with the display unit 34. If it is a touch panel, the user can input a tap operation, a swipe operation, and the like. Of course, instead of the touch panel, a switch button, a mouse, a QWERTY keyboard, or the like may be adopted. That is, the input unit 35 receives the operation input made by the user. Further, the input unit 35 includes a line-of-sight measuring device 4 and a microphone 5 built in or externally attached to the housing of the information processing device 3. With these devices, the user can input the movement of the line of sight and the voice of the subject 2 into the information processing device 3. The input is transferred as a signal to the control unit 33 via the communication bus 30, and the control unit 33 can execute predetermined control or calculation as needed.

2. 2. Functional configuration This chapter describes the functional configuration of this embodiment. As shown in FIG. 3, the control unit 33 includes an acquisition unit 331, a specific unit 332, a feature setting unit 333, a feature acquisition unit 334, a time measurement unit 335, a generation unit 336, and a conversion unit 337. A pre-processing unit 338, a result storage unit 339, and a display control unit 33a are provided. Hereinafter, each component will be further described.

(Acquisition unit 331)
In the acquisition unit 331, information processing by software (stored in the storage unit 32) is specifically realized by hardware (control unit 33). The acquisition unit 331 is configured to acquire the electrical data detected by the line-of-sight measuring device 4 and the microphone 5 via the communication unit 31.

The acquisition unit 331 is configured to be able to acquire the movement of the line of sight of the subject 2 who is reading the document. The movement of the line of sight is a movement caused by the eye movement of the subject 2 when the subject 2 is reading a document. FIG. 5 is an example of the test document D1 read by the subject 2. There are various methods for detecting eye movements, but in the present embodiment, the line-of-sight measuring device 4 tracks the viewpoint that the subject 2 is gazing at, and the acquisition unit 331 converts the result into electrical data. get. Further, the acquisition unit 331 may be configured to be able to acquire the voice uttered by the subject 2.

(Specific part 332)
The specific unit 332 is configured to be able to identify which case the acquired movement of the line of sight corresponds to in the line-of-sight motion data G. Here, the line-of-sight motion data G is a memory of the line-of-sight movement of a person when reading a document for each of a plurality of cases. FIG. 6 shows the results of a control experiment using the test document D1. be. FIG. 6A is a diagram showing a control image DC which is a control group. FIG. 6B is a diagram showing a multiple system atrophy image DM, which is an experimental group. As shown in FIGS. 6A and 6B, different characteristics are observed in the gaze movement 6 of a healthy person and the gaze movement 6 of a patient with multiple system atrophy. Similarly, there is an inherent tendency in gaze movement 6 of other neurologically ill patients. Based on such a difference in tendency, the identification unit 332 can identify whether the subject 2 is a healthy person or a neurological disease patient. Furthermore, it is possible to identify what kind of neurological disease the subject 2 is suffering from.

(Characteristic setting unit 333)
The feature setting unit 333 is configured to be able to set the feature FT for each of a plurality of cases corresponding to the line-of-sight motion data G. FIG. 7 is a feature amount list F in which the feature amount FQ and the feature amount FT for specifying a neurological disease are described. These feature quantities FQ are examples, and are not limited to those shown in FIG. 7. As described in the feature amount list F, the feature FT is the number of gaze points 61 obtained from the feature amount FQ, the number of connecting lines 62, and the like. Further, the feature FT also includes a composite number generated by combining a plurality of these feature quantities FQ. For example, the composite number includes the value obtained by multiplying the number of the gazing points 61 by the average value of the lengths of the connecting lines 62. The feature setting unit 333 sets such a feature FT for each case of a neurological disorder.

(Characteristic acquisition unit 334)
The feature acquisition unit 334 is configured to be able to acquire the feature FT from the acquired movement of the line of sight of the subject 2. In order to identify whether or not the subject 2 has a neurological disorder and, if so, the type of the neurological disorder, the feature acquisition unit 334 moves the line of sight of the subject 2. The feature FT is obtained from. The specific unit 332 is configured to be able to identify which case of the line-of-sight motion data G the acquired line-of-sight movement corresponds to based on the feature FT acquired from the subject 2 and the set feature FT. Will be done. When the characteristic FT of the subject 2 belongs to the specimen of the characteristic FT of the healthy subject, the subject 2 is identified as a healthy subject. On the other hand, if the characteristic FT of the subject 2 belongs to, for example, a specimen of the characteristic FT of multiple system atrophy, the subject 2 is identified as having multiple system atrophy.

(Time measurement unit 335)
In order to improve the accuracy of identifying the presence or absence of a neurological disorder or the type of a neurological disorder in the subject 2, a larger feature amount FQ is required. Therefore, the time measuring unit 335 is configured to be able to measure the time while the subject 2 is reading the document. It is useful to measure the time while the subject 2 is reading the document when the time of gazing at the characters and the speed of movement of the line of sight that moves the line of sight tend to be peculiar to the neurological disorder.

(Generation unit 336)
The generation unit 336 is composed of various feature FTs acquired by the feature acquisition unit 334, for example, the movement of the line of sight of the subject 2, the measurement time during the examination, and the characters of the test document D1 read by the subject 2. Generate the following data. Hereinafter, each secondary data will be described.

FIG. 8 is an example of the first line-of-sight data GD1. The generation unit 336 is configured to be able to generate the first line-of-sight data GD1 in which the acquired movement of the line of sight and the measured time are associated with each other. In order to calculate the time when the subject 2 is gazing at a specific character, the speed of the line-of-sight movement 6 between the specific characters, and the like, the acquired movement of the line of sight is associated with the measured time. Similarly, in the line-of-sight movement data G of the neurological disorder patient, the line-of-sight movement is associated with the measured time. In other words, the gaze movement data G includes the time while a person including the subject 2 or the patient with a neurological disorder was reading the document. Further, in the present embodiment, the line-of-sight motion data G may be the line-of-sight movement when a person reads the document aloud.

The first line-of-sight data GD1 includes a start time, an end time, a gaze time, and a movement time. The start time is the time when the line of sight of the subject 2 starts to gaze at the gaze point 61. The end time is the time when the line of sight of the subject 2 finishes gazing at the gazing point 61. The gaze time is the time from the start time to the end time. The movement time is the time from the end time of one gazing point 61 to the start time of the next gazing point 61. Here, the unit of time included in the first line-of-sight data GD1 is seconds. The unit of time is not limited to this.

FIG. 9 is an example of the second line-of-sight data GD2. The generation unit 336 is configured to be able to generate the second line-of-sight data GD2 in which the acquired line-of-sight movement and the converted text data are associated with each other. While the subject 2 is reading a specific character, the tendency of the positional relationship between the character and the line of sight varies depending on the neurological disorder. Here, the positional relationship indicates where the gazing point 61 is located up, down, left, or right with respect to the character being read. Patients with multiple system atrophy / spinocerebellar degeneration tend to be unable to move their gaze to the intended location. Therefore, the second line-of-sight data GD2 is effective for identifying which neurological disease the subject 2 is suffering from.

The phonetic characters described in the second line-of-sight data GD2 are the characters in the test document D1 read by the subject 2. Xc and Yc are the coordinate points of the display unit 34 of the center of gravity of the read character. The distance is the distance from the gazing point 61 to the center of gravity of the read character.

(Conversion unit 337)
The conversion unit 337 is configured to be able to convert the acquired voice into text data. The conversion unit 337 recognizes the voice emitted by the subject 2 and converts the voice into text data. The conversion unit 337 may automatically convert the voice into text data, or the user may convert the voice into text data while listening to the voice of the subject 2. The user can edit the result of converting the voice into text data.

(Pretreatment unit 338)
The pretreatment unit 338 is configured to be able to extract movements that contribute to the feature FT from the acquired movements of the line of sight of the subject 2. The data regarding the movement of the line of sight of the subject 2 also includes the unintended movement of the subject 2. Such data, especially outliers, may reduce the accuracy with which the identification unit 332 identifies the presence or absence of a neurological disorder or the neurological disorder of the subject 2. Therefore, the preprocessing unit 338 removes noise data in advance.

FIG. 10 is an example of pretreatment used in a method for identifying a neurological disorder. As a preprocessing, the end point P1 of the connecting line L1 is at a predetermined distance or more from the center of gravity GC of all the gazing points 61, and each length of the connecting line L1 and the connecting line L2 continuous with the connecting line L1 has a predetermined length. In the above case, the end point P1 may be used as an outlier. At this time, the preprocessing unit 338 deletes the connection line L1 and the connection line L2 connected to the end point P1 and sets the connection line M1 instead. In this way, the endpoint P1 which is an outlier is deleted, but the method is not limited to this.

(Result storage unit 339)
In the result storage unit 339, information processing by software (stored in the storage unit 32) is specifically realized by hardware (control unit 33). The result storage unit 339 stores data related to the use of the information processing device 3 in the storage unit 32. For example, the result storage unit 339 stores the line-of-sight motion data G of the subject 2, the acquired data regarding the feature FT, and the test result in the storage unit 32.

(Display control unit 33a)
In the display control unit 33a, information processing by software (stored in the storage unit 32) is specifically realized by hardware (control unit 33). The display control unit 33a causes the display unit 34 to display the test document D1. The display control unit 33a causes the display unit 34 to display the line-of-sight motion data G, the inspection result (not shown), and the like while the subject 2 is using the information processing device 3. The line-of-sight motion data G can be displayed on the display unit 34 not only during the inspection but also after the inspection.

3. 3. Examination This chapter describes an examination to identify which case is applicable based on the movement of the line of sight obtained from the subject 2.

3.1 Preparation before examination The user executes the result storage unit 339 and stores the line-of-sight movement data G of a plurality of patients with neurological disorders in the storage unit 32 before the examination of the subject 2. The line-of-sight movement data G is a memory of the line-of-sight movement when a patient with a neurological disorder is reading the test document D2 for each case. Similarly, the result storage unit 339 also stores the line-of-sight motion data G of a healthy person in the storage unit 32. Further, the user may execute the time measurement unit 335 and the generation unit 336 to associate the time while reading the test document D2 with the movement of the line of sight, and execute the acquisition unit 331 and the conversion unit 337 to read aloud. The text data obtained by converting the acquired voice may be associated with the text data.

3.2 Inspection method The subject 2 reads the document displayed on the display unit 34. FIG. 11 is an example of the test document D2 read by the subject 2. In this way, the test document D2 may be a document in which "aiueo" are simply arranged in the order of the Japanese syllabary. The size and color of the characters described in the test document D2 are arbitrary, and the background color of the test document D2 is preferably different from the color of the characters. The document may include a horizontal document and a vertical document. For example, patients with progressive supranuclear palsy often have difficulty moving their gaze up and down since the early stages of the disease. Therefore, by using the horizontal writing document and the vertical writing document for the inspection, it is possible to easily identify the patient who has difficulty in moving the line of sight in the vertical direction.

The characters of the test document D2 are not limited to Japanese, but may be English or French. Language does not matter. Further, the test document D2 may include numbers and symbols. During the inspection, the display control unit 33a does not need to display the test document D2 in full text on the display unit 34, and may partially display the test document D2.

When the subject 2 is made to read the test document D2, either reading aloud or silent reading may be used. It should be noted that, in reading aloud, the load on the brain is increased by the process of emitting a voice in addition to the visual inspection, so that the characteristic FT is considered to appear prominently. Further, when reading aloud, it is possible to acquire data related to voice in addition to the line of sight. For example, the subject 2 may be made to read the horizontally written document three times and then read the vertically written document three times. Then, the gaze point 61 and the connecting line 62 when the test document D2 is read may be combined for each of the horizontal writing document and the vertical writing document. This is an example, and the same document may be read multiple times, or different documents may be read multiple times.

3.3 Examination Results Here, the movement of the line of sight of the subject 2 acquired by the acquisition unit 331 while the subject 2 is reading the test document D2 three times will be described. FIG. 12 is a diagram showing the test results of a healthy person and multiple system atrophy when the vertical writing document shown in FIG. 11 is read. FIG. 12A is a diagram showing a control image EC of a healthy person. FIG. 12B is a diagram showing a multiple system atrophy image EM. The tendency of gaze movement 6 in patients with multiple system atrophy is that the gliding eye movement becomes impulsive or the gaze cannot be moved to the intended place. Therefore, the number of gaze points 61 in patients with multiple system atrophy is larger than the number of gaze points 61 in healthy subjects. The impulsive gliding eye movements of patients with multiple system atrophy are reflected in the multiple system atrophy image EM. In addition, the distance shown in FIG. 9 tends to be larger in the line-of-sight movement 6 of the multiple system atrophy patient than in the line-of-sight movement 6 of the healthy person. It is reflected in the multiple system atrophy image EM that it is difficult for patients with multiple system atrophy to move their gaze to the intended place.

FIG. 13 is an image showing the test results of Parkinson's disease and progressive supranuclear palsy when the vertical document shown in FIG. 11 is read. FIG. 13A is a diagram showing the Parkinson's disease image ED. As a tendency of the line-of-sight movement 6 of Parkinson's disease patients, there is a symptom that the movement of the eyeball (eg, sliding movement, impulsive movement) progresses as the disease progresses. Therefore, the line-of-sight movement 6 of the Parkinson's disease patient tends to have a larger distance shown in FIG. 9 than the line-of-sight movement 6 of a healthy person. FIG. 13B is a diagram showing a progressive supranuclear palsy image ES. As a tendency of gaze movement 6 in patients with progressive supranuclear palsy, there is a symptom that it becomes difficult to move the gaze in the vertical (up and down) direction from the early stage of the disease. Therefore, despite the vertical writing of the test document D2, the line-of-sight movement 6 of progressive supranuclear palsy does not form a uniform trajectory in the vertical direction and diverges in all directions.

4. Explanation of Identification of Neurological Disease A method for identifying whether the subject 2 is a healthy person or a patient with a neurological disease and specifying the type of the neurological disease will be described below. Characteristics of eye movements in healthy subjects and patients with neurological disorders FT differs from each other. In addition, the characteristic FT tends to be common to the types of healthy subjects and patients with neurological disorders. Therefore, the specimens of characteristic FT for each type of healthy subjects and patients with neurological disorders follow a specific probability distribution. Therefore, the above-mentioned feature FT is calculated from the movement of the line of sight of the subject 2, and the specific unit 332 specifies to which feature FT the probability distribution the calculated feature FT belongs based on a statistical method.

As shown in FIGS. 12 and 13, the images of the test results tend to be unique to each case of neurological disorder. Therefore, the specific unit 332 may specify the type of neurological disease by pattern matching by image processing. In this case, the image corresponding to the line-of-sight movement 6 of the subject 2 and the image representing a plurality of cases among the line-of-sight movement data G stored in advance are collated by pattern matching to match the line-of-sight of the subject 2. It is possible to identify which case the exercise 6 corresponds to.

It should be noted that deep learning may be used to identify whether the subject 2 is a healthy person or a neurological disease patient, and to specify the type of neurological disease. When deep learning is used, the feature quantity FQ and the feature FT can be learned from the line-of-sight motion data G by performing learning using a large-scale labeled data and the structure of the neural network. The data to be input may be eye movement data G, or may be data such as control image EC and multiple system atrophy image EM.

5. Specific Method This chapter describes a method for identifying a neurological disease using the information processing apparatus 3 described above. The specific method includes an acquisition step and a specific step. In the acquisition step, the movement of the line of sight of the subject 2 reading the test document D1 is acquired. In the specific step, it is specified which case the acquired gaze movement corresponds to in the gaze movement data G. The line-of-sight movement data G stores the movement of the line-of-sight of a person when reading a document for each of a plurality of cases. Specifically, this specific method will be described.

5.1 Method for identifying neurological disorders using test document D1 This section describes an example of a method for identifying neurological disorders using test document D1. FIG. 14 is an activity diagram showing an example of a method for identifying a neurological disorder according to an embodiment.

[from here]
(Activity A01)
In order to inspect the subject 2, the acquisition unit 331 acquires data regarding the movement of the line of sight of the subject 2.
(Activity A02)
The feature acquisition unit 334 acquires the feature FT from the acquired movement of the line of sight of the subject 2.
(Activity A03)
In the specific unit 332, the movement of the line of sight acquired based on the feature FT acquired from the subject 2 and the feature FT preset for the neurological disorder patient is any of the gaze movement data G of the neurological disorder patient. Identify if this is the case.
(Activity A04)
The display control unit 33a displays the inspection result on the display unit 34.
(Activity A05)
The result storage unit 339 stores the inspection result in the storage unit 32.
[to this point]

5.2 Acquisition method of data related to eye movement This section describes an example of acquisition method of eye movement data. FIG. 15 is a sub-activity diagram showing an example of a method for identifying a neurological disorder according to an embodiment. FIG. 15A is a sub-activity diagram showing an example of a method (A01) for acquiring data related to the movement of the line of sight.

[from here]
(Sub-activity A11)
The acquisition unit 331 starts acquiring data regarding the movement of the line of sight of the subject 2.
(Sub-activity A12)
The acquisition unit 331 acquires the movement of the line of sight of the subject 2 reading the test document D1.
(Sub-activity A13)
The time measuring unit 335 measures the time while the subject 2 is reading the test document D1.
(Sub-activity A14)
The acquisition unit 331 acquires the voice emitted by the subject 2.
(Sub-activity A15)
The conversion unit 337 converts the voice into text data.
(Sub-activity A16)
The acquisition unit 331 ends the acquisition of the movement data of the line of sight of the subject 2.
[to this point]

5.3 Acquisition method of feature FT This section describes an example of acquisition method of feature FT. FIG. 15B is a sub-activity diagram showing an example of the feature FT acquisition method (A02).

[from here]
(Sub-activity A21)
The feature setting unit 333 starts to acquire the feature FT of the movement of the line of sight of the subject 2.
(Sub-activity A22)
The generation unit 336 generates the first line-of-sight data GD1 in which the acquired movement of the line-of-sight of the subject 2 and the measured time are associated with each other.
(Sub-activity A23)
The generation unit 336 generates the second line-of-sight data GD2 in which the acquired movement of the line-of-sight of the subject 2 and the converted text data are associated with each other.
(Sub-activity A24)
The pre-processing unit 338 executes pre-processing for extracting the movement that contributes to the feature FT from the acquired movement of the line of sight of the subject 2.
(Sub-activity A25)
The feature setting unit 333 acquires the feature FT from the line-of-sight motion data G, the first line-of-sight data GD1 and the second line-of-sight data GD2, and ends the process.
[to this point]

The method for identifying a neurological disorder using the test document D1 has a shorter examination time and is cheaper than the head MRI method, the cerebral blood flow SPECT method, the MIBG myocardial scintigraphy method, and the DAT scan method. By performing the double task of reading aloud and reading aloud during the examination, the load on the brain of the patient with a neurological disorder increases, and the symptoms become prominent. Further, by acquiring the voice while reading the test document D1, it is possible to grasp how far the character being read aloud with respect to the line of sight is advanced. Since there is a difference in the position of the line of sight and the text being read aloud for each symptom of the neurological disorder, the accuracy of identifying the case is high.

As the neurological disease progresses, the line-of-sight movement 6 of the subject 2 also changes, so that the progress of the disease can be grasped. For example, since the movement of the eyeball of a patient with Parkinson's disease (eg, sliding movement, impulsive movement) progresses as the disease progresses, the progress can be grasped by this specific method. On the contrary, when the neurological disease is cured, the recovery status can be grasped by this specific method. When the user identifies the neurological disorder of the subject 2 using the same test document D1, the test results show reproducibility. On the other hand, by changing the content of the document of the test document D1, the size of the text, and the like for each inspection, the subject 2 is inspected regardless of the experience of the inspection in the past. Therefore, this identification method does not have a learning effect.

6. Modification example This chapter describes a modification example related to the information processing apparatus 3. That is, the above-described embodiment may be implemented according to the following aspects.

(1) The specific characters of the test document D1 and the test document D2 may be colored differently from other characters. Reading a specific character increases the load on the brain, and the load is reflected in the line-of-sight movement 6 of the subject 2.
(2) The user may point to a specific character of the test document D1 and the test document D2 with a pointer, and the subject 2 may track and read aloud when the color of the specific character changes. The load on a specific area of the brain due to reading a specific character is reflected in the line-of-sight movement 6 of the subject 2.
(3) A program may be provided that causes the computer to function as the information processing device 3.

7. Conclusion In this way, by silently reading or reading aloud the test document, it is possible to provide an information processing device capable of sufficiently identifying a patient with a neurological disorder having a speech disorder or a sliding eye movement disorder. Furthermore, by conducting continuous examinations, the progress of neurological disorders can be grasped. It can also be used to determine the administration of therapeutic agents.

It may be provided in each of the following embodiments.
The information processing apparatus further includes a feature setting unit, which is configured to be capable of setting features for each of a plurality of cases corresponding to the line-of-sight motion data.
The information processing apparatus further includes a feature acquisition unit, the feature acquisition unit is configured to be able to acquire features from the acquired movement of the subject's line of sight, and the specific unit is configured to acquire the acquired features and the settings. It is configured so that it is possible to identify which case the acquired line-of-sight movement corresponds to in the line-of-sight movement data based on the characteristics obtained.
In the information processing apparatus, the document includes a horizontal writing document and a vertical writing document.
The information processing apparatus further includes a time measuring unit and a generation unit, and the time measuring unit is configured to be capable of measuring the time while the subject is reading the document. A device capable of generating first line-of-sight data in which the acquired movement of the line of sight and the measured time are linked.
The information processing apparatus further includes a conversion unit, the acquisition unit being configured to be able to acquire the voice uttered by the subject, and the conversion unit being configured to be able to convert the voice into text data. The generation unit is configured to be capable of generating second line-of-sight data in which the acquired movement of the line of sight and the converted text data are associated with each other.
In the information processing apparatus, the line-of-sight motion data includes a time while a person is reading the document.
In the information processing apparatus, the line-of-sight motion data is the movement of the line-of-sight when a person reads the document aloud.
The information processing apparatus includes a feature acquisition unit and a preprocessing unit, the feature acquisition unit is configured to be able to acquire features from the acquired movement of the subject's line of sight, and the preprocessing unit is configured to be capable of acquiring features. A device that can extract movements that contribute to the characteristics from the acquired movements of the subject's line of sight.
It is a specific method and includes an acquisition step and a specific step. In the acquisition step, the movement of the line of sight of the subject reading the document is acquired, and in the specific step, the movement of the acquired line of sight is obtained. A method of identifying which case corresponds to the line-of-sight movement data, and the line-of-sight movement data stores the movement of the line of sight of a person when reading a document for each of a plurality of cases.
A program that causes a computer to function as each part of the information processing device.
Of course, this is not the case.

Finally, various embodiments of the present invention have been described, but these are presented as examples and are not intended to limit the scope of the invention. The novel embodiment can be implemented in various other embodiments, and various omissions, replacements, and changes can be made without departing from the gist of the invention. The embodiment and its modifications are included in the scope and gist of the invention, and are included in the scope of the invention described in the claims and the equivalent scope thereof.

100: System 2: Subject 3: Information processing device 30: Communication bus 31: Communication unit 32: Storage unit 33: Control unit 331: Acquisition unit 332: Specific unit 333: Feature setting unit 334: Feature acquisition unit 335: Time Measurement unit 336: Generation unit 337: Conversion unit 338: Preprocessing unit 339: Result storage unit 33a: Display control unit 34: Display unit 35: Input unit 4: Line-of-sight measurement device 5: Microphone 6: Line-of-sight motion 61: Gaze point 62 : Connection line D1: Test document D2: Test document DC: Control image DM: Multiple system atrophy image EC: Control image ED: Parkinson's disease image EM: Multiple system atrophy image ES: Progressive supranuclear palsy image F: Features Amount list FQ: Feature amount FT: Feature G: Line-of-sight motion data GC: Center of gravity GD1: First line-of-sight data GD2: Second line-of-sight data L1: Connection line L2: Connection line M1: Connection line P1: End point

Claims (11)

It is an information processing device
It has an acquisition part and a specific part,
The acquisition unit is configured to be able to acquire the movement of the line of sight of the subject reading the document.
The specific unit is configured to be able to identify which case the acquired gaze movement corresponds to in the gaze movement data.
The line-of-sight movement data stores the movement of the line-of-sight of a person while reading a document for each of a plurality of cases.
thing. In the information processing apparatus according to claim 1,
Further equipped with a feature setting unit
The feature setting unit is configured to be able to set features for each of a plurality of cases corresponding to the line-of-sight motion data.
thing. In the information processing apparatus according to claim 2,
Equipped with a feature acquisition unit
The feature acquisition unit is configured to be able to acquire features from the acquired movement of the subject's line of sight.
The specific unit is configured to be able to identify which case the acquired line-of-sight movement corresponds to in the line-of-sight movement data based on the acquired features and the set features.
thing. The information processing apparatus according to any one of claims 1 to 3.
The document includes a horizontal document and a vertical document.
thing. The information processing apparatus according to any one of claims 1 to 4.
Further equipped with a time measurement unit and a generation unit,
The time measuring unit is configured to be able to measure the time while the subject is reading the document.
The generation unit is configured to be able to generate first line-of-sight data in which the acquired movement of the line of sight is associated with the measured time.
thing. In the information processing apparatus according to claim 5,
With more converters
The acquisition unit is configured to be able to acquire the voice uttered by the subject.
The conversion unit is configured to be able to convert the voice into text data.
The generation unit is configured to be capable of generating second line-of-sight data in which the acquired movement of the line of sight and the converted text data are associated with each other.
thing. The information processing apparatus according to any one of claims 1 to 6.
The line-of-sight data includes the time while the person was reading the document.
thing. The information processing apparatus according to any one of claims 1 to 7.
The line-of-sight movement data is the movement of the line of sight when a person reads the document aloud.
thing. The information processing apparatus according to any one of claims 1 to 8.
It has a feature acquisition unit and a pretreatment unit.
The feature acquisition unit is configured to be able to acquire features from the acquired movement of the subject's line of sight.
The pretreatment unit is configured to be able to extract movements that contribute to the characteristics from the acquired movements of the subject's line of sight.
thing. It is a specific method, and has an acquisition step and a specific step.
In the acquisition step, the movement of the line of sight of the subject reading the document is acquired.
In the specific step, it is specified which case the acquired gaze movement corresponds to in the gaze movement data.
The line-of-sight movement data stores the movement of the line-of-sight of a person while reading a document for each of a plurality of cases.
Method. It ’s a program,
A computer that functions as each part of the information processing apparatus according to any one of claims 1 to 9.

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