Eye movement data processing method and system for autism spectrum disorder detection

Abstract

The invention discloses an eye movement data processing method and system for autism spectrum disorder detection, a plurality of detection icons in a detection task are displayed according to different movement rules, the moving detection icons guide a testee to watch a display module, then an eye movement image sequence of the testee watching the detection icons is collected, and the eye movement image sequence of the testee watching the detection icons is displayed according to the collected eye movement image sequence. Extracting eye movement data from the detection icons, grouping the eye movement data, calculating a sight line drop point displacement data set corresponding to each group of eye movement data, importing a motion function corresponding to each detection icon into a test coordinate system, and respectively importing each sight line drop point displacement data set into the test coordinate system; and judging which motion function each sight line drop point displacement data set accords with, thereby dynamically associating the sight line position of the testee with the detection icon, and realizing the rule capture of the sight line of the testee following the detection icon. The detection mode does not need to be matched with high-precision and high-cost hardware for use, the universality is improved, and a large-range and rapid-detection screening scene can be met.

Description

Technical Field

[0001] The present invention belongs to the technical field of image data processing, and in particular, relates to an eye movement data processing method and system for autism spectrum disorder detection. Background Art

[0002] At present, the detection methods of autism spectrum disorder are divided into two categories: the first method detects the three core characteristics of autism spectrum disorder and the unique behavioral patterns of autism spectrum disorder based on the core characteristics; the second method detects the physiological indicators of autistic patients that are different from normal people (including but not limited to eye movement parameters, brain structure and connectivity related parameters, etc.).

[0003] The existing various questionnaires and scales for screening and detecting autism spectrum disorders (Child Heart Scale-II, CARS, etc.) are based on the first type of method, and scenario-based questionnaires are designed for detection, combined with on-site actual interaction and physical operation to achieve the purpose of evaluating the condition; on this basis, some existing equipment reproduces the required detection scenarios in the form of visual scenario images and images, optimizes and simplifies manual operations in an intelligent way, and cooperates with eye trackers to capture the corresponding sight positions, and submits the test results to doctors as a basis for assisting doctors in diagnosis. However, the detection form of traditional scales takes a very long time to complete, and doctors need to maintain professional interaction and observation with patients and relatives throughout the process, and the work intensity is high. It is not conducive to large-scale and rapid screening, and the efficiency of diagnostic work is relatively low. The auxiliary diagnostic device used with the eye tracker, because each patient needs to undergo an eye calibration process of about 15 minutes before each test, before starting the detection process; because it is necessary to know the exact sight position to judge the actual viewing content of the patient, the accuracy of the eye tracker is also very high. The above two reasons also make the first method unfavorable for large-scale and rapid screening. At the same time, the early calibration process also requires a high degree of cooperation from the patient, which increases the complexity of the doctor's operation, making it more difficult to complete the test. Young children are difficult to cooperate in completing the calibration, which can easily make them lose the opportunity for early intervention.

[0004] When patients with autism spectrum disorders look at specific objects, scenes, actions, etc., their saccade parameters (such as saccade amplitude, saccade latency / saccade reaction time, saccade peak velocity), eye movement parameters (such as mean, standard deviation / standard error, effect size), pupil size and other values ​​are different from those of normal people. On the basis of the auxiliary diagnostic device used with the eye tracker mentioned above, a more accurate eye tracker is used to obtain and analyze relevant physiological information and give statistical data, combined with the patient's response to the test scenario for comprehensive judgment, to improve the accuracy and credibility of the test results. The disadvantages of this second method are the same as those of the first method. At the same time, higher accuracy means more expensive costs, which further amplifies the above disadvantages. Summary of the invention

[0005] The purpose of the present invention is to provide a more efficient eye movement data processing method and system for autism spectrum disorder detection. The detection task is designed based on the three core characteristics of autism spectrum disorder (social disorder, communication disorder, narrow interests and stereotyped behavior) and unique behavior patterns (joint attention paradigm, name-calling response paradigm, non-social sound stimulation behavior paradigm, etc.), and a display device is used to display each detection icon in the detection task with different movement patterns. The eye movement image sequence of the test subject's eyes watching the detection icon is collected, the eye movement image sequence is analyzed, and compared with the movement pattern of the detection icon, it is determined whether the test subject's eye movement follows the detection icon, and the test results are obtained to assist doctors in diagnosis.

[0006] In order to solve the above technical problems, this application adopts the following technical solutions:

[0007] A method for processing eye movement data for autism spectrum disorder detection is proposed, comprising:

[0008] S1, displaying a moving detection icon group on a display module; wherein the detection icon group includes at least two detection icons, and each detection icon moves according to a different motion function;

[0009] S2, collecting the eye movement image sequence of the test subject when watching the display module, analyzing to obtain the eye movement position data, and constructing the eye movement data scatter plot with time as the horizontal axis and the eye movement position as the vertical axis;

[0010] S3, grouping the eye movement data based on the eye movement data scatter plot;

[0011] S4, calculating the sight point displacement data set of each data group, and constructing a scatter plot of the sight point displacement data;

[0012] S5, constructing a test coordinate system, and importing the motion function corresponding to the detection icon into the test coordinate system;

[0013] S6. Import the line-of-sight landing point displacement data sets of each group into the test coordinate system in sequence and compare them with the graphs of each motion function to find the motion function that each line-of-sight landing point displacement data set conforms to;

[0014] S7. Classify the line-of-sight landing point displacement data sets of each group and output the statistical results.

[0015] In some embodiments of the present invention, step S3 specifically includes:

[0016] Set a constant k. Starting from the origin of the scatter plot coordinate system of the eye movement data, sequentially take two adjacent data in the horizontal axis direction; the constant k is: the critical value between the eye movement amplitude and the saccade amplitude during tracking and fixation, manifested as the displacement amount in the collected eye movement images;

[0017] Obtain the vertical coordinate values of the two data and calculate the absolute value Δk of the difference between the two;

[0018] Compare Δk with the constant k. If Δk < k, classify the two data into the same group of data. If Δk > k, take a as the segmentation point of the data set, classify a into the previous group of data, and classify b into a new group of data.

[0019] In some embodiments of the present invention, calculating the line-of-sight landing point displacement data set of each data group in step S4 specifically includes:

[0020] Multiply the position coordinates of the eye movement data by the constant to obtain the line-of-sight landing point displacement data set; where , is the displacement amount of a single detection icon, is the iris displacement amount corresponding to the eye movement image sequence.

[0021] In some embodiments of the present invention, importing the motion function corresponding to the detection icon into the test coordinate system in step S5 specifically includes:

[0022] Taking the center of the detection icon as the origin of the test coordinate system and the motion function as the motion trajectory of the origin, setting the distance between the left edge picture of the detection icon and the origin and the distance between the right edge picture of the detection icon and the origin as a, and obtaining the motion function of the left edge of the detection icon and the motion function of the right edge picture;

[0023] Import the motion function of the left edge and the motion function of the right edge into the test coordinate system, and the range enclosed by the two motion functions constitutes the actual motion area of the entire picture of the detection icon.

[0024] In some embodiments of the present invention, step S6 specifically includes:

[0025] Import each set of sight point displacement data sets into the test coordinate system in turn;

[0026] Compare each set of sight point displacement data sets with the motion area corresponding to each detection icon, and find the number of data points in each motion area for each set of sight point displacement data sets ;

[0027] Will The motion function corresponding to the maximum value is judged as the running function that the sight point displacement data set conforms to.

[0028] An eye movement data processing system for autism spectrum disorder detection is proposed, comprising:

[0029] A display module, used for displaying a moving detection icon group; wherein the detection icon group includes at least two detection icons, and each detection icon moves according to a different motion function;

[0030] A collection module, used for collecting eye movement image sequences when the subject is viewing the display module;

[0031] The computing module is used to process the eye movement data according to the following steps:

[0032] The eye movement position data is obtained by parsing, and a scatter plot of the eye movement data is constructed with time as the horizontal axis and eye movement position as the vertical axis;

[0033] Group the eye movement data based on the eye movement data scatter plot;

[0034] Calculate the sight point displacement data set of each data group and construct a scatter plot of the sight point displacement data;

[0035] Construct a test coordinate system and import the motion function corresponding to the detection icon into the test coordinate system;

[0036] Import each group of sight point displacement data sets into the test coordinate system in turn and compare them with each motion function graph to find the motion function that each sight point displacement data set conforms to;

[0037] Classify each group of sight point displacement data sets and output the statistical results.

[0038] In some embodiments of the present invention, when the computing module groups the eye movement data based on the eye movement data scatter plot, the computing module includes:

[0039] A constant k is set, starting from the origin of the eye movement data scatter plot coordinate system, and two adjacent data in the horizontal axis direction are sequentially taken; the constant k is: the critical value between the eye movement amplitude and the eye saccade amplitude during tracking gaze, which is expressed as the displacement in the collected eye movement image;

[0040] Obtain the vertical coordinate values of two data, and calculate the absolute value Δk of the difference between the two;

[0041] Compare Δk with the constant k. If Δk < k, classify the two data into the same group of data. If Δk > k, use a as the splitting point of the data set, classify a into the previous group of data, and classify b into a new group of data.

[0042] In some embodiments of the present invention, when the operation module calculates the line-of-sight landing point displacement data set of each data group, it specifically includes:

[0043] Multiply the position coordinates of the eye movement data by the constant to obtain the line-of-sight landing point displacement data set; where , is the displacement of a single detection icon, is the iris displacement corresponding to the eye movement image sequence.

[0044] In some embodiments of the present invention, when the operation module imports the motion function corresponding to the detection icon into the test coordinate system, it specifically includes:

[0045] Taking the center of the detection icon as the origin of the test coordinate system, and the motion function as the motion trajectory of the origin, set the distance between the left edge image of the detection icon and the origin and the distance between the right edge image of the detection icon and the origin as a, and obtain the motion function of the left edge of the detection icon and the motion function of the right edge image, where is the number of the motion function;

[0046] Import the motion function of the left edge and the motion function of the right edge into the test coordinate system, and the range enclosed by the two motion functions constitutes the actual motion area of the entire image of the detection icon.

[0047] In some embodiments of the present invention, when the operation module finds the motion function that each line-of-sight landing point displacement data set conforms to, it specifically includes:

[0048] Import each line-of-sight landing point displacement data set into the test coordinate system in turn;

[0049] Compare each line-of-sight landing point displacement data set with the motion area corresponding to each detection icon respectively, and find the number of data points where each line-of-sight landing point displacement data set falls into each motion area ;

[0050] Take the motion function corresponding to the maximum value as the operating function that the line-of-sight landing point displacement data set conforms to.

[0051] Compared with the prior art, the advantages and positive effects of the present application are: in the eye movement data processing method and system for autism spectrum disorder detection proposed in the present application, based on the core characteristics and unique behavioral patterns of autism spectrum disorder, a test task is designed to mine the eye movement patterns of the test subject based on observing detection icons, thereby assisting the tester in judging whether the test subject has autism spectrum disorder based on the mined eye movement patterns. During the test, a display module is used to display multiple detection icons in the detection task with different motion patterns. The detection icons based on the motion are used to guide the testee to view the display module, and then an eye movement image sequence of the testee's eyes viewing the detection icons is collected, eye movement data is extracted from the eye movement image sequence and grouped, the sight point displacement data set corresponding to each group of eye movement data is calculated, the motion function corresponding to each detection icon is imported into the test coordinate system, and each sight point displacement data set is respectively imported into the test coordinate system, and it is determined which motion function each sight point displacement data set conforms to, thereby dynamically associating the testee's sight line position with the detection icon, and realizing the capture of the regularity of the testee's sight line following the detection icon. Compared with the existing detection method used in conjunction with an eye tracker, the method of the present invention can instantly determine the testee's sight line position, omit the calibration process of the instrument, and achieve the effect of not requiring the testee to adjust and cooperate throughout the process, which can effectively improve the detection efficiency and reduce the difficulty of the detection operation. At the same time, the detection method of the present invention does not need to be used with high-precision and high-cost hardware, improves versatility, and can meet the screening scenarios of large-scale and rapid detection.

[0052] After reading the detailed description of the embodiments of the present application in conjunction with the accompanying drawings, other features and advantages of the present application will become more clear. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 The following is a schematic diagram of the steps of the eye movement data processing method for autism spectrum disorder detection proposed by the present invention;

[0054] Figure 2 A diagram showing a detection icon group in the present invention displayed with different motion functions;

[0055] Figure 3 This is an example of an eye movement data scatter plot constructed in the present invention;

[0056] Figure 4 For the present invention Figure 3 An example of an eye-tracking data scatter plot after it has been grouped is shown;

[0057] Figure 5 It is a diagram showing the relationship between the movement of the iris of the human eye and the displacement of the detection icon that the eye is looking at;

[0058] Figure 6 For the present invention Figure 4An example of a scatter plot of gaze point displacement data of an eye movement data scatter plot shown;

[0059] Figure 7 A schematic diagram of a motion function corresponding to a detection icon in the present invention after being imported into a test coordinate system;

[0060] Figure 8 is an example of a detection icon in the present invention;

[0061] Fig. 9 For the present invention Figure 8 An example of the motion area of ​​the detection icon in the test coordinate system is shown;

[0062] Fig.10 This is a schematic diagram of importing a segment of sight point displacement data set into a test coordinate system in the present invention;

[0063] Fig.11 for Fig.10 The diagram shows the comparison between the sight point displacement data set and the motion area of ​​a detection image;

[0064] Fig.12 for Fig.10 Schematic diagram of the comparison between the gaze point displacement dataset and the motion area of ​​another detection image. DETAILED DESCRIPTION

[0065] The specific implementation methods of the present application are further described in detail below in conjunction with the accompanying drawings.

[0066] The present invention aims to propose an eye movement data processing method for auxiliary diagnosis of autism spectrum disorder detection. The whole system includes a display module, an acquisition module, an operation module, a calculation module and an output module; the display module is used to display the detection icon, and the displayed content is controlled by the calculation module; the acquisition module is used to collect the eye movement image sequence of the test subject when viewing the detection icon; the operation module is used to control the start, stop, pause and other operations of the detection; the calculation module is used to drive each module, run the algorithm, and obtain the data processing result; the output module outputs the data processing result in electronic or paper form.

[0067] Combination Figure 1 As shown, the eye movement data processing method for autism spectrum disorder detection proposed by the present invention comprises the following steps:

[0068] S1: Displaying a moving detection icon group on a display module; the detection icon group includes at least two detection icons, and each detection icon moves according to a different motion function.

[0069] For example, multiple detection icons of a detection icon group are displayed horizontally back and forth on the display module using different motion functions, for example Figure 2For the two detection icons shown, the detection icon of the human head image moves back and forth from left to right at the first set rate, and the detection icon of the train head image moves back and forth from right to left at the second set rate.

[0070] Among them, multiple detection diagrams are marked as detection icon 1, detection icon 2, ……, detection icon n; the corresponding motion functions are denoted as , , ……, .

[0071] S2: Collect the eye movement image sequence of the tested person when watching the display module, parse to obtain the eye movement position data, use time as the horizontal axis and eye movement position as the vertical axis to construct an eye movement data scatter plot.

[0072] The acquisition module collects the eye movement image sequence of the tested person during the viewing of the detection icon on the display module, parses the eye movement image sequence to obtain the eye movement position data (horizontal and / or vertical), uses time as the horizontal axis and the position data as the vertical axis to establish the coordinate system of the eye movement data scatter plot, and imports the eye movement position data into the coordinate system according to the acquisition time of the image sequence to obtain the scatter plot of the eye movement data. As Figure 3 shown is the eye movement data scatter plot obtained by importing the horizontal position data of the eye movement into the coordinate system according to the acquisition time.

[0073] S3: Group the eye movement data based on the eye movement data scatter plot.

[0074] Set a constant k. Starting from the origin of the eye movement data scatter plot coordinate system, successively take two adjacent data a and b in the horizontal axis direction (that is, the eye movement data corresponding to two adjacent time points), obtain the vertical coordinate values of the two data, calculate the absolute value Δk of the difference between the two, compare Δk with the constant k. If Δk < k, then classify the two data into the same group of data. If Δk > k, then use a as the segmentation point of the data set, classify a into the previous group of data, and classify b into a new group of data.

[0075] The purpose of this step is to optimize the fitting degree of the fitting curve in subsequent operations and reduce interference.

[0076] The actual meaning of the constant k is: the critical value between the eye movement amplitude and the saccade amplitude during tracking gaze, which is the displacement amount shown in the collected eye movement images.

[0077] The scatter plot after grouping is as Figure 4 shown. Assume that finally N groups of eye movement data are separated.

[0078] S4: Calculate the line-of-sight landing point displacement data set of each data group and construct a line-of-sight landing point displacement data scatter plot.

[0079] As Figure 5As shown, there is a positive proportional relationship between the iris movement L1 of the human eye and the displacement L2 of the detection icon that the eye is looking at per unit time. In the figure, o is the eye axis position, L3 is the distance between the iris and the eye axis, and L4 is the distance between the iris and the display module. Similarly, the iris displacement L0 in the eye movement image sequence is also in direct proportion to the iris displacement L1 of the human eye. Therefore, the displacement L2 of the detection icon, the iris displacement L0 of the eye movement image sequence, and the iris displacement L1 of the human eye are in direct proportion. By measuring the displacement L2 of a single detection icon and the corresponding iris displacement L0 in the eye movement image sequence, the ratio between the two can be obtained. .

[0080] According to the above principles, the eye movement dataset By multiplying the position coordinate value of with the constant J, we can get the sight point displacement data set or ; Reconstruct the scatter plot based on the new data set to obtain the sight point displacement data scatter plot, such as Figure 6 shown.

[0081] S5: construct a test coordinate system, and import the motion function corresponding to the detection icon into the test coordinate system.

[0082] Construct a test coordinate system and transform the motion function of each detection icon (1, 2, ..., n) , ,……, Import the constructed test coordinate system, such as Figure 7 An embodiment is shown.

[0083] S6: Importing each group of sight point displacement data sets into the test coordinate system in turn and comparing them with each motion function graph to find the motion function that each sight point displacement data set conforms to.

[0084] Taking the eye movement data divided into four groups in step S3 as an example, , , , Indicates that first The data of the data group are imported into the test coordinate system to determine the range of the motion function graph within which each data point falls. Assume The data set contains 10 data points. Then determine which range of motion function graph these 10 data points fall into, and count the number of data points that fall within the same range of motion function graph. Set a threshold m. When the number of data points that fall within the same range of motion function graph exceeds the threshold m, determine The data points of the data set conform to the motion function, corresponding to the test subject's line of sight following the detection icon moving with the motion function. The data of the data group is imported into the test coordinate system and the above method is repeated to determine which motion function it conforms to; then Data Group, The data set is imported into the test coordinate system and the above method is repeated to determine which motion function is respectively met.

[0085] S7: Classify each group of sight point displacement data sets and output statistical results.

[0086] That is, the eye movement data of viewing the same detection icon are counted into a set and the result is output.

[0087] From step S6, it has been determined which motion function each sight point displacement data set conforms to, for example The data of the data set conforms to the motion function , The data of the data set conforms to the motion function , The data of the data set conforms to the motion function , The data of the data set conforms to the motion function , then Data Sets and Motion Functions Divide into one category and output the binding relationship, , and Data Sets and Motion Functions Classify into one category and output the binding relationship.

[0088] The present invention is based on the core features and unique behavioral patterns of autism spectrum disorder, and designs a test task based on observing detection icons to explore the eye movement patterns of the test subject, thereby assisting the tester in determining whether the test subject has autism spectrum disorder based on the mined eye movement patterns. During the test, a display module is used to display multiple detection icons in the detection task with different motion patterns, and the test subject is guided to watch the display module based on the motion detection icons (to test the test subject's interest in and ability to follow each detection icon), and then an eye movement image sequence of the test subject watching the detection icon is collected, eye movement data is extracted from the eye movement image sequence and grouped, a sight point displacement data set corresponding to each group of eye movement data is calculated, a motion function corresponding to each detection icon is imported into a test coordinate system, and each sight point displacement data set is respectively imported into the test coordinate system, and it is determined which motion function each sight point displacement data set conforms to, thereby dynamically associating the test subject's sight position with the detection icon, and realizing the capture of the regularity of the test subject's sight following the detection icon. Compared with the existing detection method used with an eye tracker, the method of the present invention can instantly determine the test subject's sight position, omit the calibration process of the instrument, and achieve the effect of not requiring the test subject to adjust and cooperate throughout the process, which can effectively improve the detection efficiency and reduce the difficulty of the detection operation. At the same time, the detection method of the present invention does not need to be used with high-precision and high-cost hardware, improves versatility, and can meet the screening scenario of large-scale and rapid detection.

[0089] The following is a detailed description of steps S5 and S6 in the eye movement data processing method proposed by the present invention using a specific embodiment.

[0090] As Figure 8 Take the detection icon shown in the figure as an example, and set the center of the detection icon as the origin of the test coordinate system. is the motion trajectory of the origin, the distance between the left edge of the detection icon and the origin is set to a, and the distance between the right edge and the origin is also a, then the motion function of the left edge of the detection icon is , the motion function of the right edge of the picture is , import these two functions into the test coordinate system, the range enclosed by the two functions is the actual motion area of ​​the entire screen of the detection icon, such as Fig. 9 shown.

[0091] Import each segment of the sight point displacement data set into the test coordinate system in turn, and compare it with the motion area corresponding to each detection icon. Fig.10 As shown, the first segment of the sight point displacement data set is imported into the test coordinate system and compared with the motion area of ​​the detection icon 1. In each segment of the data set, the multi-table of data points is , keep the horizontal axis (time axis) value unchanged, and add a constant value to the vertical axis data of all data points ,get , so as to achieve the overall translation of the data points in the data set along the ordinate direction in the test coordinate system; The value satisfies the following condition: as many data points as possible in the current data set are included in the motion area of ​​the current detection icon.

[0092] After the above conditions are met, the total number of data points in the statistical data set is recorded as , is the number of the sight point displacement data set; then the number of data points contained in the jth motion area is counted and recorded as .

[0093] like Fig.11 In the embodiment shown, the total number of data points in the first segment of the data set is 11. After all the points in the data set are uniformly translated vertically, at most 9 data points can be included in the motion area of ​​the detection icon 1. , Similarly, if Fig.12 As shown, by comparing the first segment of the data set with the motion area of ​​the detection icon 2, we can get , .

[0094] Repeat the above steps, and after each data set is compared, multiple Value and a value.

[0095] Pick The maximum value among the values ,like , then this segment of the data set and the detection icon The overlap of the motion area is the highest, and it is judged that the test subject's gaze follows the detection icon No other detection icons follow.

[0096] In some embodiments of the present invention, a constant is set Used for Make a judgment, if , it is determined that the time period of the current data set is when the test subject's line of sight is following the detection icon ,like , it is determined that during the time period of the current data set, the test subject’s line of sight did not follow any detection icon.

[0097] In some embodiments of the present invention, the statistical data outputted in step S7 may be visualized in the form of a chart or the like, so that the tester can intuitively know the classification result.

[0098] Based on the above-mentioned eye movement data processing method for autism spectrum disorder detection, the present invention also proposes an eye movement data processing system for autism spectrum disorder detection, including:

[0099] A display module for displaying a group of detection icons in motion; wherein, the group of detection icons includes at least two detection icons, and each detection icon moves according to a different motion function.

[0100] An acquisition module for acquiring a sequence of eye movement images when the subject is watching the display module.

[0101] An operation module for processing eye movement data according to the following steps: parsing to obtain eye movement position data, constructing a scatter plot of eye movement data with time as the horizontal axis and eye movement position as the vertical axis; grouping the eye movement data based on the scatter plot of eye movement data; calculating a set of line-of-sight landing point displacement data for each data group, and constructing a scatter plot of line-of-sight landing point displacement data; constructing a test coordinate system, and importing the motion function corresponding to the detection icon into the test coordinate system; sequentially importing each set of line-of-sight landing point displacement data sets into the test coordinate system for comparison with the graphs of each motion function to find the motion function that each set of line-of-sight landing point displacement data sets conforms to; classifying each set of line-of-sight landing point displacement data sets and outputting the statistical results.

[0102] When the operation module groups the eye movement data based on the scatter plot of eye movement data, it includes: setting a constant k, starting from the origin of the scatter plot coordinate system of the eye movement data, and sequentially taking two adjacent data in the horizontal axis direction; the constant k is: the critical value between the eye movement amplitude and the saccade amplitude during tracking gaze, manifested as the displacement amount in the acquired eye movement images; obtaining the vertical coordinate values of the two data, and calculating the absolute value Δk of the difference between the two; comparing Δk with the constant k, if Δk < k, then classify the two data into the same data group, if Δk > k, then use a as the segmentation point of the data set, classify a into the previous data group, and classify b into a new data group.

[0103] When the operation module calculates the set of line-of-sight landing point displacement data for each data group, it specifically includes: multiplying the position coordinates of the eye movement data by the constant to obtain the set of line-of-sight landing point displacement data; wherein, , is the displacement amount of a single detection icon, is the iris displacement amount corresponding to the eye movement image sequence.

[0104] When the operation module imports the motion function corresponding to the detection icon into the test coordinate system, it specifically includes: taking the center of the detection icon as the origin of the test coordinate system, and the motion function as the motion trajectory of the origin, setting the distance between the left edge image of the detection icon and the origin and the distance between the right edge image of the detection icon and the origin as a, and obtaining the motion function of the left edge of the detection icon and the motion function of the right edge of the screen , is the number of the motion function; the motion function of the left edge and the motion function of the right edge are imported into the test coordinate system, and the range enclosed by the two motion functions constitutes the actual motion area of ​​the entire screen of the detection icon.

[0105] The calculation module finds the motion function that each sight point displacement data set conforms to, specifically including: importing each set of sight point displacement data sets into the test coordinate system in turn; comparing each set of sight point displacement data sets with the motion area corresponding to each detection icon, and finding the number of data points of each set of sight point displacement data sets falling into each motion area ;Will The motion function corresponding to the maximum value is judged as the running function that the sight point displacement data set conforms to.

[0106] The present invention also proposes an autism spectrum disorder detection device, which is equipped with the eye movement data processing system given above, runs the eye movement data processing method given above, and outputs the eye movement regularity data of the test subject. The tester can combine the core characteristics and behavioral patterns of autism spectrum disorder and use the eye movement regularity data of the test subject to assist in determining whether the test subject has autism spectrum disorder.

[0107] It should be noted that, in the specific implementation process, the above-mentioned control part can be implemented by a hardware processor executing computer execution instructions in software form stored in the memory, which will not be elaborated here, and the programs corresponding to the actions performed by the above-mentioned control circuit can be stored in the computer-readable storage medium of the system in software form, so that the processor can call and execute the operations corresponding to the above-mentioned modules.

[0108] The computer-readable storage medium mentioned above may include volatile memory, such as random access memory; may also include non-volatile memory, such as read-only memory, flash memory, hard disk or solid-state drive; may also include a combination of the above types of memory.

[0109] The processor mentioned above may also be a general term for multiple processing elements. For example, the processor may be a central processing unit, or other general-purpose processors, digital signal processors, application-specific integrated circuits, field programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc., or may be a dedicated processor.

[0110] It should be pointed out that the above description is not a limitation of the present invention, and the present invention is not limited to the above examples. Changes, modifications, additions or substitutions made by ordinary technicians in this technical field within the essential scope of the present invention should also fall within the protection scope of the present invention.

Claims

1. A method for processing eye movement data for autism spectrum disorder detection, characterized in that: Including: S1, displaying a group of moving detection icons on a display module; wherein, the detection icon group includes at least two detection icons, and each detection icon moves according to a different motion function; S2, collecting an eye movement image sequence of a subject when watching the display module, parsing to obtain eye movement position data, and constructing an eye movement data scatter plot with time as the horizontal axis and eye movement position as the vertical axis; S3, grouping the eye movement data based on the eye movement data scatter plot; S4, calculating a line-of-sight landing point displacement data set for each data group, and constructing a line-of-sight landing point displacement data scatter plot; S5, constructing a test coordinate system and importing the motion function corresponding to the detection icon into the test coordinate system; S6, sequentially importing each group of line-of-sight landing point displacement data sets into the test coordinate system for comparison with the graphs of each motion function, and finding the motion function that each line-of-sight landing point displacement data set conforms to; S7, classifying each group of line-of-sight landing point displacement data sets and outputting a statistical result.

2. The eye movement data processing method for autism spectrum disorder detection according to claim 1, characterized in that: Step S3 specifically includes: Setting a constant k, starting from the origin of the eye movement data scatter plot coordinate system, sequentially taking two adjacent data in the horizontal axis direction; the constant k is: the critical value between the eye movement amplitude and the saccade amplitude during tracking and fixation, manifested as the displacement amount in the collected eye movement images; Obtaining the vertical coordinate values of the two data and calculating the absolute value Δk of the difference between the two; Comparing Δk with the constant k. If Δk < k, then the two data are grouped into the same data group. If Δk > k, then using a as the segmentation point of the data set, a is grouped into the previous data group, and b is grouped into a new data group.

3. The eye movement data processing method for autism spectrum disorder detection according to claim 1, characterized in that: In step S4, calculating the line-of-sight landing point displacement data set for each data group specifically includes: The position coordinates of the eye movement data are compared with the constant Multiply them together to get the sight point displacement data set; where, , is the displacement of a single detection icon, is the iris displacement corresponding to the eye movement image sequence.

4. The eye movement data processing method for autism spectrum disorder detection according to claim 1, characterized in that: In step S5, importing the motion function corresponding to the detection icon into the test coordinate system specifically includes: The center of the detection icon is taken as the origin of the test coordinate system, the motion function is the motion trajectory of the origin, and the distance between the left edge of the detection icon and the origin and the distance between the right edge of the detection icon and the origin are set to a, and the motion function of the left edge of the detection icon is obtained. and the motion function of the right edge of the screen , is the number of the motion function; Importing the motion function of the left edge and the motion function of the right edge into the test coordinate system, and the range enclosed between the two motion functions constitutes the actual motion area of the entire picture of the detection icon.

5. The eye movement data processing method for autism spectrum disorder detection according to claim 4, characterized in that: Step S6 specifically includes: Sequentially importing each group of line-of-sight landing point displacement data sets into the test coordinate system; Compare each set of sight point displacement data sets with the motion area corresponding to each detection icon, and find the number of data points in each motion area for each set of sight point displacement data sets ; Will The motion function corresponding to the maximum value is judged as the running function that the sight point displacement data set conforms to.

6. An eye movement data processing system for autism spectrum disorder detection, characterized in that: Including: A display module for displaying a group of moving detection icons; wherein, the detection icon group includes at least two detection icons, and each detection icon moves according to a different motion function; An acquisition module for collecting an eye movement image sequence of a subject when watching the display module; An operation module for processing eye movement data according to the following steps: Parsing to obtain eye movement position data, and constructing an eye movement data scatter plot with time as the horizontal axis and eye movement position as the vertical axis; Grouping the eye movement data based on the eye movement data scatter plot; Calculating a line-of-sight landing point displacement data set for each data group, and constructing a line-of-sight landing point displacement data scatter plot; Constructing a test coordinate system and importing the motion function corresponding to the detection icon into the test coordinate system; Sequentially importing each group of line-of-sight landing point displacement data sets into the test coordinate system for comparison with the graphs of each motion function, and finding the motion function that each line-of-sight landing point displacement data set conforms to; Classifying each group of line-of-sight landing point displacement data sets and outputting a statistical result.

7. The eye movement data processing system for autism spectrum disorder detection according to claim 6, characterized in that: When the operation module groups the eye movement data based on the eye movement data scatter plot, it includes: Set a constant k. Starting from the origin of the coordinate system of the eye movement data scatter plot, successively take two adjacent data in the horizontal axis direction; the constant k is: the critical value between the eye movement amplitude during tracking gaze and the saccade amplitude, manifested as the displacement amount in the collected eye movement images. Obtain the vertical coordinate values of the two data, and calculate the absolute value Δk of the difference between the two. Compare Δk with the constant k. If Δk < k, then classify the two data into the same group of data. If Δk > k, then use a as the segmentation point of the data set, classify a into the previous group of data, and classify b into a new group of data.

8. The eye movement data processing system for autism spectrum disorder detection according to claim 6, characterized in that: When calculating the line-of-sight landing point displacement data set of each data group, the operation module specifically includes: The position coordinates of the eye movement data are compared with the constant Multiply them together to get the sight point displacement data set; where, , is the displacement of a single detection icon, is the iris displacement corresponding to the eye movement image sequence.

9. The eye movement data processing system for autism spectrum disorder detection according to claim 6, characterized in that: When the operation module imports the motion function corresponding to the detection icon into the test coordinate system, it specifically includes: The center of the detection icon is taken as the origin of the test coordinate system, the motion function is the motion trajectory of the origin, and the distance between the left edge of the detection icon and the origin and the distance between the right edge of the detection icon and the origin are set to a, and the motion function of the left edge of the detection icon is obtained. and the motion function of the right edge of the screen , is the number of the motion function; Import the motion function of the left edge and the motion function of the right edge into the test coordinate system. The range enclosed by the two motion functions constitutes the actual motion area of the entire picture of the detection icon.

10. The eye movement data processing system for autism spectrum disorder detection according to claim 9, characterized in that: When the operation module finds the motion function that each line-of-sight landing point displacement data set conforms to, it specifically includes: Successively import each group of line-of-sight landing point displacement data sets into the test coordinate system. Compare each set of sight point displacement data sets with the motion area corresponding to each detection icon, and find the number of data points in each motion area for each set of sight point displacement data sets ; Will The motion function corresponding to the maximum value is judged as the running function that the sight point displacement data set conforms to.

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