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

Abstract

This invention provides a method, apparatus, electronic device, and storage medium for assessing hand-eye coordination. The method includes: S1, determining multiple sampling points based on a first beta distribution parameter; S2, randomly displaying the multiple sampling points on a screen according to their respective explicit durations; S3, determining hand-eye coordination assessment data corresponding to the user based on click operations input by the user to a touchpad for the multiple sampling points on the screen; and S4, determining target hand-eye coordination assessment data based on the hand-eye coordination assessment data and preset conditions, wherein the target hand-eye coordination assessment data satisfies the preset conditions. This method quantifies hand-eye coordination ability in real time based on beta distribution parameters, breaking through the limitations of traditional single-indicator assessments, and has high applicability, capable of meeting the ability training needs of different groups on different tasks.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method, apparatus, electronic device, and storage medium for assessing hand-eye coordination. Background Technology

[0002] With the development of modern society, increasingly higher demands are being placed on the hand-eye coordination abilities of professionals such as drivers and pilots. Whether in complex and ever-changing driving environments or high-altitude flight missions, the ability to quickly and accurately process visual information and make corresponding operational responses is a key factor in ensuring safety and improving efficiency. However, traditional hand-eye coordination training methods primarily focus on training individuals with disabilities, with simple and repetitive tasks (e.g., throwing a ball along certain trajectories, requiring the trainee to make a timely judgment and catch it). Furthermore, training methods in the sports field often focus on specific skills, such as reaction speed in receiving serves. This type of training typically emphasizes specific skills rather than cognitive abilities and heavily relies on experienced trainers and coaches.

[0003] In summary, the shortcomings of traditional hand-eye coordination training methods are that they cannot directly target cognitive training, i.e., training of the brain, lack personalized adjustment mechanisms, have insufficient generalization, and are difficult to meet the ability training needs of different groups on different tasks. Summary of the Invention

[0004] This invention provides a hand-eye coordination assessment method, device, electronic device, and storage medium to address the shortcomings of existing brain training methods, such as the lack of personalized adjustment mechanisms, insufficient generalization, and difficulty in meeting the ability training needs of different groups on different tasks. It achieves real-time quantification of hand-eye coordination ability based on beta distribution parameters, breaks through the limitations of traditional single-index assessment, has high applicability, and can meet the ability training needs of different groups on different tasks.

[0005] This invention provides a method for assessing hand-eye coordination, comprising the following steps.

[0006] S1. Determine multiple sampling points based on the first beta distribution parameters;

[0007] S2. Randomly display the multiple sampling points on the screen according to the explicit duration corresponding to each of the multiple sampling points;

[0008] S3. Based on the user's click operation input to the touchpad for the multiple sampling points on the screen, determine the user's corresponding hand-eye coordination evaluation data;

[0009] S4. Based on the hand-eye coordination assessment data and preset conditions, determine the target hand-eye coordination assessment data, wherein the target hand-eye coordination assessment data satisfies the preset conditions.

[0010] According to a hand-eye coordination assessment method provided by the present invention, the step of determining target hand-eye coordination assessment data based on the hand-eye coordination assessment data and preset conditions includes: S41, if the hand-eye coordination assessment data meets the preset conditions, determining the hand-eye coordination assessment data as the target hand-eye coordination assessment data; S42, if the hand-eye coordination assessment data does not meet the preset conditions, generating a second beta distribution parameter; and determining the second beta distribution parameter as a new first beta distribution parameter; S43, repeating the above steps S1-S3 until the finally determined hand-eye coordination assessment data meets the preset conditions, and determining the finally determined hand-eye coordination assessment data as the target hand-eye coordination assessment data.

[0011] According to a hand-eye coordination assessment method provided by the present invention, the step of determining the hand-eye coordination assessment data corresponding to the user based on the user's click operations input to the touchpad for multiple sampling points on the screen includes: collecting the number of clicks, the number of correct clicks, and the click duration from display to being clicked for each of the multiple sampling points based on the user's click operations input to the touchpad for multiple sampling points on the screen; determining the click accuracy rate based on all clicks and all correct clicks, and determining the average reaction time based on all correct clicks and all click durations; determining the click accuracy rate and the average reaction time as the hand-eye coordination assessment data; or, determining the force control stability level corresponding to each of the multiple partitions on the screen based on the user's click operations input to the touchpad for multiple sampling points on the screen; determining the click accuracy rate, the average reaction time, and the difference between the maximum and minimum values ​​among the multiple force control stability levels as the hand-eye coordination assessment data.

[0012] According to a hand-eye coordination assessment method provided by the present invention, when the hand-eye coordination assessment data consists of the click accuracy rate and the average reaction time, the preset conditions include a preset accuracy rate threshold and a preset reaction time threshold; the hand-eye coordination assessment data satisfying the preset conditions are: 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 reaction time threshold; when the hand-eye coordination assessment data consists of the click accuracy rate, the average reaction time, and the difference, the preset condition is a preset difference threshold; the hand-eye coordination assessment data satisfying the preset conditions are: the click accuracy rate is less than or equal to the preset accuracy threshold, the average reaction time is less than or equal to the preset reaction time threshold, and the difference is less than or equal to the preset difference threshold.

[0013] According to a hand-eye coordination assessment method provided by the present invention, the first beta distribution parameter includes a first parameter and a second parameter. Generating the second beta distribution parameter includes: when the hand-eye coordination assessment data consists of the click accuracy and the average reaction time, if the click accuracy is greater than a preset accuracy threshold and the average reaction time is greater than a preset reaction time threshold, then the first parameter and the second parameter are increased by the same first preset step size to obtain the second beta distribution parameter; when the hand-eye coordination assessment data consists of 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 reaction time threshold, and the difference is greater than a preset difference threshold, then the first parameter or the second parameter is increased by a second preset step size to obtain the second beta distribution parameter.

[0014] According to a hand-eye coordination assessment method provided by the present invention, the step of randomly displaying the plurality of sampling points on the screen according to the explicit duration corresponding to each sampling point includes: 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.

[0015] According to a hand-eye coordination assessment method provided by the present invention, the step of 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 the explicit duration corresponding to the sampling point is determined based on the diameter data.

[0016] The present invention also provides a hand-eye coordination assessment device, comprising the following modules:

[0017] The visual information presentation module is used for: S1, determining multiple sampling points based on the first beta distribution parameters; and S2, randomly displaying the multiple sampling points on the screen according to the explicit duration corresponding to each of the multiple sampling points.

[0018] The data processing module is used for: S3, determining the user's corresponding hand-eye coordination evaluation data based on the user's click operation input to the touchpad for the multiple sampling points on the screen; S4, determining target hand-eye coordination evaluation data based on the hand-eye coordination evaluation data and preset conditions, wherein the target hand-eye coordination evaluation data satisfies the preset conditions.

[0019] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the hand-eye coordination assessment method as described above.

[0020] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the hand-eye coordination assessment method as described above.

[0021] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the hand-eye coordination assessment method as described above.

[0022] The hand-eye coordination assessment method, device, electronic device, and storage medium provided by this invention, through the following steps: S1, determining multiple sampling points based on a first beta distribution parameter; S2, randomly displaying the multiple sampling points on a screen according to their respective explicit durations; S3, determining the user's corresponding hand-eye coordination assessment data based on the user's click operations on the touchpad for the multiple sampling points on the screen; S4, determining target hand-eye coordination assessment data based on the hand-eye coordination assessment data and preset conditions, wherein the target hand-eye coordination assessment data satisfies the preset conditions. This method quantifies hand-eye coordination ability in real time based on beta distribution parameters, breaking through the limitations of traditional single-indicator assessment, and has high applicability, capable of meeting the ability training needs of different groups on different tasks. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0024] Figure 1 This is a flowchart illustrating the hand-eye coordination assessment method provided by the present invention.

[0025] Figure 2a This is a schematic diagram of the probability distribution curves corresponding to the various probability distribution functions provided by this invention.

[0026] Figure 2b This is a schematic diagram of a scenario for the hand-eye coordination assessment method provided by the present invention.

[0027] Figure 3 This is a schematic diagram of the hand-eye coordination assessment device provided by the present invention.

[0028] Figure 4 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0030] First, an example is given to illustrate the application scenarios of the hand-eye coordination assessment method provided by this invention.

[0031] Example 1: The hand-eye coordination assessment method provided by this invention can be used to train professionals with high hand-eye coordination skills, especially drivers and pilots. Specifically, in driver training, this hand-eye coordination assessment method can not only help novices master the necessary driving skills more quickly, but also improve the driving ability of experienced drivers by dynamically adjusting the training difficulty.

[0032] Example 2: The hand-eye coordination assessment method provided by this invention can be applied to the training and rehabilitation of people such as children with cognitive impairment. Because the parameter characteristics of the beta distribution can cover a wide range of difficulty, it enables trainees to recover and improve their hand-eye coordination ability in tasks with progressively increasing difficulty.

[0033] It should be noted that the beta distribution, as a continuous probability distribution defined on the interval (0, 1), possesses characteristics such as flexibility and adjustability, giving it 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 user performance (such as feedback data). This characteristic provides the theoretical basis for the hand-eye coordination assessment method provided in this invention.

[0034] 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-index evaluation, and have high applicability, which can meet the ability training needs of different groups on different tasks.

[0035] Secondly, the hand-eye coordination assessment method provided by this invention will be described in detail below.

[0036] Figure 1 This is a flowchart illustrating the hand-eye coordination assessment method provided by the present invention, as shown below. Figure 1 As shown, the method includes the following:

[0037] Step 101: Determine multiple sampling points based on the first beta distribution parameters.

[0038] The first beta distribution parameters include: the first parameter α and the second parameter β.

[0039] It should be noted that different probability distribution functions can be formed by changing the first parameter α and the second parameter β. For example... Figure 2a The diagram shown is a schematic representation of the probability distribution curves corresponding to the various probability distribution functions provided by this invention. From... Figure 2a As can be seen from this, (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 a higher value.

[0040] (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 near x=0. This means that increasing the second parameter β will cause the probability distribution to tend to a lower value.

[0041] For the multiple sampling points determined by the electronic device based on the first beta distribution parameter, the x-coordinate of these multiple sampling points is the x-coordinate randomly sampled by the electronic device from the probability distribution corresponding to the first beta distribution parameter; the y-coordinate of these multiple sampling points is the y-coordinate sampled by the electronic device from a uniform distribution, providing data support for subsequent user training.

[0042] Step 102: Randomly display multiple sampling points on the screen according to their respective explicit durations.

[0043] After determining multiple sampling points, the electronic device can determine the display duration corresponding to each sampling point and randomly display the corresponding sampling points on the screen according to their respective display durations. At this time, the user can place their hand on the touchpad provided by the hand operation interaction module in advance to form a "ready state," reducing response delay and ensuring the immediacy of operation. After seeing the multiple sampling points on the screen, the user can input the corresponding click operation to the touchpad.

[0044] The touchpad can be understood as a user interface.

[0045] For example, the explicit duration of each sampling point varies randomly between 0.5 seconds and 2.5 seconds.

[0046] In some embodiments, the electronic device randomly displays multiple sampling points on the screen according to the explicit duration corresponding to each sampling point. This may include performing the following operations for each sampling point: the electronic device determines the explicit duration corresponding to the sampling point based on the geometric parameters corresponding to the sampling point; the electronic device randomly displays the sampling point on the screen according to the explicit duration.

[0047] Since the magnitude of the geometric parameters is positively correlated with the display duration, electronic devices can indicate the display duration of a sampling point based on the corresponding geometric parameters, and then randomly display that sampling point on the screen. For example, larger sampling points may be allocated a longer display duration to ensure visibility, while smaller sampling points may have a shorter display duration to avoid visual congestion.

[0048] 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. This may include: when the sampling point is a circular pattern, the geometric parameter is the diameter data of the circular pattern, and the electronic device determines the explicit duration corresponding to the sampling point based on the diameter data.

[0049] In other words, electronic devices can determine the explicit duration corresponding to a sampling point based on the diameter data of a circular pattern.

[0050] Optionally, when the sampling points are regular polygonal patterns, the geometric parameter is the distance from the center of the regular polygonal pattern to any vertex.

[0051] For example, the electronic device continuously presents 30 sampling points corresponding to the first beta parameter distribution on the screen in each round. Each sampling point corresponds to a circular pattern on the screen, and the display duration is randomly displayed within 0.5 seconds to 2.5 seconds. The electronic device records the random values, and the display duration of the circular pattern is indicated by the visual diameter data of the circular pattern. The user first places their hand on the touchpad, with their finger initially in the center of the touchpad. Based on the visually presented circular image, they need to click on a fixed position, and after clicking correctly, there is a visual cue (such as the circular pattern changing from red to green), and they continue pressing until the circle disappears. In other words, the electronic device can require the user to perform hand click operations based on the visually presented circular pattern, thereby achieving hand-eye coordination training.

[0052] Step 103: Based on the user's click operations on the touchpad for multiple sampling points on the screen, determine the corresponding hand-eye coordination assessment data for the user.

[0053] After seeing multiple sampling points on the screen, the user can input corresponding click operations onto the touchpad. During this process, the user can click according to the position of the circular pattern provided on the screen. Upon successful click, the circular pattern changes from red to green (in advanced versions, not only is a click required, but also correct pressure control is necessary for a successful click). The user must continue pressing until the circular pattern disappears, and the corresponding click operation is generated in the electronic device to confirm the correctness of the operation and enhance hand-eye coordination. Based on the feedback information corresponding to this click operation, the electronic device determines the user's corresponding hand-eye coordination assessment data.

[0054] Optionally, feedback information may include click accuracy and average response time, as well as the stability of force control.

[0055] Optionally, the user's hand pressure data is acquired by a force tactile sensor, wherein the sampling frequency of the force tactile sensor is 100Hz.

[0056] In some embodiments, the electronic device determines the user's hand-eye coordination assessment data based on the user's click operations input to the touchpad at multiple sampling points on the screen, which may include one of the following implementation methods:

[0057] Implementation Method 1: The electronic device collects the number of clicks, the number of correct clicks, and the click duration from the time the click is displayed to the time the user inputs clicks to multiple sampling points on the screen onto the touchpad. Based on all click counts and all correct click counts, the electronic device determines the click accuracy rate, and based on all correct click counts and all click durations, it determines the average reaction time. The electronic device uses the click accuracy rate and average reaction time as hand-eye coordination evaluation data.

[0058] The electronic device collects the number of clicks corresponding to each of the multiple sampling points on the screen and the click duration from the time the click is displayed to the time the click is made, based on the user's click operations on the touchpad. The electronic device then determines the correct number of clicks from the multiple click counts. Then, the electronic device uses a first formula to determine the click accuracy rate and a second formula to determine the average reaction time. Finally, the click accuracy rate and the average reaction time are used as hand-eye coordination evaluation data.

[0059] The first formula is: Click accuracy = Number of correct clicks / Total number of clicks 100%.

[0060] The second formula is: Average reaction time = Total duration of all clicks / Number of correct clicks.

[0061] It should be noted that click accuracy and average reaction time are used to estimate the difficulty of the current task.

[0062] Implementation Method 2: The electronic device determines the force control stability of each of the multiple zones on the screen based on the user's click operations input to the touchpad at multiple sampling points on the screen; the electronic device determines the click accuracy, average reaction time, and the difference between the maximum and minimum values ​​among the multiple force control stability as hand-eye coordination evaluation data.

[0063] Optionally, multiple partitions in the screen can be multiple average partitions of the screen in the horizontal direction (e.g., 5 partitions).

[0064] The electronic device, based on the user's click operations input to the touchpad at multiple sampling points on the screen, collects the variance of the time series values ​​corresponding to each zone recorded by the force haptic sensor, and determines the force control stability of the corresponding zone using the third formula: Force control stability of the corresponding zone = Variance of the time series values ​​corresponding to the corresponding zone. Based on this, the electronic device can determine the force control stability of each of the multiple zones on the screen; and then, combined with the click accuracy and average reaction time determined in implementation method 1, determines the hand-eye coordination evaluation data.

[0065] In summary, regardless of whether it is implementation method 1 or implementation method 2, by collecting user feedback data (such as click accuracy, average reaction time, and stability of multiple force controls), detailed data support can be provided for subsequent analysis, helping to accurately assess the user's current training ability level, that is, to conduct a highly accurate quantitative assessment of the user's overall training performance.

[0066] Step 104: Based on the hand-eye coordination assessment data and preset conditions, determine the target hand-eye coordination assessment data, and the target hand-eye coordination assessment data meets the preset conditions.

[0067] After determining the hand-eye coordination assessment data, the electronic device can determine the target hand-eye coordination assessment data based on whether the hand-eye coordination assessment data meets the preset conditions. If the target hand-eye coordination assessment data meets the preset conditions, it indicates that the user's hand-eye coordination training effect is good.

[0068] In some embodiments, the electronic device determines target hand-eye coordination assessment data based on hand-eye coordination assessment data and preset conditions, which may include: S41, if the hand-eye coordination assessment data meets the preset conditions, the electronic device determines the hand-eye coordination assessment data as the target hand-eye coordination assessment data; S42, if the hand-eye coordination assessment 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, repeating the above steps S1-S3 until the finally determined hand-eye coordination assessment data meets the preset conditions, and then determining the finally determined hand-eye coordination assessment data as the target hand-eye coordination assessment data.

[0069] In the process of determining the target hand-eye coordination assessment data, the electronic device first determines whether the hand-eye coordination assessment data meets the preset conditions. If it does, it means that the user's hand-eye coordination training effect is good. At this time, the hand-eye coordination assessment data can be directly determined as the target hand-eye coordination assessment data. If it does not meet the conditions, it means that the user's hand-eye coordination training effect is poor. 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. The above steps S1-S3 are repeated until the finally determined hand-eye coordination assessment data meets the preset conditions. Then, the finally determined hand-eye coordination assessment data is determined as the target hand-eye coordination assessment data.

[0070] In other words, if the hand-eye coordination assessment 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.

[0071] It should be noted that, in the process of updating the sampling points according to the second beta distribution parameters, the x-coordinate of the new sampling point is the x-coordinate randomly selected by the electronic device from the probability distribution corresponding to the second beta distribution parameters; the y-coordinate of the new sampling point is the y-coordinate sampled by the electronic device from a uniform distribution.

[0072] In some embodiments, when the hand-eye coordination evaluation data are click accuracy and average reaction time, the preset conditions include a preset accuracy threshold and a preset reaction time threshold; the hand-eye coordination evaluation data meets the preset conditions as follows: 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 reaction time threshold.

[0073] In some embodiments, when the hand-eye coordination evaluation data are click accuracy, average reaction time, and difference, the preset condition is a preset difference threshold; the hand-eye coordination evaluation data meets the preset condition as follows: click accuracy is less than or equal to a preset accuracy threshold, average reaction time is less than or equal to a preset reaction time threshold, and difference is less than or equal to a preset difference threshold.

[0074] For example, the preset accuracy threshold is 90%; the preset duration threshold is 400ms; and the preset difference threshold is 20%.

[0075] It should be noted that among the multiple sampling points displayed on the screen, the larger the first parameter α, the more to the right the sampling points are presented; the larger the second parameter β, the more to the left the sampling points are presented. If the hand-eye coordination evaluation data does not meet the preset conditions, the second beta distribution parameters can be generated using the following process. Specifically, it can include one of the following implementation methods:

[0076] Implementation Method 1: When the hand-eye coordination evaluation data are 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 reaction time threshold, the electronic device will increase the first parameter and the second parameter by the same first preset step size to obtain the second beta distribution parameter.

[0077] When the hand-eye coordination evaluation data consists of click accuracy and average reaction time, if the click accuracy exceeds a preset accuracy threshold and the average reaction time exceeds a preset reaction time threshold, it indicates that both the click accuracy and average reaction time are too high. In this case, the electronic device can simultaneously increase the first parameter α and the second parameter β by a certain step size, that is, increase the first parameter α and the second parameter β by the same first preset step size b, to obtain the second beta distribution parameters. The second beta distribution parameters include a third parameter and a fourth parameter, where the third parameter = α + b; and the fourth parameter = β + b. This entire process allows multiple sampling points to move towards both ends.

[0078] Implementation Method 2: When the hand-eye coordination evaluation data are click accuracy, average reaction time and difference, if the click accuracy is greater than the preset accuracy threshold, the average reaction time is greater than the preset reaction time threshold, and the difference is greater than the preset difference threshold, the electronic device will increase the first parameter or the second parameter by a second preset step size to obtain the second beta distribution parameter.

[0079] Implementation Method 2 builds upon Implementation Method 1. If the difference exceeds a preset difference threshold, it indicates a significant deviation in force control stability across different zones. In this case, the electronic device can increase either the first parameter α or the second parameter β by a second preset step size c to obtain the second beta distribution parameter. The third parameter is defined as α + c, and the fourth parameter as β + c.

[0080] It should be noted that the processing and updating of the first beta distribution parameter in the above implementation method 1 mainly adapts to changes in difficulty, while the processing and updating of the first beta distribution parameter in the above implementation method 2 mainly adapts to individual differences in hand-eye coordination control habits.

[0081] In this embodiment, S1, multiple sampling points are determined based on the first beta distribution parameter; S2, the multiple sampling points are randomly displayed on the screen according to their respective explicit durations; S3, the user's corresponding hand-eye coordination evaluation data is determined based on the user's click operations on the touchpad for the multiple sampling points on the screen; S4, target hand-eye coordination evaluation data is determined based on the hand-eye coordination evaluation data and preset conditions, wherein the target hand-eye coordination evaluation data meets the preset conditions. This method quantifies hand-eye coordination ability in real time based on beta distribution parameters, breaking through the limitations of traditional single-index evaluation, and has high applicability, capable of meeting the ability training needs of different groups on different tasks.

[0082] Figure 2b This is a schematic diagram of a scenario for the hand-eye coordination assessment method provided by this invention. For example... Figure 3 As shown, the electronic device may include a visual information presentation module and a data processing module. Specifically, the data processing module includes a hand-operated interaction module, a data acquisition module, a capability assessment module, a beta distribution parameter estimation module, and a question update module.

[0083] The visual information presentation module dynamically displays multiple sampling points on the screen based on the current beta distribution parameters. Each sampling point appears as a circular pattern, and the display duration of each sampling point randomly varies between 0.5 and 2.5 seconds. The module also provides user feedback based on the diameter of the circular pattern. By selecting different beta distribution parameters, the electronic device simulates visual processing tasks of varying difficulty levels, enhancing the user's attention, reaction speed, and hand-eye coordination.

[0084] The hand-operated interaction module allows users to place their fingers in the center of the touchpad and tap a circular pattern provided by the visual information presentation module. Upon successful tapping, the circular pattern changes from red to green, and the user must hold the button down until the pattern disappears, thus confirming the correctness of the operation and enhancing hand-eye coordination. This hand-operated interaction module integrates a force sensor.

[0085] The data acquisition module is used to record various data during the user's training process in real time, including but not limited to parameters such as the display duration and diameter of the circular pattern, the time of each click operation, data received from the force and tactile sensor in the hand operation interaction module, and the accuracy of the synthesis.

[0086] The capability assessment module, based on information collected by the data acquisition module, is used to quantitatively evaluate a user's overall performance. Specifically, it can employ the first, second, and third formulas mentioned above, which will not be elaborated upon here.

[0087] The beta distribution parameter estimation module is used to update the first beta parameter distribution, i.e., to generate the second beta parameter distribution.

[0088] The problem update module is used to update the sampling points based on the distribution of the second beta parameters.

[0089] The entire process is based on a rigorous mathematical distribution to control the difficulty and suitability of individual training questions. The difficulty can be controlled according to the duration of visual stimulus presentation and the range of effort exerted, making it suitable for training various groups of people.

[0090] The hand-eye coordination assessment device provided by the present invention is described below. The hand-eye coordination assessment device described below can be referred to in correspondence with the hand-eye coordination assessment method described above.

[0091] Figure 3This is a schematic diagram of the hand-eye coordination assessment device provided by the present invention, as shown below. Figure 3 As shown, the device includes the following:

[0092] The visual information presentation module 301 is used for: S1, determining multiple sampling points according to the first beta distribution parameters; S2, randomly displaying the multiple sampling points on the screen according to the explicit duration corresponding to each of the multiple sampling points.

[0093] The data processing module 302 is used for: S3, determining the user's corresponding hand-eye coordination evaluation data based on the user's click operation input to the touchpad for the multiple sampling points on the screen; S4, determining the target hand-eye coordination evaluation data based on the hand-eye coordination evaluation data and preset conditions, wherein the target hand-eye coordination evaluation data satisfies the preset conditions.

[0094] Optionally, the data processing module 302 is specifically configured to: S41, if the hand-eye coordination assessment data meets the preset condition, determine the hand-eye coordination assessment data as the target hand-eye coordination assessment data; S42, if the hand-eye coordination assessment data does not meet the preset condition, generate a second beta distribution parameter; and determine the second beta distribution parameter as the new first beta distribution parameter; S43, repeat the above steps S1-S3 until the finally determined hand-eye coordination assessment data meets the preset condition, and determine the finally determined hand-eye coordination assessment data as the target hand-eye coordination assessment data.

[0095] Optionally, the data processing module 302 is specifically configured to: collect the number of clicks, the number of correct clicks, and the click duration from display to being clicked for each of the multiple sampling points on the screen, based on the user's click operations input to the touchpad for the multiple sampling points on the screen; determine the click accuracy rate based on all clicks and all correct clicks, and determine the average reaction time based on all correct clicks and all click durations; and determine the click accuracy rate and the average reaction time as the hand-eye coordination evaluation data; or, determine the force control stability level corresponding to each of the multiple zones on the screen based on the user's click operations input to the touchpad for the multiple sampling points on the screen; and determine the click accuracy rate, the average reaction time, and the difference between the maximum and minimum values ​​among the multiple force control stability levels as the hand-eye coordination evaluation data.

[0096] Optionally, when the hand-eye coordination evaluation data consists of the click accuracy rate and the average reaction time, the preset condition includes a preset accuracy threshold and a preset reaction time threshold; the hand-eye coordination evaluation data satisfies the preset condition as follows: the click accuracy rate is less than or equal to the preset accuracy threshold, and the average reaction time is less than or equal to the preset reaction time threshold; when the hand-eye coordination evaluation data consists of the click accuracy rate, 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 as follows: the click accuracy rate is less than or equal to the preset accuracy threshold, the average reaction time is less than or equal to the preset reaction time threshold, and the difference is less than or equal to the preset difference threshold.

[0097] Optionally, the first beta distribution parameter includes a first parameter and a second parameter. 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 reaction time threshold, then the first parameter and the second parameter are 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 reaction time threshold, and the difference is greater than the preset difference threshold, then the first parameter or the second parameter is increased by a second preset step size to obtain the second beta distribution parameter.

[0098] Optionally, the data processing module 302 is specifically used to perform the following operations for each sampling point: determine the display duration corresponding to the sampling point based on the geometric parameters corresponding to the sampling point; and randomly display the sampling point on the screen according to the display duration.

[0099] Optionally, the data processing module 302 is specifically used to determine the explicit duration corresponding to the sampling point when the sampling point is a circular pattern, where the geometric parameter is the diameter data of the circular pattern.

[0100] Figure 4 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 4As shown, the electronic device may include: a processor 410, a communications interface 420, a memory 430, and a communication bus 440, wherein the processor 410, the communications interface 420, and the memory 430 communicate with each other through the communication bus 440. The processor 410 can call logical instructions in the memory 430 to execute a hand-eye coordination assessment method, which includes: S1, determining multiple sampling points according to a first beta distribution parameter; S2, randomly displaying the multiple sampling points on the screen according to the explicit duration corresponding to each of the multiple sampling points; S3, determining the hand-eye coordination assessment data corresponding to the user based on the click operation input by the user to the touchpad for the multiple sampling points on the screen; S4, determining target hand-eye coordination assessment data based on the hand-eye coordination assessment data and preset conditions, wherein the target hand-eye coordination assessment data satisfies the preset conditions.

[0101] Furthermore, the logical instructions in the aforementioned memory 430 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0102] On the other hand, the present invention also provides a computer program product, the computer program product including a computer program, the computer program being able to be stored on a non-transitory computer-readable storage medium, the computer program being executed by a processor, the computer being able to execute the hand-eye coordination assessment method provided by the above methods, the method including: S1, determining multiple sampling points according to a first beta distribution parameter; S2, randomly displaying the multiple sampling points on a screen according to the explicit duration corresponding to each of the multiple sampling points; S3, determining the hand-eye coordination assessment data corresponding to the user based on the click operation input by the user to the touchpad for the multiple sampling points on the screen; S4, determining target hand-eye coordination assessment data based on the hand-eye coordination assessment data and preset conditions, the target hand-eye coordination assessment data satisfying the preset conditions.

[0103] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the hand-eye coordination assessment method provided by the above 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 duration corresponding to each of the plurality of sampling points; S3, determining hand-eye coordination assessment data corresponding to the user based on a click operation input by the user to a touchpad for the plurality of sampling points on the screen; S4, determining target hand-eye coordination assessment data based on the hand-eye coordination assessment data and preset conditions, wherein the target hand-eye coordination assessment data satisfies the preset conditions.

[0104] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0105] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0106] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for assessing hand-eye coordination, characterized in that, include: S1. Determine multiple sampling points based on the first beta distribution parameter; wherein, the first beta distribution parameter includes a first parameter and a second parameter, the abscissa of the multiple sampling points is the abscissa randomly sampled from the probability distribution corresponding to the first beta distribution parameter, and the ordinate of the multiple sampling points is the ordinate sampled from a uniform distribution; S2. Randomly display the multiple sampling points on the screen according to the explicit duration corresponding to each of the multiple sampling points; S3. Based on the user's click operations input to the touchpad at the multiple sampling points on the screen, collect the number of clicks, the number of correct clicks, and the click duration from display to being clicked for each of the multiple sampling points; determine the click accuracy rate based on all click counts and all correct click counts, and determine the average reaction time based on all correct click counts and all click durations; determine the click accuracy rate and the average reaction time as the hand-eye coordination evaluation data; or, based on the user's click operations input to the touchpad at the multiple sampling points on the screen, determine the force control stability of each of the multiple partitions on the screen; determine the click accuracy rate, the average reaction time, and the difference between the maximum and minimum values ​​among the multiple force control stability rates as the hand-eye coordination evaluation data; S41. If the hand-eye coordination assessment data meets the preset conditions, the hand-eye coordination assessment data is determined as the target hand-eye coordination assessment data. S42. When the hand-eye coordination evaluation data are the click accuracy and the average reaction time, if the click accuracy is greater than a preset accuracy threshold and the average reaction time is greater than a preset reaction time threshold, then the first parameter and the second parameter are increased by the same first preset step size to obtain the second beta distribution parameter. When the hand-eye coordination evaluation data are 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 reaction time threshold, and the difference is greater than the preset difference threshold, then the first parameter or the second parameter is increased by a second preset step size to obtain the second beta distribution parameter. And the second beta distribution parameter is determined as the new first beta distribution parameter; S43. Repeat steps S1-S3 above until the final determined hand-eye coordination assessment data meets the preset conditions, and determine the final determined hand-eye coordination assessment data as the target hand-eye coordination assessment data. The click accuracy rate is calculated using a first formula: Click Accuracy Rate = Number of Correct Clicks / Total Number of Clicks 100%; The average reaction time is calculated by the second formula, which is: Average reaction time = Total duration of all clicks / Number of correct clicks; The force control stability of each of the multiple partitions is calculated by the third formula, which is: Force control stability of partition = Variance of time series value corresponding to partition.

2. The hand-eye coordination assessment method according to claim 1, characterized in that, When the hand-eye coordination evaluation data consists of the click accuracy and the average reaction time, the preset conditions include a preset accuracy threshold and a preset reaction time threshold; the hand-eye coordination evaluation data satisfies the preset conditions as follows: 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 reaction time threshold. When the hand-eye coordination evaluation data consists of 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 as follows: 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 reaction time threshold, and the difference is less than or equal to the preset difference threshold.

3. The hand-eye coordination assessment method according to claim 1 or 2, characterized in that, The step of randomly displaying the plurality of sampling points on the screen according to the explicit duration corresponding to each of the plurality of sampling points includes: Perform the following operations for each sampling point: The explicit duration corresponding to the sampling point is determined based on the geometric parameters corresponding to the sampling point. According to the specified display duration, the sampling points are randomly displayed on the screen.

4. The hand-eye coordination assessment method according to claim 3, characterized in that, The step of 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 the explicit duration corresponding to the sampling point is determined based on the diameter data.

5. A hand-eye coordination assessment device, characterized in that, include: A visual information presentation module is used to determine multiple sampling points based on a first beta distribution parameter; wherein the first beta distribution parameter includes a first parameter and a second parameter, the abscissa of the multiple sampling points is abscissa randomly sampled from the probability distribution corresponding to the first beta distribution parameter, and the ordinate of the multiple sampling points is ordinate sampled from a uniform distribution; and the multiple sampling points are randomly displayed on the screen according to the explicit duration corresponding to each of the multiple sampling points. The data processing module is used to collect the number of clicks, the number of correct clicks, and the click duration from display to being clicked for each of the multiple sampling points on the screen, based on the user's click operations input to the touchpad for multiple sampling points on the screen; determine the click accuracy rate based on all clicks and all correct clicks, and determine the average reaction time based on all correct clicks and all click durations; and determine the click accuracy rate and the average reaction time as the hand-eye coordination evaluation data; or, based on the user's click operations input to the touchpad for multiple sampling points on the screen, determine the force control stability level corresponding to each of the multiple partitions on the screen; determine the click accuracy rate, the average reaction time, and the difference between the maximum and minimum values ​​among the multiple force control stability levels as the hand-eye coordination evaluation data; if the hand-eye coordination evaluation data meets preset conditions, determine the hand-eye coordination evaluation data as the target hand-eye coordination evaluation data; and if the hand-eye coordination evaluation data is the click accuracy rate and the average reaction time... In the case where the click accuracy is greater than a preset accuracy threshold and the average reaction time is greater than a preset duration threshold, the first parameter and the second parameter are increased by the same first preset step size to obtain a second beta distribution parameter. When the hand-eye coordination evaluation data consists of 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 duration threshold, and the difference is greater than a preset difference threshold, the first parameter or the second parameter is increased by a second preset step size to obtain a second beta distribution parameter. The second beta distribution parameter is then determined as the new first beta distribution parameter. The process of determining the hand-eye coordination evaluation data is repeated until the finally determined hand-eye coordination evaluation data meets the preset conditions, and the finally determined hand-eye coordination evaluation data is determined as the target hand-eye coordination evaluation data. The click accuracy is calculated using a first formula: Click Accuracy = Number of Correct Clicks / Total Number of Clicks. 100%; The average reaction time is calculated by the second formula, which is: Average reaction time = Total duration of all clicks / Number of correct clicks; The force control stability of each partition in the multiple partitions is calculated by the third formula, which is: Force control stability of partition = Variance of time series value corresponding to partition.

6. 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, it implements the hand-eye coordination assessment method as described in any one of claims 1 to 4.

7. 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, it implements the hand-eye coordination assessment method as described in any one of claims 1 to 4.

Images