Hand-eye coordination assessment method and device, electronic equipment and storage medium

Abstract

The invention provides a hand-eye coordination evaluation method and device, electronic equipment and a storage medium, and the method comprises the steps: S1, determining a plurality of sampling points according to a first beta distribution parameter; s2, randomly displaying the plurality of sampling points in a screen according to the explicit durations respectively corresponding to the plurality of sampling points; s3, determining hand-eye coordination evaluation data corresponding to a user according to click operations input to a touch panel by the user aiming at the plurality of sampling points in the screen; and S4, according to the hand-eye coordination evaluation data and a preset condition, target hand-eye coordination evaluation data is determined, and the target hand-eye coordination evaluation data meets the preset condition. According to the method, the hand-eye coordination ability is quantified in real time based on the beta distribution parameters, the limitation of traditional single index evaluation is broken through, the method has high applicability, and ability training of different groups on different tasks can be met.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a hand-eye coordination evaluation method, device, electronic equipment and storage medium. Background Art

[0002] With the development of modern society, higher and higher requirements are placed on the hand-eye coordination ability of drivers, pilots and other professionals. Whether in a complex and changeable driving environment or in a high-altitude flight mission, quickly and accurately processing visual information and making corresponding operational responses is one of the key factors to ensure safety and improve efficiency. However, on the one hand, traditional hand-eye coordination training methods mainly focus on the training of abnormal people, and the tasks are simple and single (for example: the hand-eye coordination training method is to throw the ball along certain trajectories, and the trainee needs to make judgments and catch it in time); on the other hand, the training methods in the field of sports often focus on special skills, such as the reaction speed of receiving and serving. This kind of training usually emphasizes special skills rather than cognitive abilities, and is very dependent on experienced trainers and coaches.

[0003] In summary, the disadvantage of traditional hand-eye coordination training methods is that they cannot directly target cognitive training, that is, brain training, lack a personalized adjustment mechanism, lack generalization, and are difficult to meet the ability training needs of different groups on different tasks. Summary of the invention

[0004] The present invention provides a hand-eye coordination evaluation method, device, electronic device and storage medium, which are used to solve the defects of the prior art in brain training, lack of personalized adjustment mechanism, insufficient generalization, and difficulty in satisfying the ability training of different groups on different tasks. The present invention realizes real-time quantification of hand-eye coordination ability based on Beta distribution parameters, breaks through the limitation of traditional single indicator evaluation, has high applicability, and can satisfy the ability training of different groups on different tasks.

[0005] The present invention provides a hand-eye coordination evaluation method, comprising the following steps.

[0006] S1. Determine a plurality of sampling points according to the first beta distribution parameter; S2. randomly displaying the plurality of sampling points on the screen according to the display durations corresponding to the plurality of sampling points; S3, determining hand-eye coordination evaluation data corresponding to the user according to a click operation input by the user to the touch panel at the plurality of sampling points on the screen; S4. Determine target hand-eye coordination evaluation data according to the hand-eye coordination evaluation data and preset conditions, wherein the target hand-eye coordination evaluation data satisfies the preset conditions.

[0007] According to a hand-eye coordination evaluation method provided by the present invention, the target hand-eye coordination evaluation data is determined according to the hand-eye coordination evaluation data and preset conditions, including: S41, when the hand-eye coordination evaluation data meets the preset conditions, the hand-eye coordination evaluation data is determined as the target hand-eye coordination evaluation data; S42, when the hand-eye coordination evaluation data does not meet the preset conditions, a second beta distribution parameter is generated; and the second beta distribution parameter is determined as the new first beta distribution parameter; S43, the above steps S1-S3 are repeated until the hand-eye coordination evaluation data finally determined meets the preset conditions, and the hand-eye coordination evaluation data finally determined is determined as the target hand-eye coordination evaluation data.

[0008] According to a hand-eye coordination evaluation method provided by the present invention, the determining of the hand-eye coordination evaluation data corresponding to the user according to the click operation input by the user to the touchpad for the multiple sampling points in the screen includes: collecting the number of clicks, the number of correct clicks, and the click duration from explicit to clicked corresponding to each of the multiple sampling points according to the click operation input by the user to the touchpad for the multiple sampling points in the screen; determining the click accuracy rate according to all the click times and all the correct click times, and determining the average reaction duration according to all the correct click times and all the click durations; determining the click accuracy rate and the average reaction duration as the hand-eye coordination evaluation data; or, determining the force control stability level corresponding to each of the multiple partitions in the screen according to the click operation input by the user to the touchpad for the multiple sampling points in the screen; determining the difference between the maximum and minimum values ​​of the click accuracy rate, the average reaction duration, and the multiple force control stability levels as the hand-eye coordination evaluation data.

[0009] According to a hand-eye coordination evaluation method provided by the present invention, when the hand-eye coordination evaluation data is the click accuracy and the average reaction time, the preset condition includes a preset accuracy threshold and a preset time threshold; the hand-eye coordination evaluation data satisfies the preset condition if: the click accuracy is less than or equal to the preset accuracy threshold, and the average reaction time is less than or equal to the preset time threshold; when the hand-eye coordination evaluation data is the click accuracy, the average reaction time and the difference, the preset condition is the preset difference threshold; the hand-eye coordination evaluation data satisfies the preset condition if: the click accuracy is less than or equal to the preset accuracy threshold, the average reaction time is less than or equal to the preset time threshold, and the difference is less than or equal to the preset difference threshold.

[0010] According to a hand-eye coordination evaluation method provided by the present invention, the first Beta distribution parameter includes a first parameter and a second parameter, and generating the second Beta distribution parameter includes: when the hand-eye coordination evaluation data is the click accuracy rate and the average reaction time, if the click accuracy rate is greater than the preset accuracy rate threshold and the average reaction time is greater than the preset time threshold, then increasing the first parameter and the second parameter by the same first preset step size respectively to obtain the second Beta distribution parameter; when the hand-eye coordination evaluation data is the click accuracy rate, the average reaction time and the difference, if the click accuracy rate is greater than the preset accuracy rate threshold, the average reaction time is greater than the preset time threshold, and the difference is greater than the preset difference threshold, then increasing the first parameter or the second parameter by the second preset step size to obtain the second Beta distribution parameter.

[0011] According to a hand-eye coordination evaluation method provided by the present invention, the multiple sampling points are randomly displayed on the screen according to the explicit durations corresponding to each of the multiple sampling points, including: performing the following operations for each sampling point: determining the explicit duration corresponding to the sampling point according to the geometric parameters corresponding to the sampling point; and randomly displaying the sampling point on the screen according to the explicit duration.

[0012] According to a hand-eye coordination evaluation method provided by the present invention, determining the explicit duration corresponding to the sampling point based on the geometric parameters corresponding to the sampling point includes: when the sampling point is a circular pattern, the geometric parameter is the diameter data of the circular pattern, and determining the explicit duration corresponding to the sampling point based on the diameter data.

[0013] The present invention also provides a hand-eye coordination assessment device, comprising the following modules: The visual information presentation module is used for: S1, determining a plurality of sampling points according to a first beta distribution parameter; S2, randomly displaying the plurality of sampling points on a screen according to the display durations corresponding to the plurality of sampling points; The data processing module is used for S3, determining the hand-eye coordination evaluation data corresponding to the user according to the click operation input by the user to the touch panel for the multiple sampling points in the screen; S4, determining the target hand-eye coordination evaluation data according to the hand-eye coordination evaluation data and preset conditions, and the target hand-eye coordination evaluation data meets the preset conditions.

[0014] 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, wherein when the processor executes the computer program, any of the above-mentioned hand-eye coordination evaluation methods is implemented.

[0015] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the hand-eye coordination assessment method as described above is implemented.

[0016] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the hand-eye coordination assessment method described above is implemented.

[0017] The hand-eye coordination evaluation method, device, electronic device and storage medium provided by the present invention, through S1, according to the first beta distribution parameter, determine multiple sampling points; S2, according to the explicit duration corresponding to each of the multiple sampling points, randomly display the multiple sampling points on the screen; S3, according to the click operation input by the user to the touch panel for the multiple sampling points on the screen, determine the hand-eye coordination evaluation data corresponding to the user; S4, according to the hand-eye coordination evaluation data and preset conditions, determine the target hand-eye coordination evaluation data, and the target hand-eye coordination evaluation data meets the preset conditions. The method quantifies the hand-eye coordination ability in real time based on the beta distribution parameters, breaks through the limitations of the traditional single indicator evaluation, has high applicability, and can meet the ability training of different groups on different tasks. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces 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 creative work.

[0019] Figure 1 It is a flow chart of the hand-eye coordination evaluation method provided by the present invention.

[0020] Figure 2a It is a schematic diagram of probability distribution curves corresponding to various probability distribution functions provided by the present invention.

[0021] Figure 2b Schematic diagram of a scene of the hand-eye coordination evaluation method provided by the present invention.

[0022] Figure 3 It is a structural schematic diagram of the hand-eye coordination evaluation device provided by the present invention.

[0023] Figure 4 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] First, an example is given to illustrate the application scenario of the hand-eye coordination evaluation method provided by the present invention.

[0026] Example 1: The hand-eye coordination evaluation method provided by the present invention can realize the training of professionals with high hand-eye coordination ability, especially drivers and pilots. Specifically, in driver training, the hand-eye coordination evaluation method can not only help novices master the necessary driving skills faster, but also improve the driving ability of experienced drivers by dynamically adjusting the training difficulty.

[0027] Example 2: The hand-eye coordination assessment method provided by the present invention can be applied to the training and rehabilitation of children with cognitive impairment and other groups. Since the parameter characteristics of the beta distribution can cover a wider range of difficulty, the trainees can restore and improve their hand-eye coordination ability in tasks of gradually increasing difficulty.

[0028] It should be noted that the Beta distribution, as a continuous probability distribution defined on the interval (0, 1), has the characteristics of flexibility and adjustability, so that it has unique advantages in describing and predicting uncertain events. By dynamically adjusting the Beta distribution parameters, the changing trend of hand-eye coordination ability can be estimated and predicted based on the user's performance (such as feedback data). This feature provides a theoretical basis for the hand-eye coordination evaluation method provided by the present invention.

[0029] In other words, both Example 1 and Example 2 can quantify hand-eye coordination ability in real time based on Beta distribution parameters, breaking through the limitations of traditional single indicator evaluation, and have high applicability, which can meet the ability training needs of different groups on different tasks.

[0030] Secondly, the hand-eye coordination evaluation method provided by the present invention is described in detail below.

[0031] Figure 1 is a flow chart of the hand-eye coordination evaluation method provided by the present invention, such as Figure 1 As shown, the method includes the following: Step 101: Determine a plurality of sampling points according to a first beta distribution parameter.

[0032] The first beta distribution parameters include: a first parameter α and a second parameter β.

[0033] It should be noted that by changing the first parameter α and the second parameter β, different probability distribution functions can be formed. Figure 2a , which is a schematic diagram of the probability distribution curves corresponding to the various probability distribution functions provided by the present invention. Figure 2a As can be seen from the figure, (1) the first parameter α controls the "weight" or "density" of the left tail (i.e., the side closer to 0) of the probability distribution. A larger first parameter α will shift the probability distribution curve to the right and make the probability distribution more concentrated around x = 1. In other words, increasing the first parameter α will cause the probability distribution to tend to higher values.

[0034] (2) The second parameter β controls the "weight" or "density" of the right tail (i.e., the side closer to 1) of the probability distribution. A larger second parameter β will shift the probability distribution curve to the left and make the probability distribution more concentrated around x = 0. This means that increasing the second parameter β will cause the probability distribution to tend to lower values.

[0035] For the multiple sampling points determined by the electronic device based on the first beta distribution parameters, the horizontal coordinates of these multiple sampling points are the horizontal coordinates randomly selected by the electronic device from the probability distribution corresponding to the first beta distribution parameters; the vertical coordinates of these multiple sampling points are the vertical coordinates sampled by the electronic device from the uniform distribution, providing data support for subsequent training of users.

[0036] Step 102: randomly display the multiple sampling points on the screen according to the display durations corresponding to the multiple sampling points.

[0037] After determining the multiple sampling points, the electronic device can determine the display durations corresponding to the multiple sampling points, and randomly display the corresponding sampling points on the screen according to the display durations. At this time, the user can put his hand on the touch pad provided by the hand operation interaction module in advance to form a "ready state" to reduce reaction delays and ensure the immediacy of operation, and after seeing the multiple sampling points on the screen, input the corresponding click operation to the touch pad.

[0038] Among them, the touchpad can be understood as a kind of operation interface.

[0039] Exemplarily, the explicit duration of each sampling point varies randomly between 0.5 seconds and 2.5 seconds.

[0040] In some embodiments, the electronic device randomly displays multiple sampling points on the screen according to the explicit durations corresponding to each of the multiple sampling points, which may include: performing the following operations for each sampling point: the electronic device determines the explicit duration corresponding to the sampling point according to the geometric parameters corresponding to the sampling point; the electronic device randomly displays the sampling point on the screen according to the explicit duration.

[0041] Since the size of the geometric parameter is positively correlated with the display duration, the electronic device can prompt the display duration of the sampling point according to the geometric parameter corresponding to the sampling point, and then randomly display the sampling point on the screen. For example, a larger sampling point may be assigned a longer display duration to ensure visibility, while a smaller sampling point may shorten the display duration to avoid visual congestion.

[0042] In some embodiments, the electronic device determines the explicit duration corresponding to the sampling point based on the geometric parameters corresponding to the sampling point, which may include: when the sampling point is a circular pattern, the geometric parameters are diameter data of the circular pattern, and the electronic device determines the explicit duration corresponding to the sampling point based on the diameter data.

[0043] That is to say, the electronic device can determine the explicit duration corresponding to the sampling point according to the diameter data of the circular pattern.

[0044] Optionally, when the sampling point is a regular polygon pattern, the geometric parameter is the distance between the center of the regular polygon pattern and any vertex.

[0045] Exemplarily, the electronic device continuously presents 30 sampling points corresponding to the first beta parameter distribution on the screen in each round, and each sampling point corresponds to a circular pattern presented on the screen. The display duration is randomly displayed within 0.5 seconds to 2.5 seconds. The electronic device will record the random value, and the presentation duration of the circular pattern will be prompted according to the visual diameter data size of the circular pattern. The user puts his hand on the touchpad in advance, with the finger initially placed in the center of the touchpad. According to the visually presented circular image, he needs to click on a fixed position, and there will be a visual prompt after clicking correctly (such as the circular pattern changes from red to green), and continue to press until the group disappears. In other words, the electronic device can require the user to perform hand clicking operations according to the visually presented circular pattern, thereby realizing the training of hand-eye coordination.

[0046] Step 103 : determining hand-eye coordination evaluation data corresponding to the user according to the click operation inputted by the user to the touch panel at the plurality of sampling points on the screen.

[0047] After the user sees multiple sampling points on the screen, during the process of inputting the corresponding click operation to the touchpad, the user can click according to the position of the circular pattern provided by the screen. After a successful click, the circular pattern will change from red to green (in the advanced version, not only clicking is required, but also correct force control is required to be defined as correct) and keep pressing until the circular pattern disappears, and a corresponding click operation is generated in the electronic device to confirm the correctness of the operation and enhance the hand-eye coordination ability. The electronic device determines the hand-eye coordination evaluation data corresponding to the user based on the feedback information corresponding to the click operation.

[0048] Optionally, the feedback information may include click accuracy and average reaction time, and may also include force control stability.

[0049] Optionally, the user's hand pressing data is collected by a force tactile sensor, wherein the sampling frequency of the force tactile sensor is 100 Hz.

[0050] In some embodiments, the electronic device determines the hand-eye coordination evaluation data corresponding to the user according to the click operation input by the user to the touch panel at multiple sampling points on the screen, which may include one of the following implementation methods: Implementation method 1: The electronic device collects the number of clicks, the number of correct clicks, and the click duration from display to being clicked corresponding to each of the multiple sampling points according to the click operations input by the user to the touch pad at the multiple sampling points on the screen; the electronic device determines the click accuracy rate according to all the click numbers and all the correct click numbers, and determines the average reaction duration according to all the correct click numbers and all the click durations; the electronic device determines the click accuracy rate and the average reaction duration as hand-eye coordination evaluation data.

[0051] The electronic device collects the number of clicks corresponding to each of the multiple sampling points and the click duration from the display to the click of each of the multiple sampling points according to the click operation input by the user to the touch pad at the multiple sampling points in the screen; the electronic device then determines the correct number of clicks from the multiple click numbers; then, the electronic device uses the first formula to determine the click accuracy, and determines the average reaction time according to the second formula; and then determines the click accuracy and the average reaction time as hand-eye coordination evaluation data.

[0052] Among them, the first formula is: click accuracy = number of correct clicks / total number of clicks*100%.

[0053] The second formula is: average reaction time = total duration of all clicks / number of correct clicks.

[0054] It should be noted that click accuracy and average reaction time are used to estimate the difficulty of the current task.

[0055] Implementation method 2: The electronic device determines the force control stability corresponding to each of the multiple partitions in the screen according to the click operation input by the user to the touch pad at multiple sampling points in the screen; the electronic device determines the click accuracy, the average reaction time, and the difference between the maximum and minimum values ​​of the multiple force control stabilities as hand-eye coordination evaluation data.

[0056] Optionally, the multiple partitions in the screen are multiple average partitions (such as 5 partitions) in the horizontal direction of the screen.

[0057] The electronic device collects, for each partition, the variance of the time series values ​​corresponding to the partition recorded by the force tactile sensor according to the click operation input by the user to the touchpad at multiple sampling points in the screen, and determines the variance as the force control stability corresponding to the partition. That is, the third formula can be used: the force control stability corresponding to the partition = the variance of the time series values ​​corresponding to the partition. Based on this, the electronic device can determine the force control stability corresponding to each of the multiple partitions in the screen; and then determine the hand-eye coordination evaluation data in combination with the click accuracy and average reaction time determined in implementation method 1.

[0058] In summary, whether it is implementation method 1 or implementation method 2, by collecting user feedback data (such as click accuracy, average reaction time, and the stability of multiple force controls), it can provide detailed data support for subsequent analysis and help accurately evaluate the user's current training ability level, that is, it can make a highly accurate quantitative evaluation of the user's overall training performance.

[0059] Step 104: Determine target hand-eye coordination evaluation data according to the hand-eye coordination evaluation data and preset conditions, and the target hand-eye coordination evaluation data meets the preset conditions.

[0060] After determining the hand-eye coordination evaluation data, the electronic device may determine the target hand-eye coordination evaluation data according to whether the hand-eye coordination evaluation data meets the preset conditions, wherein if the target hand-eye coordination evaluation data meets the preset conditions, it indicates that the user's hand-eye coordination training effect is better.

[0061] In some embodiments, the electronic device determines the target hand-eye coordination evaluation data based on the hand-eye coordination evaluation data and preset conditions, which may include: S41. When the hand-eye coordination evaluation data meets the preset conditions, the electronic device determines the hand-eye coordination evaluation data as the target hand-eye coordination evaluation data; S42. When the hand-eye coordination evaluation data does not meet the preset conditions, the electronic device generates a second beta distribution parameter; and determines the second beta distribution parameter as a new first beta distribution parameter; S43. Repeat the above steps S1-S3 until the hand-eye coordination evaluation data finally determined meets the preset conditions, and determines the hand-eye coordination evaluation data finally determined as the target hand-eye coordination evaluation data.

[0062] In the process of determining the target hand-eye coordination evaluation data, the electronic device first determines whether the hand-eye coordination evaluation data meets the preset conditions. If so, it means that the user's hand-eye coordination training effect is better. At this time, the hand-eye coordination evaluation data can be directly determined as the target hand-eye coordination evaluation data. If not, it means that the user's hand-eye coordination training effect is not good. At this time, it is necessary to generate a second beta distribution parameter, and determine the second beta distribution parameter as the new first beta distribution parameter, and repeat the above steps S1-S3 until the hand-eye coordination evaluation data finally determined meets the preset conditions, and then the hand-eye coordination evaluation data finally determined is determined as the target hand-eye coordination evaluation data.

[0063] That is to say, if the hand-eye coordination evaluation data does not meet the preset conditions, the sampling points need to be updated and new hand-eye coordination training needs to be performed on the user.

[0064] It should be noted that, in the process of updating the sampling points according to the second beta distribution parameters, the horizontal coordinate of the new sampling point is the horizontal coordinate randomly drawn by the electronic device from the probability distribution corresponding to the second beta distribution parameters; the vertical coordinate of the new sampling point is the vertical coordinate sampled by the electronic device from the uniform distribution.

[0065] In some embodiments, when the hand-eye coordination evaluation data is click accuracy and average reaction time, the preset conditions include a preset accuracy threshold and a preset time threshold; the hand-eye coordination evaluation data satisfies the preset conditions if: the click accuracy is less than or equal to the preset accuracy threshold, and the average reaction time is less than or equal to the preset time threshold.

[0066] In some embodiments, when the hand-eye coordination evaluation data is click accuracy, average reaction time and difference, the preset condition is a preset difference threshold; the hand-eye coordination evaluation data satisfies the preset conditions if: the click accuracy is less than or equal to the preset accuracy threshold, the average reaction time is less than or equal to the preset time threshold, and the difference is less than or equal to the preset difference threshold.

[0067] Exemplarily, the preset accuracy threshold is 90%; the preset duration threshold is 400ms; and the preset difference threshold is 20%.

[0068] It should be noted that, among the multiple sampling points displayed on the screen, the larger the first parameter α is, the more the sampling points presented are to the right, and the larger the second parameter β is, the more the sampling points presented are to the left. If the hand-eye coordination evaluation data does not meet the preset conditions, then the following process can be used to generate the second beta distribution parameters. Specifically, it can include one of the following implementation methods: Implementation method 1: When the hand-eye coordination evaluation data is click accuracy and average reaction time, if the click accuracy is greater than a preset accuracy threshold and the average reaction time is greater than a preset time threshold, the electronic device increases the first parameter and the second parameter by the same first preset step size to obtain a second beta distribution parameter.

[0069] In the case where the hand-eye coordination evaluation data is click accuracy and average reaction time, if the click accuracy is greater than the preset accuracy threshold and the average reaction time is greater than the preset time threshold, it means that the click accuracy and the average reaction time are too high. At this time, the electronic device can increase the first parameter α and the second parameter β at the same time with a certain step size, that is, increase the first parameter α and the second parameter β by the same first preset step size b, respectively, to obtain the second beta distribution parameter. Among them, the second beta distribution parameter includes: a third parameter and a fourth parameter, the third parameter = α + b; the fourth parameter = β + b. The whole process can make multiple sampling points move to both ends.

[0070] Implementation method 2: When the hand-eye coordination evaluation data is click accuracy, average reaction time and difference, if the click accuracy is greater than a preset accuracy threshold, the average reaction time is greater than a preset time threshold, and the difference is greater than a preset difference threshold, the electronic device increases the first parameter or the second parameter by a second preset step size to obtain a second beta distribution parameter.

[0071] Implementation 2 is based on Implementation 1. If the difference is greater than the preset difference threshold, it means that if the force control stability is large in different partitions, the electronic device can increase the first parameter α or the second parameter β by the second preset step length c to obtain the second beta distribution parameter. Among them, the third parameter = α + c, and the fourth parameter = β + c.

[0072] It should be noted that the processing and updating of the first beta distribution parameters in the above implementation method 1 is mainly adapted to the difficulty change, and the processing and updating of the first beta distribution parameters in the above implementation method 2 is mainly adapted to the habit differences of individual hand-eye coordination control.

[0073] In the embodiment of the present application, S1, according to the first beta distribution parameter, multiple sampling points are determined; S2, according to the explicit duration corresponding to each of the multiple sampling points, the multiple sampling points are randomly displayed on the screen; S3, according to the click operation input by the user to the touchpad for the multiple sampling points on the screen, the hand-eye coordination evaluation data corresponding to the user is determined; S4, according to the hand-eye coordination evaluation data and the preset conditions, the target hand-eye coordination evaluation data is determined, and the target hand-eye coordination evaluation data meets the preset conditions. This method is based on the beta distribution parameters to quantify the hand-eye coordination ability in real time, breaking through the limitations of the traditional single indicator evaluation, and has high applicability, which can meet the ability training of different groups on different tasks.

[0074] Figure 2b Schematic diagram of the scene of the hand-eye coordination evaluation method provided by the present invention. Figure 3 As shown, the electronic device may include: a visual information presentation module and a data processing module. The data processing module specifically includes: a hand operation interaction module, a data acquisition module, an ability evaluation module, a beta distribution parameter estimation module and a question update module.

[0075] The visual information presentation module is used to dynamically display multiple sampling points sampled according to the current beta distribution parameters on the screen. Each sampling point appears in the form of a circular pattern. The display duration of each sampling point varies randomly between 0.5 seconds and 2.5 seconds, and prompts the user based on the diameter data size of the circular pattern. By selecting different beta distribution parameters, the electronic device simulates visual processing tasks of different difficulty levels to enhance the user's attention, reaction speed and hand-eye coordination.

[0076] The hand operation interaction module is used for the user to place his finger in the center of the touchpad and click according to the circular pattern position provided by the visual information presentation module. After a successful click, the circular pattern will change from red to green and continue to be pressed until the pattern disappears, so as to confirm the correctness of the operation and enhance the hand-eye coordination ability. Among them, the hand operation interaction module integrates a force tactile sensor.

[0077] The data acquisition module is used to record various data during the user training process in real time, including but not limited to parameters such as the display duration and diameter data of the circular pattern, the time of each click operation, the data of the force tactile sensor in the hand operation interaction module, the synthesis accuracy, etc.

[0078] The capability evaluation module is used to quantitatively evaluate the overall performance of the user based on the information collected by the data collection module. Specifically, the first formula, the second formula and the third formula mentioned above can be used, and no further details are given here.

[0079] The Beta distribution parameter estimation module is used to update the first Beta parameter distribution, that is, to generate a second Beta parameter distribution.

[0080] The topic updating module is used to update the sampling points based on the second beta parameter distribution.

[0081] The entire process controls the difficulty and adaptability of individual training questions based on rigorous mathematical distribution. The difficulty can be controlled according to the duration of visual stimulation and the range of force used, and can be adapted to the training of various groups of people.

[0082] The hand-eye coordination evaluation device provided by the present invention is described below. The hand-eye coordination evaluation device described below and the hand-eye coordination evaluation method described above can be referenced to each other.

[0083] Figure 3is a schematic diagram of the structure of the hand-eye coordination evaluation device provided by the present invention, such as Figure 3 As shown, the device includes the following: The visual information presentation module 301 is used for: S1, determining a plurality of sampling points according to a first beta distribution parameter; S2, randomly displaying the plurality of sampling points on a screen according to the display durations corresponding to the plurality of sampling points; The data processing module 302 is used for S3, determining the hand-eye coordination evaluation data corresponding to the user according to the click operation input by the user to the touch panel for the multiple sampling points in the screen; S4, determining the target hand-eye coordination evaluation data according to the hand-eye coordination evaluation data and the preset conditions, and the target hand-eye coordination evaluation data meets the preset conditions.

[0084] Optionally, the data processing module 302 is specifically used for S41, when the hand-eye coordination evaluation data meets the preset condition, determining the hand-eye coordination evaluation data as the target hand-eye coordination evaluation data; S42, when the hand-eye coordination evaluation data does not meet the preset condition, generating a second beta distribution parameter; and determining the second beta distribution parameter as the new first beta distribution parameter; S43, repeating the above steps S1-S3 until the finally determined hand-eye coordination evaluation data meets the preset condition, and determining the finally determined hand-eye coordination evaluation data as the target hand-eye coordination evaluation data.

[0085] Optionally, the data processing module 302 is specifically used to collect the number of clicks, the number of correct clicks, and the click duration from display to being clicked corresponding to each of the multiple sampling points according to the click operations input by the user to the touchpad for the multiple sampling points in the screen; determine the click accuracy according to all the clicks and all the correct clicks, and determine the average reaction time according to all the correct clicks and all the click durations; determine the click accuracy and the average reaction time as the hand-eye coordination evaluation data; or, determine the force control stability corresponding to each of the multiple partitions in the screen according to the click operations input by the user to the touchpad for the multiple sampling points in the screen; determine the click accuracy, the average reaction time, and the difference between the maximum and minimum values ​​of the multiple force control stability degrees as the hand-eye coordination evaluation data.

[0086] Optionally, when the hand-eye coordination evaluation data is the click accuracy and the average reaction time, the preset condition includes a preset accuracy threshold and a preset time threshold; the hand-eye coordination evaluation data satisfies the preset condition if: the click accuracy is less than or equal to the preset accuracy threshold, and the average reaction time is less than or equal to the preset time threshold; when the hand-eye coordination evaluation data is the click accuracy, the average reaction time and the difference, the preset condition is a preset difference threshold; the hand-eye coordination evaluation data satisfies the preset condition if: the click accuracy is less than or equal to the preset accuracy threshold, the average reaction time is less than or equal to the preset time threshold, and the difference is less than or equal to the preset difference threshold.

[0087] Optionally, the first Beta distribution parameter includes a first parameter and a second parameter, and the data processing module 302 is specifically used to, when the hand-eye coordination evaluation data is the click accuracy and the average reaction time, if the click accuracy is greater than the preset accuracy threshold and the average reaction time is greater than the preset time threshold, then increase the first parameter and the second parameter by the same first preset step size to obtain the second Beta distribution parameter; when the hand-eye coordination evaluation data is the click accuracy, the average reaction time and the difference, if the click accuracy is greater than the preset accuracy threshold, the average reaction time is greater than the preset time threshold, and the difference is greater than the preset difference threshold, then increase the first parameter or the second parameter by the second preset step size to obtain the second Beta distribution parameter.

[0088] Optionally, the data processing module 302 is specifically configured to perform the following operations for each sampling point: determine the explicit duration corresponding to the sampling point according to the geometric parameters corresponding to the sampling point; and randomly display the sampling point on the screen according to the explicit duration.

[0089] Optionally, the data processing module 302 is specifically used to determine the explicit duration corresponding to the sampling point according to the diameter data when the sampling point is a circular pattern and the geometric parameter is diameter data of the circular pattern.

[0090] Figure 4 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 4As shown, the electronic device may include: a processor 410, a communication interface 420, a memory 430 and a communication bus 440, wherein the processor 410, the communication interface 420 and the memory 430 complete mutual communication through the communication bus 440. The processor 410 may call the logic instructions in the memory 430 to execute the hand-eye coordination evaluation method, which includes: S1, determining a plurality of sampling points according to the first beta distribution parameter; S2, randomly displaying the plurality of sampling points on the screen according to the explicit durations corresponding to the plurality of sampling points; S3, determining the hand-eye coordination evaluation data corresponding to the user according to the click operation inputted by the user to the touch panel for the plurality of sampling points on the screen; S4, determining the target hand-eye coordination evaluation data according to the hand-eye coordination evaluation data and the preset conditions, and the target hand-eye coordination evaluation data meets the preset conditions.

[0091] In addition, the logic instructions in the above-mentioned memory 430 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on this understanding, the technical solution of the present invention, in essence, 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, and the computer software product is stored in a storage medium, including a number of instructions for 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 method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0092] On the other hand, the present invention also provides a computer program product, which includes a computer program, which can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the hand-eye coordination evaluation method provided by the above-mentioned methods, and the method includes: S1. Determine multiple sampling points according to a first beta distribution parameter; S2. Randomly display the multiple sampling points on the screen according to the explicit duration corresponding to each of the multiple sampling points; S3. Determine the hand-eye coordination evaluation data corresponding to the user according to the click operation input by the user to the touch panel for the multiple sampling points on the screen; S4. Determine the target hand-eye coordination evaluation data according to the hand-eye coordination evaluation data and preset conditions, and the target hand-eye coordination evaluation data meets the preset conditions.

[0093] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the hand-eye coordination evaluation method provided by the above-mentioned methods, the method comprising: S1, determining a plurality of sampling points according to a first beta distribution parameter; S2, randomly displaying the plurality of sampling points on a screen according to the explicit durations corresponding to each of the plurality of sampling points; S3, determining hand-eye coordination evaluation data corresponding to the user according to a click operation input by the user to a touch pad for the plurality of sampling points on the screen; S4, determining target hand-eye coordination evaluation data according to the hand-eye coordination evaluation data and preset conditions, the target hand-eye coordination evaluation data satisfying the preset conditions.

[0094] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0095] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0096] 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 embodiments of the present invention.

Claims

1. A hand-eye coordination assessment method, characterized in that: include: S1. Determine a plurality of sampling points according to the first beta distribution parameter; S2. randomly displaying the plurality of sampling points on the screen according to the display durations corresponding to the plurality of sampling points; S3, determining hand-eye coordination evaluation data corresponding to the user according to a click operation input by the user to the touch panel at the plurality of sampling points on the screen; S4. Determine target hand-eye coordination evaluation data according to the hand-eye coordination evaluation data and preset conditions, wherein the target hand-eye coordination evaluation data satisfies the preset conditions.

2. The hand-eye coordination assessment method according to claim 1, characterized in that: Determining target hand-eye coordination evaluation data according to the hand-eye coordination evaluation data and preset conditions includes: S41, if the hand-eye coordination evaluation data meets the preset condition, determining the hand-eye coordination evaluation data as the target hand-eye coordination evaluation data; S42: if the hand-eye coordination evaluation data does not meet the preset condition, generate a second beta distribution parameter; and determine the second beta distribution parameter as a new first beta distribution parameter; S43, repeatedly executing the above steps S1-S3 until the finally determined hand-eye coordination evaluation data meets the preset condition, and determining the finally determined hand-eye coordination evaluation data as the target hand-eye coordination evaluation data.

3. The hand-eye coordination evaluation method according to claim 2, characterized in that: The step of determining the hand-eye coordination evaluation data corresponding to the user according to the click operation inputted by the user to the touch panel at the plurality of sampling points on the screen comprises: According to the click operation inputted by the user to the touchpad at the multiple sampling points in the screen, the number of clicks, the number of correct clicks, and the click duration from display to being clicked corresponding to each of the multiple sampling points are collected; the click accuracy is determined according to all the clicks and all the correct clicks, and the average reaction duration is determined according to all the correct clicks and all the click durations; the click accuracy and the average reaction duration are determined as the hand-eye coordination evaluation data; or, According to the click operation input by the user to the touch pad at the multiple sampling points in the screen, the force control stability corresponding to each of the multiple partitions in the screen is determined; the click accuracy, the average reaction time, and the difference between the maximum and minimum values ​​of the multiple force control stability levels are determined as the hand-eye coordination evaluation data.

4. The hand-eye coordination evaluation method according to claim 3, characterized in that: In the case where the hand-eye coordination evaluation data is the click accuracy rate and the average reaction time, the preset condition includes a preset accuracy rate threshold and a preset time threshold; the hand-eye coordination evaluation data satisfies the preset condition if: the click accuracy rate is less than or equal to the preset accuracy rate threshold, and the average reaction time is less than or equal to the preset time threshold; When the hand-eye coordination evaluation data is the click accuracy, the average reaction time and the difference, the preset condition is a preset difference threshold; the hand-eye coordination evaluation data satisfies the preset condition if: the click accuracy is less than or equal to the preset accuracy threshold, the average reaction time is less than or equal to the preset time threshold, and the difference is less than or equal to the preset difference threshold.

5. The hand-eye coordination evaluation method according to claim 4, characterized in that: The first Beta distribution parameter includes a first parameter and a second parameter, and generating the second Beta distribution parameter includes: In a case where the hand-eye coordination evaluation data is the click accuracy and the average reaction time, if the click accuracy is greater than the preset accuracy threshold and the average reaction time is greater than the preset time threshold, the first parameter and the second parameter are respectively increased by the same first preset step size to obtain the second beta distribution parameter; When the hand-eye coordination evaluation data is the click accuracy, the average reaction time and the difference, if the click accuracy is greater than the preset accuracy threshold, the average reaction time is greater than the preset time threshold, and the difference is greater than the preset difference threshold, the first parameter or the second parameter is increased by a second preset step size to obtain the second beta distribution parameter.

6. The hand-eye coordination evaluation method according to any one of claims 1 to 5, characterized in that: The step of randomly displaying the plurality of sampling points on the screen according to the display durations corresponding to the plurality of sampling points includes: The following operations are performed for each sampling point: Determining the explicit duration corresponding to the sampling point according to the geometric parameters corresponding to the sampling point; The sampling points are randomly displayed on the screen according to the display duration.

7. The hand-eye coordination assessment method according to claim 6, characterized in that: The determining, according to the geometric parameters corresponding to the sampling points, the explicit duration corresponding to the sampling points, includes: In the case where the sampling point is a circular pattern, the geometric parameter is diameter data of the circular pattern, and the explicit duration corresponding to the sampling point is determined according to the diameter data.

8. A hand-eye coordination assessment device, characterized in that: include: A visual information presentation module, used to determine a plurality of sampling points according to a first beta distribution parameter; Randomly displaying the plurality of sampling points on the screen according to the display durations corresponding to the plurality of sampling points; A data processing module is used to determine the hand-eye coordination evaluation data corresponding to the user according to the click operation input by the user to the touch panel for the multiple sampling points in the screen; and determine the target hand-eye coordination evaluation data according to the hand-eye coordination evaluation data and preset conditions, and the target hand-eye coordination evaluation data meets the preset conditions.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the hand-eye coordination assessment method according to any one of claims 1 to 7 is implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the hand-eye coordination assessment method according to any one of claims 1 to 7 is implemented.

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