Eye movement information collecting and processing method and device, electronic equipment and storage medium

Abstract

The invention relates to the field of intelligent medical treatment, and provides an eye movement information collecting and processing method and device, electronic equipment and a storage medium, and the method comprises the steps: carrying out eyeball track detection on a user, and obtaining track detection data; calculating an error rate of the trajectory detection data and calculating a shake rate of the trajectory detection data; if the error rate and the shake rate of the track detection data meet the set requirements, eye movement information is obtained through calculation based on the track detection data, and the eye movement information is used for detecting mental diseases of the user. In the prior art, there is no method for collecting eye movement information and carrying out refined data processing on the eye movement information, the scheme of the invention can improve the accuracy of the obtained eye movement information.

Description

Technical Field

[0001] The present invention relates to the field of smart medical technology, and in particular to a method, device, electronic device and storage medium for collecting and processing eye movement information. Background Art

[0002] Common diagnostic methods for mental illnesses such as schizophrenia, depression, and Alzheimer's disease rely on clinical presentation and user self-reports, which are often subjective and susceptible to bias.

[0003] Eye tracking technology has demonstrated significant diagnostic potential in the fields of psychology and neurobiology, particularly in the study of attention deficits and abnormal visual information processing. As an auxiliary diagnostic screening tool, eye movement detection systems are commonly used for early screening, symptom assessment, and medical research. As early as 1908, Diefendorf and Dodge first proposed using corneal reflection, photographic timing, and recording eye movement speed and responses to novel peripheral stimuli to study the eye responses of users with psychiatric disorders. In 1987, Holzman et al. first reported that users with schizophrenia had a greater saccade rate in smooth eye movements than normal controls and non-schizophrenic users, suggesting that abnormal eye movements may be a characteristic marker of schizophrenia. Exploratory eye movements (EEM) refer to eye movements made by users while looking at a static image.

[0004] Kojima et al. found that users with schizophrenia made fewer eye movements in response to verbal stimuli when the same horizontal S-shaped pattern was played alternately with a slightly different pattern, and reported that these "exploratory eye movements" appear to be a specific marker of schizophrenia.

[0005] However, the current related technologies still lack methods for collecting eye movement information and performing refined data processing on it. Summary of the Invention

[0006] The present invention provides a method, device, electronic device and storage medium for collecting and processing eye movement information, so as to solve the defect in the related art that there is a lack of a method for collecting eye movement information and performing refined data processing on it.

[0007] The present invention provides a method for collecting and processing eye movement information, comprising:

[0008] Detect the user's eye trajectory and obtain trajectory detection data;

[0009] calculating an error rate of the trajectory detection data and calculating a jitter rate of the trajectory detection data;

[0010] If the error rate and the jitter rate of the trajectory detection data meet the set requirements, eye movement information is calculated based on the trajectory detection data, and the eye movement information is used to detect the user's mental illness.

[0011] According to the method for collecting and processing eye movement information provided by the present invention, calculating the error rate of the trajectory detection data includes:

[0012] Obtain the coordinates of several measured points corresponding to the reference points in the user's trajectory detection data;

[0013] The distance between each measured point and the corresponding reference point is calculated using the following formula (1):

[0014]

[0015] Among them, d ij is the distance between the measured point and the i-th reference point, X ij is the horizontal coordinate of the measured point, X i is the horizontal coordinate of the i-th reference point, Y ij is the vertical coordinate of the measured point, Y i is the ordinate of the i-th reference point;

[0016] The average distance between the measured points and the corresponding reference points is calculated by the following formula (2):

[0017]

[0018] Where n is the number of measured points corresponding to the benchmark point;

[0019] The error rate of each reference point is calculated using the following formula (3):

[0020] δ i =d i / TVL (3);

[0021] Among them, δ i is the error rate of the i-th reference point, TVL is the display resolution;

[0022] The error rate of the trajectory detection data is calculated by the following formula (4):

[0023]

[0024] Wherein, R is the error rate of the trajectory detection data.

[0025] According to the eye movement information collection and processing method provided by the present invention, the step of calculating the wobble rate of the trajectory detection data includes:

[0026] Obtain the coordinates of several measured points corresponding to the set gaze points in the user's trajectory detection data;

[0027] Based on the coordinates of the several measured points, the horizontal coordinate of the center coordinate point is determined as Xc , the vertical coordinate is Y c ;

[0028] The distance between each measured point and the center coordinate point is calculated by the following formula (5):

[0029]

[0030] Among them, d ti is the distance between the measured point and the center coordinate point, X ti is the horizontal coordinate of the measured point, Y ti is the vertical coordinate of the measured point;

[0031] The average distance between the measured points and the center coordinate point is calculated by the following formula (6):

[0032]

[0033] Wherein, m is the number of the measured points;

[0034] The jitter rate of the trajectory detection data is calculated by the following formula (7):

[0035] δ=d tc / TVL (7);

[0036] Wherein, δ is the jitter rate of the trajectory detection data, and TVL is the display resolution.

[0037] According to the eye movement information collection and processing method provided by the present invention, the horizontal coordinate of the center coordinate point is determined as X based on the coordinates of the plurality of measured points. c , the vertical coordinate is Y c ,include:

[0038] connecting the plurality of measured points to form a polygon;

[0039] Splitting the polygon into a plurality of triangles and calculating the center point of each triangle;

[0040] A weighted sum is performed on the abscissas of the center points of the triangles to obtain the abscissa and the ordinate of the center coordinate point, and a weighted sum is performed on the ordinates of the center points of the triangles to obtain the ordinate of the center coordinate point.

[0041] According to the method for collecting and processing eye movement information provided by the present invention, the eye movement information is calculated based on the trajectory detection data, including:

[0042] The missing data points are linearly interpolated using the following formula (8):

[0043]

[0044] Among them, x i is the horizontal coordinate of the point where data is missing, y i is the vertical coordinate of the point where data is missing, x i-1 is the horizontal coordinate of the previous valid point of the data missing point, y i-1 is the vertical coordinate of the previous valid point of the data missing point, x i+1 is the horizontal coordinate of the next valid point after the data missing point, y i+1 is the ordinate of the next valid point after the data missing point, t i is the timestamp corresponding to the data missing point, t i-1 is the timestamp of the last valid eye movement data point before the missing data point, t i+1 The timestamp of the next valid eye movement data point after the missing data point.

[0045] According to the method for collecting and processing eye movement information provided by the present invention, the eye movement information is calculated based on the trajectory detection data, further comprising:

[0046] The trajectory detection data is smoothed using the following formula (9):

[0047]

[0048] in, W is the window length, c i is the filter coefficient, is the data after smoothing, y t+i is the data before smoothing.

[0049] According to the method for collecting and processing eye movement information provided by the present invention, when the eye movement information meets set conditions, it is determined that the user suffers from a mental illness.

[0050] The present invention also provides a device for collecting and processing eye movement information, comprising:

[0051] A data acquisition module is used to detect the user's eye trajectory and obtain trajectory detection data;

[0052] a data verification module, configured to calculate an error rate and a jitter rate of the trajectory detection data;

[0053] The data processing module is used to calculate eye movement information based on the trajectory detection data if the error rate and jitter rate of the trajectory detection data meet the set requirements, and the eye movement information is used to detect the user's mental illness.

[0054] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, any of the above-mentioned methods for collecting and processing eye movement information is implemented.

[0055] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-mentioned methods for collecting and processing eye movement information.

[0056] The present invention also provides a computer program product, comprising a computer program, which implements any of the above-mentioned methods for collecting and processing eye movement information when executed by a processor.

[0057] The method for collecting and processing eye movement information provided by the present invention can convert the results of eye trajectory detection into eye movement information for detecting a user's mental illness. In this process, the trajectory detection data can also be screened by calculating the error rate and jitter rate of the trajectory detection data. In this way, the accuracy of the acquired eye movement information can be improved, and further, the accuracy of subsequent detection of the user's mental illness can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0059] Figure 1 1 is a flow chart of a method for collecting and processing eye movement information provided by an embodiment of the present invention;

[0060] Figure 2 1 is a schematic diagram of the structure of an apparatus for collecting and processing eye movement information provided by an embodiment of the present invention;

[0061] Figure 3 It is a schematic diagram of the physical structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0062] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0063] Figure 1It is a flowchart of the method for collecting and processing eye movement information provided by an embodiment of the present invention.

[0064] like Figure 1 As shown, this embodiment provides a method for collecting and processing eye movement information, including:

[0065] Step 101: Detect the user's eye trajectory and obtain trajectory detection data;

[0066] In practical applications, eye trajectory detection

[0067] In practical applications, the trajectory detection data obtained can include gaze accuracy, average gaze latency, total gaze time, gaze track length, gaze interest area ratio, coverage and number of fixations, saccade count, and gaze following rate.

[0068] The gaze accuracy rate can reflect the user's gaze accuracy on the target area. The normal range of gaze accuracy rate is 80%-90%. If the gaze accuracy rate is lower than 80%, it indicates that the user may have attention bias or neglect. This detection data can be used to evaluate the user's concentration on a specific target area.

[0069] The average gaze latency reflects the speed of the user's gaze reaction. The longer the latency, the slower the attention shift. The normal range of the average gaze latency is 200-300 milliseconds. If the user's gaze latency significantly exceeds 300 milliseconds, it means that the user may have a slow reaction. This test data can be used to evaluate the patient's reaction speed to new stimuli.

[0070] The normal range of total gaze time is 10-15 seconds. The longer the gaze time, the more time the patient may need to process information. If the user's gaze time significantly exceeds 15 seconds, it means that the user's cognitive load has increased. This test data can be used to judge the user's task completion efficiency and the use of cognitive resources.

[0071] The length of the gaze track reflects the user's eye exploration of the visual scene. The normal range of the gaze track length is 5-10 meters. If the gaze track length is shortened, it means that the user has neglected a certain area. This detection data can be used to evaluate the user's visual exploration behavior and attention distribution.

[0072] The gaze interest area ratio reflects the distribution of the user's gaze time on a specific area of ​​interest. The left-right ratio of the gaze interest area should be close to 1:1. If it is significantly lower or higher, it means that the user may be neglecting it. This detection data can be used to diagnose the type and severity of the user's neglect.

[0073] The coverage rate and number of fixations can reflect the user's coverage of the target object and the number of times they fixate on it. The normal range of coverage rate is 80%-90%. If it is lower than 80%, it means that the user may have neglected or insufficient visual search. The detection data can be used to evaluate the user's efficiency in specific visual tasks.

[0074] The number of saccades reflects the number of times the user's eyes jump in the visual scene, representing the breadth of visual information search. The normal range of saccades should be 20-40 times / minute. If the user's saccades are too few, it may indicate insufficient search. This detection data can be used to evaluate the completeness of the user's visual search process.

[0075] The gaze following rate indicates the user's preference for the left or right side of the space. The difference between the left and right offsets should be within 10%. If there is a significant difference, it means that the user may have a spatial preference or neglect. This detection data can be used to determine the user's attention bias towards the left and right space.

[0076] Step 102, calculating the error rate of the trajectory detection data and the jitter rate of the trajectory detection data;

[0077] In practical applications, the calculation of the error rate of the trajectory detection data includes:

[0078] Obtain the coordinates of several measured points corresponding to the reference points in the user's trajectory detection data;

[0079] The distance between each measured point and the corresponding reference point is calculated using the following formula (1):

[0080]

[0081] Among them, d ij is the distance between the measured point and the i-th reference point, X ij is the horizontal coordinate of the measured point, X i is the horizontal coordinate of the i-th reference point, Y ij is the vertical coordinate of the measured point, Y i is the ordinate of the i-th reference point;

[0082] The average distance between the measured points and the corresponding reference points is calculated by the following formula (2):

[0083]

[0084] Where n is the number of measured points corresponding to the benchmark point;

[0085] The error rate of each reference point is calculated using the following formula (3):

[0086] δ i =d i / TVL (3);

[0087] Among them, δ i is the error rate of the i-th reference point, TVL is the display resolution;

[0088] The error rate of the trajectory detection data is calculated by the following formula (4):

[0089]

[0090] Wherein, R is the error rate of the trajectory detection data.

[0091] In practical applications, the calculation of the wobble rate of the trajectory detection data includes:

[0092] Obtain the coordinates of several measured points corresponding to the set gaze points in the user's trajectory detection data;

[0093] Based on the coordinates of the several measured points, the horizontal coordinate of the center coordinate point is determined as X c , the vertical coordinate is Y c ;

[0094] The distance between each measured point and the center coordinate point is calculated by the following formula (5):

[0095]

[0096] Among them, d ti is the distance between the measured point and the center coordinate point, X ti is the horizontal coordinate of the measured point, Y ti is the vertical coordinate of the measured point;

[0097] The average distance between the measured points and the center coordinate point is calculated by the following formula (6):

[0098]

[0099] Wherein, m is the number of the measured points;

[0100] The jitter rate of the trajectory detection data is calculated by the following formula (7):

[0101] δ=d tc / TVL (7);

[0102] Wherein, δ is the jitter rate of the trajectory detection data, and TVL is the display resolution.

[0103] In the above steps, the horizontal and vertical coordinates of the center coordinate point can be determined as follows:

[0104] connecting the plurality of measured points to form a polygon;

[0105] Splitting the polygon into a plurality of triangles and calculating the center point of each triangle;

[0106] A weighted sum is performed on the abscissas of the center points of the triangles to obtain the abscissa and the ordinate of the center coordinate point, and a weighted sum is performed on the ordinates of the center points of the triangles to obtain the ordinate of the center coordinate point.

[0107] Step 103 : If the error rate and the jitter rate of the trajectory detection data meet the set requirements, eye movement information is calculated based on the trajectory detection data, and the eye movement information is used to detect the user's mental illness.

[0108] In practical applications, the following formula (8) can be used to perform linear interpolation on missing data points:

[0109]

[0110] Among them, x i is the horizontal coordinate of the point where data is missing, y i is the vertical coordinate of the point where data is missing, x i-1 is the horizontal coordinate of the previous valid point of the data missing point, y i-1 is the vertical coordinate of the previous valid point of the data missing point, x i+1 is the horizontal coordinate of the next valid point after the data missing point, y i+1 is the ordinate of the next valid point after the data missing point, t i is the timestamp corresponding to the data missing point, t i-1 is the timestamp of the last valid eye movement data point before the missing data point, t i+1 The timestamp of the next valid eye movement data point after the missing data point.

[0111] Furthermore, the trajectory detection data can be smoothed using the following formula (9):

[0112]

[0113] in, W is the window length, c i is the filter coefficient, is the data after smoothing, y t+i is the data before smoothing.

[0114] In actual application, when the eye movement information meets the set conditions, it can be determined that the user suffers from a mental illness, and the severity of the user's mental illness can be further determined based on the eye movement information. The mental illnesses described in this embodiment can include depression and schizophrenia.

[0115] In practical applications, the accuracy of gaze and NEF are negatively correlated; the accuracy of gaze and total gaze distance are negatively correlated; the gaze latency and reaction exploration score are negatively correlated; the gaze time and cognitive exploration score are positively correlated; the gaze time and NEF are negatively correlated; the total gaze distance / average distance and reaction exploration score are positively correlated; the total gaze distance / average distance and cognitive exploration score are negatively correlated;

[0116] The method for collecting and processing eye movement information provided in this embodiment can convert the results of eye trajectory detection into eye movement information for detecting a user's mental illness. In this process, the trajectory detection data can also be screened by calculating the error rate and jitter rate of the trajectory detection data. In this way, the accuracy of the acquired eye movement information can be improved, and further, the accuracy of subsequent detection of the user's mental illness can be improved.

[0117] The following describes the apparatus for collecting and processing eye movement information provided by the present invention. The apparatus for collecting and processing eye movement information described below and the method for collecting and processing eye movement information described above can refer to each other.

[0118] Figure 2 It is a structural diagram of an apparatus for collecting and processing eye movement information provided by an embodiment of the present invention.

[0119] like Figure 2 As shown, the apparatus for collecting and processing eye movement information provided in this embodiment includes:

[0120] The data acquisition module 201 is used to detect the user's eye trajectory and obtain trajectory detection data;

[0121] A data verification module 202 is configured to calculate an error rate and a jitter rate of the trajectory detection data;

[0122] The data processing module 203 is configured to calculate eye movement information based on the trajectory detection data if the error rate and the jitter rate of the trajectory detection data meet set requirements, and the eye movement information is used to detect the user's mental illness.

[0123] In an exemplary embodiment, the data verification module 202 is further configured to: obtain coordinates of a plurality of measured points corresponding to the reference points in the user's trajectory detection data;

[0124] The distance between each measured point and the corresponding reference point is calculated using the following formula (1):

[0125]

[0126] Among them, d ij is the distance between the measured point and the i-th reference point, X ij is the horizontal coordinate of the measured point, Xi is the horizontal coordinate of the i-th reference point, Y ij is the vertical coordinate of the measured point, Y i is the ordinate of the i-th reference point;

[0127] The average distance between the measured points and the corresponding reference points is calculated by the following formula (2):

[0128]

[0129] Where n is the number of measured points corresponding to the benchmark point;

[0130] The error rate of each reference point is calculated using the following formula (3):

[0131] δ i =d i / TVL (3);

[0132] Among them, δ i is the error rate of the i-th reference point, TVL is the display resolution;

[0133] The error rate of the trajectory detection data is calculated by the following formula (4):

[0134]

[0135] Wherein, R is the error rate of the trajectory detection data.

[0136] In an exemplary embodiment, the data verification module 202 is further configured to:

[0137] Obtain the coordinates of several measured points corresponding to the set gaze points in the user's trajectory detection data;

[0138] Based on the coordinates of the several measured points, the horizontal coordinate of the center coordinate point is determined as X c , the vertical coordinate is Y c ;

[0139] The distance between each measured point and the center coordinate point is calculated by the following formula (5):

[0140]

[0141] Among them, d ti is the distance between the measured point and the center coordinate point, X ti is the horizontal coordinate of the measured point, Y ti is the vertical coordinate of the measured point;

[0142] The average distance between the measured points and the center coordinate point is calculated by the following formula (6):

[0143]

[0144] Wherein, m is the number of the measured points;

[0145] The jitter rate of the trajectory detection data is calculated by the following formula (7):

[0146] δ=d tc / TVL (7);

[0147] Wherein, δ is the jitter rate of the trajectory detection data, and TVL is the display resolution.

[0148] In an exemplary embodiment, the data verification module 202 is further configured to:

[0149] connecting the plurality of measured points to form a polygon;

[0150] Splitting the polygon into a plurality of triangles and calculating the center point of each triangle;

[0151] A weighted sum is performed on the abscissas of the center points of the triangles to obtain the abscissa and the ordinate of the center coordinate point, and a weighted sum is performed on the ordinates of the center points of the triangles to obtain the ordinate of the center coordinate point.

[0152] In an exemplary embodiment, the data processing module is further configured to:

[0153] The missing data points are linearly interpolated using the following formula (8):

[0154]

[0155] Among them, x i is the horizontal coordinate of the point where data is missing, y i is the vertical coordinate of the point where data is missing, x i-1 is the horizontal coordinate of the previous valid point of the data missing point, y i-1 is the vertical coordinate of the previous valid point of the data missing point, x i+1 is the horizontal coordinate of the next valid point after the data missing point, y i+1 is the ordinate of the next valid point after the data missing point, t i For t i-1 For t i+1 for.

[0156] In an exemplary embodiment, the data processing module is further configured to:

[0157] The trajectory detection data is smoothed by the following formula (9):

[0158]

[0159] in, W is the window length, c i is the filter coefficient, is the data after smoothing, y t+i is the data before smoothing.

[0160] The specific implementation method of the device for collecting and processing eye movement information provided in this embodiment can be implemented with reference to the above embodiments and will not be repeated here.

[0161] Figure 3 An example of a physical structure diagram of an electronic device is shown below. Figure 3 As shown, the electronic device may include: a processor 310, a communication interface 320, a memory 330, and a communication bus 340, wherein the processor 310, the communication interface 320, and the memory 330 communicate with each other via the communication bus 340. The processor 310 may call the logic instructions in the memory 330 to execute the method for collecting and processing eye movement information, which includes:

[0162] Detect the user's eye trajectory and obtain trajectory detection data;

[0163] calculating an error rate of the trajectory detection data and calculating a jitter rate of the trajectory detection data;

[0164] If the error rate and the jitter rate of the trajectory detection data meet the set requirements, eye movement information is calculated based on the trajectory detection data, and the eye movement information is used to detect the user's mental illness.

[0165] In addition, the logic instructions in the above-mentioned memory 330 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the various embodiments of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0166] On the other hand, the present invention further provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can perform the eye movement information collection and processing method provided by the above methods, which includes:

[0167] Detect the user's eye trajectory and obtain trajectory detection data;

[0168] calculating an error rate of the trajectory detection data and calculating a jitter rate of the trajectory detection data;

[0169] If the error rate and the jitter rate of the trajectory detection data meet the set requirements, eye movement information is calculated based on the trajectory detection data, and the eye movement information is used to detect the user's mental illness.

[0170] In another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for collecting and processing eye movement information provided by the above methods is implemented, and the method includes:

[0171] Detect the user's eye trajectory and obtain trajectory detection data;

[0172] calculating an error rate of the trajectory detection data and calculating a jitter rate of the trajectory detection data;

[0173] If the error rate and the jitter rate of the trajectory detection data meet the set requirements, eye movement information is calculated based on the trajectory detection data, and the eye movement information is used to detect the user's mental illness.

[0174] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units. That is, they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0175] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus the necessary general hardware platform, or of course, by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the existing technology can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of each embodiment or certain parts of the embodiment.

[0176] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for collecting and processing eye movement information, characterized in that: include: Detect the user's eye trajectory and obtain trajectory detection data; calculating an error rate of the trajectory detection data and calculating a jitter rate of the trajectory detection data; If the error rate and the jitter rate of the trajectory detection data meet the set requirements, eye movement information is calculated based on the trajectory detection data, and the eye movement information is used to detect the user's mental illness.

2. The method for collecting and processing eye movement information according to claim 1, characterized in that: Calculating the error rate of the trajectory detection data includes: Obtain the coordinates of several measured points corresponding to the reference points in the user's trajectory detection data; The distance between each measured point and the corresponding reference point is calculated using the following formula (1): Among them, d ij is the distance between the measured point and the i-th reference point, X ij is the horizontal coordinate of the measured point, X i is the horizontal coordinate of the i-th reference point, Y ij is the vertical coordinate of the measured point, Y i is the ordinate of the i-th reference point; The average distance between the measured points and the corresponding reference points is calculated by the following formula (2): Where n is the number of measured points corresponding to the benchmark point; The error rate of each reference point is calculated using the following formula (3): δ i =d i / TVL (3); Among them, δ i is the error rate of the i-th reference point, TVL is the display resolution; The error rate of the trajectory detection data is calculated by the following formula (4): Wherein, R is the error rate of the trajectory detection data.

3. The method for collecting and processing eye movement information according to claim 1, characterized in that: The calculating the wobble rate of the trajectory detection data includes: Obtain the coordinates of several measured points corresponding to the set gaze points in the user's trajectory detection data; Based on the coordinates of the several measured points, the horizontal coordinate of the center coordinate point is determined as X c , the vertical coordinate is Y c ; The distance between each measured point and the center coordinate point is calculated by the following formula (5): Among them, d ti is the distance between the measured point and the center coordinate point, X ti is the horizontal coordinate of the measured point, Y ti is the vertical coordinate of the measured point; The average distance between the measured points and the center coordinate point is calculated by the following formula (6): Wherein, m is the number of the measured points; The jitter rate of the trajectory detection data is calculated by the following formula (7): δ=d tc / TVL (7); Wherein, δ is the jitter rate of the trajectory detection data, and TVL is the display resolution.

4. The method for collecting and processing eye movement information according to claim 3, characterized in that: Based on the coordinates of the several measured points, the horizontal coordinate of the center coordinate point is determined as X c , the vertical coordinate is Y c ,include: connecting the plurality of measured points to form a polygon; Splitting the polygon into a plurality of triangles and calculating the center point of each triangle; A weighted sum is performed on the abscissas of the center points of the triangles to obtain the abscissa and the ordinate of the center coordinate point, and a weighted sum is performed on the ordinates of the center points of the triangles to obtain the ordinate of the center coordinate point.

5. The method for collecting and processing eye movement information according to claim 1, characterized in that: The calculating and obtaining eye movement information based on the trajectory detection data includes: The missing data points are linearly interpolated using the following formula (8): Among them, x i is the horizontal coordinate of the point where data is missing, y i is the vertical coordinate of the point where data is missing, x i-1 is the horizontal coordinate of the previous valid point of the data missing point, y i-1 is the vertical coordinate of the previous valid point of the data missing point, x i+1 is the horizontal coordinate of the next valid point after the data missing point, y i+1 is the ordinate of the next valid point after the data missing point, t i is the timestamp corresponding to the data missing point, t i-1 is the timestamp of the last valid eye movement data point before the missing data point, t i+1 The timestamp of the next valid eye movement data point after the missing data point.

6. The method for collecting and processing eye movement information according to claim 1, characterized in that: The step of calculating the eye movement information based on the trajectory detection data further includes: The trajectory detection data is smoothed by the following formula (9): in, W is the window length, c i is the filter coefficient, is the data after smoothing, y t+i is the data before smoothing.

7. The method for collecting and processing eye movement information according to claim 1, characterized in that: When the eye movement information meets the set conditions, it is determined that the user suffers from a mental illness.

8. A device for collecting and processing eye movement information, characterized in that: include: A data acquisition module is used to detect the user's eye trajectory and obtain trajectory detection data; a data verification module, configured to calculate an error rate and a jitter rate of the trajectory detection data; The data processing module is used to calculate eye movement information based on the trajectory detection data if the error rate and jitter rate of the trajectory detection data meet the set requirements, and the eye movement information is used to detect the user's mental illness.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the method for collecting and processing eye movement information as described in any one of claims 1 to 7 is implemented.

10. A storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for collecting and processing eye movement information as described in any one of claims 1 to 7 is implemented.

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