A visual fatigue detection method and device, electronic equipment and storage medium

Abstract

The application provides a visual fatigue detection method and device, electronic equipment and storage medium, and relates to the technical field of visual testing. The method comprises the following steps: obtaining first test data and second test data corresponding to a plurality of test persons; performing integral height processing on the test data within a preset viewing time period to obtain a corresponding integral height function; the integral height processing comprises the following steps: integrating the vital sign index monitoring data within the preset viewing time period, and calculating the ratio between the integral result and the preset viewing time period; performing inverse transformation based on the integral height function to obtain an ideal state function corresponding to the test data and excluding the influence of noise; and determining the visual fatigue detection speed based on the ideal state function corresponding to different test data. The application determines the visual fatigue speed based on the integral height function excluding the noise interference created based on the fatigue test data, eliminates the non-systematic error, and improves the accuracy of the visual fatigue speed.

Description

Technical Field

[0001] This application relates to the field of visual testing technology, and in particular to a method, apparatus, electronic device and storage medium for detecting visual fatigue. Background Technology

[0002] With the rapid development of technology and the increasing number of electronic products, people have more ways to read. However, with the increasing number of electronic products, the quality of their display devices cannot be guaranteed, which can cause certain harm to the eyes of users. In order to allow users to better understand the effect of the display device of the reading device they are using, it is necessary to conduct visual fatigue tests on the display device.

[0003] Most existing visual fatigue testing methods use direct test values ​​as the source data for processing. Due to differences in the testing environment, testing equipment, subjects, and testers, certain noise disturbances will occur during each test, resulting in different test errors in the final test values. Existing technologies mostly rely on increasing the number of test data samples to mask the errors. However, this method not only increases the user's cost, but also increases the complexity of the data processing process due to the increased number of test samples. Summary of the Invention

[0004] To facilitate understanding, we will first introduce the definitions of the product height function and the noise function.

[0005] The product height function is defined as follows:

[0006]

[0007] F(t) is the product height function of the function y(t).

[0008] Its inverse transformation is denoted as: Right now

[0009] y(t)=[F(t)×T] ′ =F(t)+F(t) ′ ×T

[0010] Preferably, the change of a state variable over time is tested, and the test result of the change of this state variable over time is defined as the test source function y(t).

[0011] Considering that the data in this test result is actually affected by noise, the test source function can be represented by the sum of the ideal state function g(t) and the noise function noise(t). That is: y(t) = g(t) + noise(t).

[0012] Specifically, in this application, the noise function noise(t) is defined with the following characteristics:

[0013] ① Finite randomness: The sub-noise function corresponding to each test noise fluctuates within a certain target value range, that is, the i-th sub-noise function noise i (t) = {-Δ, +Δ}, where Δ is a relatively small real number, and Δ represents the maximum noise detected by multiple test subjects during the preset viewing time period.

[0014] ② Self-cancellation: The sum of the sub-noise functions, i.e. the noise function noise(t), also fluctuates within the target range, and its value is a random value between {-Δ, +Δ}, i.e., -Δ≤noise(t)≤+Δ.

[0015] On the other hand, the ideal state function g(t) can be expressed as a power function:

[0016]

[0017] Next, we will introduce the accumulation height denoising method:

[0018] The test source function y(t) is composed of several discrete points; therefore, its product height function can be calculated using the following formula:

[0019]

[0020] Where ΔT is the time interval between two adjacent tests, and T is the total test time. In this application, T can be such that when Δt is sufficiently small and T is sufficiently large, ΔT / T can be ignored. According to property ② of the noise function, we have:

[0021]

[0022] C K It can be obtained from the power function fitting curve of the product height function F(t).

[0023] Therefore, according to C K =A K / (K+1), the coefficients of the ideal state function are:

[0024] A K = (K+1)×C K

[0025] It can be seen that by performing product height transformation or product height processing and then ignoring the noise terms, the ideal state function can be obtained as follows:

[0026]

[0027] In view of the background, the purpose of this application is to provide at least one method, device, electronic device and storage medium for visual fatigue detection, which determines visual fatigue speed by creating a product height function of fatigue test data, eliminates non-systematic errors and improves the accuracy of visual fatigue speed.

[0028] This application mainly includes the following aspects:

[0029] In a first aspect, embodiments of this application provide a method for detecting visual fatigue, the method comprising:

[0030] First and second test data were acquired from multiple subjects. The first test data consisted of vital sign monitoring data used to characterize the degree of visual fatigue when the subjects viewed a standard device within a preset viewing time period. The second test data consisted of vital sign monitoring data when the subjects viewed a test device within the preset viewing time period. The vital sign monitoring data included the vital sign index monitoring values ​​for each subject at each viewing duration within the preset viewing time period. The vital sign monitoring data indicated by the first test data were processed using an integral height calculation within the preset viewing time period to obtain the first integral height index value corresponding to each viewing duration. The integral height calculation included: integrating the vital sign monitoring data within the preset viewing time period and calculating the ratio between the integral result and the preset viewing time period; and performing an integral height calculation on the vital sign monitoring data indicated by the second test data within the preset viewing time period. The second cumulative height index value corresponding to each viewing duration is obtained. A first cumulative height function corresponding to the first test data is constructed by fitting the first cumulative height index value corresponding to each viewing duration, and a second cumulative height function corresponding to the second test data is constructed by fitting the second cumulative height index value corresponding to each viewing duration. The cumulative height function is used to characterize the mapping relationship between the cumulative height index value and the viewing duration. Based on the first ideal state function, the baseline vital sign index monitoring value at the baseline time is obtained. The first ideal state function is determined by the first cumulative height function. The ideal state function refers to a function that eliminates noise terms and is used to characterize the cumulative change of vital sign indicators with viewing time. Based on the second ideal state function, the comparison time corresponding to the baseline vital sign index monitoring value is obtained. The second ideal state function is determined by the second cumulative height function. Visual fatigue detection is performed based on the baseline time and the comparison time.

[0031] In one possible implementation, vital sign monitoring data is determined as follows: if the vital sign indicators are continuous measurements, the vital sign indicator monitoring values ​​generated by each subject within a preset viewing time period are collected at a preset collection frequency; the vital sign monitoring data is formed by the multiple vital sign indicator monitoring values ​​corresponding to each subject within the preset viewing time period and the viewing duration corresponding to each vital sign indicator monitoring value.

[0032] In one possible implementation, vital sign monitoring data is determined as follows: if the vital sign indicator is a non-continuous measurement, the preset viewing time period is divided into multiple viewing periods according to a preset time interval; according to the number of viewing periods, multiple test subjects are divided into multiple test groups, and multiple test groups are assigned one-to-one to multiple viewing periods to watch tests on standard equipment or test equipment, with each test group having an equal number of people; for each test subject, the corresponding vital sign indicator monitoring value is collected during the viewing period associated with the test group to which the test subject belongs; the vital sign monitoring data is formed by the corresponding vital sign indicator monitoring value for each test subject and the viewing duration corresponding to the vital sign indicator monitoring value.

[0033] In one possible implementation, the first product height function is determined as follows: For the first test data, for each viewing duration, the weighted average of the vital signs monitoring values ​​of all subjects under that viewing duration is calculated to obtain the comprehensive index value corresponding to that viewing duration; a first mapping relationship curve between the viewing duration and the comprehensive index value is established; within a preset viewing time period, the first mapping relationship curve is processed by product height processing to obtain a second mapping relationship curve, which describes the mapping relationship between the viewing duration and the first product height index value; based on the second mapping relationship curve, the first product height function is fitted.

[0034] In one possible implementation, the product height function is represented by the following formula:

[0035]

[0036] In this formula, F(t) represents the product height index value corresponding to the test subject at viewing time t, n represents the number of power function terms corresponding to the product height function, and C K t represents the Kth power function term that forms the product height function. K The corresponding fitting coefficients.

[0037] In one possible implementation, the ideal state function is represented by the following formula:

[0038]

[0039] In this formula, g(t) represents the ideal vital sign value of the subject under ideal conditions without noise interference, corresponding to a viewing time t, where A K The term t represents the Kth power term of the function that forms the ideal state. K The corresponding fitting coefficients, where A K = (K+1)×C K .

[0040] In one possible implementation, the steps for visual fatigue detection based on a reference time and a comparison time include:

[0041] Calculate the first ratio between the baseline vital sign monitoring value and the baseline time, and determine the first ratio as the baseline visual fatigue speed corresponding to the standard equipment;

[0042] Calculate the second ratio between the baseline vital signs monitoring value and the comparison time, and determine the second ratio as the target visual fatigue speed corresponding to the tested equipment. Compare the target visual fatigue speed with the baseline visual fatigue speed, and determine the degree of difficulty of the tested equipment in inducing visual fatigue relative to the standard equipment based on the comparison results.

[0043] Alternatively, calculate the third ratio between the reference time and the comparison time, determine the third ratio as the target visual fatigue speed corresponding to the tested device, compare the target visual fatigue speed with 1, and determine the degree of difficulty of the tested device inducing visual fatigue relative to the standard device based on the comparison result.

[0044] Secondly, embodiments of this application also provide an eye fatigue detection device, the device comprising:

[0045] The first acquisition module is used to acquire first test data and second test data from multiple test subjects. The first test data consists of vital sign monitoring data representing the degree of visual fatigue from multiple test subjects viewing a standard device during a preset viewing time period. The second test data consists of vital sign monitoring data from multiple test subjects viewing a test device during a preset viewing time period. The vital sign monitoring data includes the vital sign index monitoring values ​​for each test subject at each viewing duration within the preset viewing time period. The first accumulation height processing module is used to perform accumulation height processing on the vital sign monitoring data indicated by the first test data within the preset viewing time period to obtain the first accumulation height index value corresponding to each viewing duration. The accumulation height processing includes: integrating the vital sign monitoring data within the preset viewing time period and calculating the ratio between the integrated result and the preset viewing time period. The second accumulation height processing module is used to perform accumulation height processing on the vital sign monitoring data indicated by the second test data within the preset viewing time period. The system obtains the second cumulative height index value corresponding to each viewing duration; a construction module is used to fit and construct a first cumulative height function corresponding to the first test data based on the first cumulative height index value corresponding to each viewing duration, and to fit and construct a second cumulative height function corresponding to the second test data based on the second cumulative height index value corresponding to each viewing duration. The cumulative height function is used to characterize the mapping relationship between the cumulative height index value and the viewing duration; a second acquisition module is used to obtain the baseline vital sign index monitoring value at the baseline time based on the first ideal state function. The first ideal state function is determined by the first cumulative height function. The ideal state function refers to a function that eliminates noise terms and is used to characterize the cumulative change of vital sign index with viewing time; a third acquisition module is used to obtain the comparison time corresponding to the baseline vital sign index monitoring value based on the second ideal state function. The second ideal state function is determined by the second cumulative height function; a detection module is used to perform visual fatigue detection based on the baseline time and the comparison time.

[0046] Thirdly, embodiments of this application also provide an electronic device, including: a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor and the memory communicate via the bus. The machine-readable instructions are executed by the processor to perform the steps of the visual fatigue detection method in the first aspect or any possible implementation of the first aspect.

[0047] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the visual fatigue detection method in the first aspect or any possible implementation of the first aspect.

[0048] This application provides a method, device, electronic device, and storage medium for detecting visual fatigue. The method includes: obtaining first and second test data corresponding to multiple test subjects; performing product-height processing on the test data within a preset viewing time period to obtain a corresponding product-height function; the product-height processing includes: integrating the vital sign monitoring data within the preset viewing time period and calculating the ratio between the integrated result and the preset viewing time period; performing an inverse transformation based on the product-height function to obtain an ideal state function corresponding to the test data, excluding noise interference; and determining the visual fatigue detection speed based on the ideal state function corresponding to different test data. This application determines the visual fatigue speed by using a product-height function created based on fatigue test data to exclude noise interference, eliminating non-systematic errors and improving the accuracy of visual fatigue detection.

[0049] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0050] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 A flowchart of a visual fatigue detection method provided in an embodiment of this application is shown;

[0052] Figure 2 This paper shows a functional block diagram of an eye fatigue detection device provided in an embodiment of this application;

[0053] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the drawings in this application are for illustrative and descriptive purposes only and are not intended to limit the scope of protection of this application. Furthermore, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in this application illustrate operations implemented according to some embodiments of this application. It should be understood that the operations in the flowcharts may not be implemented in sequence, and steps without logical contextual relationships may be reversed or implemented simultaneously. In addition, those skilled in the art, guided by the content of this application, may add one or more other operations to the flowcharts, or remove one or more operations from the flowcharts.

[0055] Furthermore, the described embodiments are merely some, not all, of the embodiments of this application. The components of the embodiments of this application described and illustrated herein can typically be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0056] In actual visual fatigue speed testing, differential methods are often used to reduce the impact of environmental factors on signal accuracy. However, this method cannot be used if a more ideal test result with fewer interference factors is desired in a single measurement.

[0057] System noise often has a certain bias, and the difference method can reduce the impact of noise on the results. However, for non-system noise signals, since they always fluctuate around an ideal value, the difference method cannot effectively reduce the impact of noise on the test results, resulting in a certain deviation in the final results.

[0058] Based on this, embodiments of this application provide a method, apparatus, and electronic device for detecting visual fatigue. The method determines visual fatigue speed by using a product height function created based on fatigue test data to eliminate noise interference, thereby eliminating non-systematic errors and improving the accuracy of visual fatigue speed detection. Specifically, the method is as follows:

[0059] Please see Figure 1 , Figure 1 A flowchart of a visual fatigue detection method provided in an embodiment of this application is shown. Figure 1 As shown, the method provided in this application embodiment includes the following steps:

[0060] S100: Obtain first and second test data from multiple test subjects.

[0061] The first test data consists of vital sign monitoring data used to characterize the degree of visual fatigue when multiple test subjects watch the standard equipment during a preset viewing time period. The second test data consists of vital sign monitoring data when multiple test subjects watch the test equipment during a preset viewing time period. The vital sign monitoring data includes the vital sign index monitoring values ​​of each test subject for each viewing duration within the preset viewing time period.

[0062] S200. Within a preset viewing time period, the vital sign monitoring data indicated by the first test data are processed by accumulation to obtain the first accumulation index value corresponding to each viewing duration.

[0063] S300. Within a preset viewing time period, perform cumulative height processing on the vital sign monitoring data indicated by the second test data to obtain the second cumulative height index value corresponding to each viewing duration.

[0064] S400: Fit and construct a first cumulative height function corresponding to the first test data based on the first cumulative height index value corresponding to each viewing duration, and fit and construct a second cumulative height function corresponding to the second test data based on the second cumulative height index value corresponding to each viewing duration.

[0065] The product height function is used to characterize the mapping relationship between the product height index value and the viewing time.

[0066] S500: Based on the first ideal state function, obtain the baseline vital signs monitoring values ​​at the baseline time.

[0067] The first ideal state function is determined by the first product height function. Specifically, the first ideal state function is determined by the first fitting coefficient corresponding to the first product height function. The ideal state function refers to the function that eliminates noise terms and is used to characterize the cumulative change of vital signs with viewing time. Specifically, the first ideal state function refers to the function that eliminates noise terms and is used to characterize the cumulative change of vital signs with viewing time under the first test data.

[0068] S600: Based on the second ideal state function, obtain the comparison time corresponding to the baseline vital sign monitoring value.

[0069] The second ideal state function is determined by the second product function. Specifically, the second ideal state function is determined by the second fitting coefficient corresponding to the second product function. The second ideal state function is a function that eliminates noise terms and is used to characterize the cumulative change of vital signs under the second test data with viewing time.

[0070] S700: Visual fatigue detection is performed based on the reference time and comparison time.

[0071] In step S100, different first and second viewing tests are performed on the same test group within a preset viewing time period to determine the first and second test data respectively. The test group includes multiple test subjects. The viewing targets are different in the two viewing tests. In the first viewing test, the viewing target of the test subject is a standard device, and in the second viewing test, the viewing target of the test subject is a test device. In this application, the test data of the standard device is mainly used as a reference to determine the visual fatigue speed of the test device. The standard device is a device whose visual fatigue speed meets the preset standard. The standard device can also be a paper document.

[0072] During the viewing test, a target vital sign indicator can be selected from multiple vital sign indicators reflecting the user's visual fatigue state, based on actual needs or test tasks. In the same viewing test, the vital sign indicator detection values ​​corresponding to the target vital sign indicator generated by the test subject within a preset viewing time period are monitored and collected to determine the vital sign monitoring data corresponding to the target vital sign indicator. In this application, different data acquisition sensors are used for different vital sign indicators. After selecting the target vital sign indicator, the data acquisition sensor corresponding to the vital sign indicator is used to complete the collection of the indicator value corresponding to the target vital sign indicator. In this application, the preset viewing time period is usually set to more than 30 minutes, which can make the test results have better stability, and is generally set to 40 minutes.

[0073] Preferably, the vital signs indicators are divided into continuous measurements and non-continuous measurements. Continuous measurements refer to vital signs indicators that can be continuously collected during the viewing time, including EEG (Electroencephalogram, brainwaves), blink frequency, etc. Non-continuous measurements are indicators that cannot be continuously monitored and require interruption of the viewing process, such as flare fusion.

[0074] In a preferred embodiment, if the vital signs are continuous measurements, the vital signs monitoring values ​​generated by each subject within a preset viewing time period are collected at a preset collection frequency. The vital signs monitoring data are formed by the multiple vital signs monitoring values ​​corresponding to each subject within the preset viewing time period and the viewing duration corresponding to each vital signs monitoring value.

[0075] Specifically, taking the first viewing test as an example, assuming the preset time period is 40 minutes and the target vital sign indicator set in the test process is the continuous measurement of EEG, then for EEG, through the preset EEG acquisition device, multiple vital sign indicator monitoring values ​​generated by each subject continuously watching the standard device within the 40 minutes of the first viewing test, as well as the viewing duration corresponding to each vital sign indicator monitoring value, are continuously collected at the preset acquisition frequency. For each subject, the preset EEG acquisition device generates vital sign monitoring data based on the multiple monitoring values ​​corresponding to the EEG collected for each subject in 40 minutes and the viewing duration corresponding to each monitoring value.

[0076] The method for obtaining vital sign monitoring data for the second viewing test is the same as that for the first viewing test, and will not be repeated here.

[0077] In another preferred embodiment, if the vital signs are non-continuous measurements, the vital signs monitoring data are determined in the following manner:

[0078] The preset viewing time period is divided into multiple viewing periods according to the preset time interval. Based on the number of viewing periods, multiple test subjects are divided into multiple test groups. Each test group is assigned to a corresponding viewing period to watch the test using standard equipment or test equipment. Each test group has an equal number of people. For each test subject, during the viewing period associated with the test group to which the test subject belongs, the corresponding vital signs monitoring values ​​are collected. The vital signs monitoring data is formed by the corresponding vital signs monitoring values ​​of each test subject and the viewing duration corresponding to the vital signs monitoring values.

[0079] Specifically, taking the first viewing test as an example, assuming the preset time period is 10 minutes, the vital signs index set in the test process is CFF (Critical Fusion Frequency), where CFF is a discontinuous measurement, and the total number of test subjects is 10, the 10 minutes is divided into multiple viewing periods of 0-5 min and 5-10 min in 5-minute intervals. The 10 subjects are divided into 2 test groups (first test group and second test group), with 5 people in each group. The two test groups are associated with different viewing periods. The 10 test subjects start a 20-minute viewing test. During the 0-5 min viewing period, the monitoring values ​​of each test subject in the first test group under CFF and the corresponding viewing duration are collected to form the monitoring data for each test subject in the first test group. During the 5-10 min viewing period, the monitoring values ​​of each test subject in the second test group under CFF and the corresponding viewing duration are collected to form the monitoring data for each test subject in the second test group. The monitoring data collected in each viewing period form the vital signs monitoring data.

[0080] In the second viewing test, the method for obtaining the vital sign monitoring data for each participant was the same as in the first viewing test, and will not be repeated here.

[0081] During the viewing test, non-system noise from the test environment, data acquisition sensors, and the test subjects can introduce various errors. In this application, the noise influence during the viewing test is eliminated by constructing a product height function, thereby obtaining an ideal state function without noise influence.

[0082] In a specific embodiment, visual fatigue is a gradually increasing process. Therefore, in this application, for the test subject, the vital signs are a function of the viewing time t. Under the ideal condition of not considering noise disturbances during the test, the functional relationship between the vital signs and the viewing time can be equivalent to the sum of at least one power function, resulting in the ideal state function:

[0083]

[0084] Where t is the viewing duration, serving as the independent variable of the ideal state function; g(t) represents the ideal vital sign value of the subject at viewing duration t under ideal conditions without noise interference; and g(t) is the dependent variable of the ideal state function, where A K t represents the Kth power term t that forms the ideal state function. K The corresponding fitting coefficients, where n represents the number of power functions that form the ideal state function.

[0085] However, in actual testing, it is necessary to consider the test fluctuations caused by different noises during the viewing test. Directly using the test data to fit the ideal state function g(t) will lead to inaccurate final visual fatigue detection results. Therefore, inspired by the above-mentioned ideal state function, this application introduces noise perturbation to create a corresponding noise function during the test. Based on the ideal state function g(t), a noise function noise(t) is introduced to construct a test source function that represents the actual test situation.

[0086] In practical implementation, based on the above analysis and the introduction of a noise function, the test source function is expressed as follows:

[0087]

[0088] In formula (1), A K t represents the fitting coefficient corresponding to the Kth power function term. KLet noise(t) represent the Kth power function term with viewing duration as the base, and let noise(t) represent the noise value of the test subject at viewing duration t. During the viewing test (either the first or second viewing test), at least one type of test noise may be generated. Therefore, the noise function noise(t) for the entire viewing test is the sum of the noise functions corresponding to each test noise. Specifically... m represents the amount of test noise generated during the viewing test. i (t) represents the sub-noise function corresponding to the i-th test noise during the viewing test, and y(t) represents the vital sign monitoring result obtained after introducing the noise value generated at the viewing time t into the corresponding vital sign monitoring value of the test subject at the viewing time t.

[0089] In fact, formula (1) describes a mapping relationship between each viewing duration and the actual measured vital signs value during the viewing test. In other words, in this application, the mapping relationship between the viewing duration and the vital signs value described by the test data is actually the test source function. As can be seen from formula (1), the test source function is related to the ideal state function g(t) and the noise function noise(t). In other words, the mapping relationship between the viewing duration and the vital signs value described in the test data still retains the influence of test noise.

[0090] In order to make the results more accurate during the visual fatigue detection process, it is necessary to eliminate the influence of noise on the visual fatigue of the target during the test. In this application, based on the characteristics of the noise function, it is determined that as long as the collected test data is processed by the product height, a product height function that eliminates the noise term can be obtained.

[0091] As can be seen from the characteristics of the noise function, since non-system noise always fluctuates around an ideal value, the final result of the noise signal superposition is still a random value in a certain range. By utilizing the self-cancellation property of non-system noise accumulation, by integrating the test source function and then calculating the ratio with the preset viewing time period T, the result can accurately determine the speed of visual fatigue.

[0092] In one specific embodiment, for the test source function To eliminate the influence of noise, using the second characteristic corresponding to the noise function mentioned above, if the test source function is subjected to product height transformation or product height processing, the following can be obtained:

[0093]

[0094] In this formula, T represents the preset viewing time period, and F(t) and y(t) can be inversely transformed, which is denoted as: F(t) is called the product height function of the test source function y(t).

[0095] In this application, y(t) can be represented by the corresponding mapping relationship between the viewing time and the vital signs monitoring values ​​described by the test data. Based on this, further transformation is performed using formula (2):

[0096]

[0097] Where ΔT is the time interval between two adjacent data collections (collection of vital sign monitoring values). When Δt is sufficiently small and T is sufficiently large, Δt / T can be ignored. According to property ② of the noise function, that is:

[0098]

[0099] Therefore, it can be seen that the product height function F(t) obtained after the test source function undergoes product height transformation can eliminate noise function terms and can be represented by the ideal state function. This shows that the product height function obtained after the test source function undergoes product height transformation and the ideal state function can be converted. Based on this, this application can determine the corresponding ideal state function by calculating the product height function, so as to facilitate subsequent visual fatigue detection.

[0100] As can be seen from the above formula, in the process of constructing the product height function, by utilizing the characteristic of self-cancellation when non-system noise accumulates, the product height function is calculated to accumulate non-system noise and remove the fluctuation in the test source function. This can effectively reduce the error of non-system noise on visual fatigue speed and improve the accuracy of subsequent visual fatigue speed.

[0101] In this formula, F(t) represents the product height index value corresponding to the test subject at viewing time t, n represents the number of power function terms corresponding to the product height function, and C K t represents the Kth power function term that forms the product height function. K The corresponding fitting coefficients.

[0102] In a preferred embodiment, in steps S200 to S400, the first product height function is determined in the following manner:

[0103] For each viewing duration, the weighted average of the vital signs monitoring values ​​of all subjects under that viewing duration is calculated to obtain the comprehensive index value corresponding to that viewing duration. A first mapping relationship curve between the viewing duration and the comprehensive index value is established. Within a preset viewing time period, the first mapping relationship curve is processed by product height processing to obtain a second mapping relationship curve. The second mapping relationship curve describes the mapping relationship between the viewing duration and the first product height index value. Based on the second mapping relationship curve, the first product height function is fitted.

[0104] Specifically, in this application, taking the first test data as an example, within the preset viewing time period, for each viewing duration, the corresponding vital signs monitoring values ​​of each test subject under that viewing duration are processed by averaging multiple vital signs monitoring values ​​under that viewing duration to obtain a comprehensive index value corresponding to that viewing duration. Further, a first mapping relationship curve is established between the viewing duration and its corresponding comprehensive index value. This first mapping relationship curve replaces the aforementioned test source function. Combined with formula (2), the first mapping relationship curve is processed by product height within the preset viewing time period to obtain the first product height index value corresponding to each viewing duration. Then, a second mapping relationship curve is established between the viewing duration and the first product height index value, combined with formula:

[0105] The first product height function is obtained.

[0106] The second product height function is determined by the second test data. The specific determination method is the same as that for the first product height function, and will not be elaborated here.

[0107] Furthermore, for the product height function, after determining the fitting coefficients corresponding to each power function term, C... K =A K / (K+1) yields the fitting coefficients for the corresponding ideal state function, i.e.:

[0108] A K = (K+1)×C K .

[0109] In the method provided in this application, the correspondence A between the fitting coefficients of the power function terms in the ideal state function and the fitting coefficients of the power function terms in the product height function is used. K = (K+1)×C K Therefore, before executing step S500, the first fitted value corresponding to each fitted coefficient in the first product height function and the second fitted value corresponding to each fitted coefficient in the second product height function can be substituted into the following formula to obtain the first ideal state function g1(t) and the second ideal state function g2(t):

[0110]

[0111] In traditional noise cancellation methods, there are two main approaches. One is the differential method, which uses the difference between adjacent signals under the same environment to remove noise. This method cannot be implemented without the relevant conditions and also requires two signal paths physically.

[0112] Another method is to remove certain harmonic components through Fourier transform. If the noise component frequency is high, this part of the influence can be removed, and then an inverse Fourier transform can be performed. However, it cannot remove low-frequency or similar-frequency noise, which requires the addition of filtering circuits in the hardware circuit, increasing the design cost.

[0113] The accuracy of the ideal state function obtained by the inverse product-high transform depends on Δt / T, which is the number of samples and the sampling time. In fact, based on the fluctuation amplitude of the test waveform, we can determine the fluctuation range of {-Δ, +Δ}, and then determine the sampling frequency and duration, i.e. the magnitude of Δt / T, according to the accuracy requirements, so as to obtain the required ideal state function in a more economical way.

[0114] In a preferred embodiment, during the determination of the product height function, the different types of vital signs indicators result in different forms of product height function. Specifically, if the vital signs indicator is a continuous measurement, the final product height function will be in a continuous form. If the vital signs indicator is a non-continuous measurement, the final product height function will be in a piecewise function form, and the segment intervals depend on the divided viewing time period.

[0115] In a preferred embodiment, in step S500, based on the above process, the corresponding first ideal state function g1(t) can be determined, and the baseline vital signs monitoring value A0 at the baseline time T0 after removing the influence of noise during the first viewing test is obtained, and A0 / T0 is taken as the standard visual fatigue speed Vs0 corresponding to the first viewing test.

[0116] In another preferred embodiment, in step S600, for the second viewing test, based on the second ideal state function g2(t) determined in the above process, the baseline vital sign monitoring value A0 is substituted into the second ideal state function g2(t) to determine the comparison time T1 corresponding to the baseline vital sign monitoring value A0.

[0117] In a preferred embodiment, step S700 includes:

[0118] Calculate the first ratio between the reference time T0 and the reference vital sign monitoring value A0, and determine the first ratio as the reference visual fatigue speed Vs0 corresponding to the standard equipment.

[0119] Furthermore, the second ratio between the baseline vital sign monitoring value A0 and the comparison time T1 is calculated, and the second ratio is determined as the target visual fatigue speed corresponding to the tested equipment. The target visual fatigue speed is compared with the baseline visual fatigue speed Vs0, and the degree of difficulty of the tested equipment in inducing visual fatigue relative to the standard equipment is determined based on the comparison results.

[0120] Specifically, if the target visual fatigue speed Vs1 is greater than the standard visual fatigue speed Vs0, it is determined that the tested device is more likely to cause visual fatigue than the standard device. If the target visual fatigue speed Vs1 is equal to the standard visual fatigue speed Vs0, it is determined that the tested device is comparable to the standard device in causing visual fatigue. If the target visual fatigue speed Vs1 is less than the standard visual fatigue speed Vs0, it is determined that the tested device is less likely to cause visual fatigue than the standard device.

[0121] In addition, this application can also determine the degree to which the tested device induces visual fatigue relative to a standard device in the following ways:

[0122] Calculate the third ratio between the reference time T0 and the comparison time T1, and determine the third ratio as the target visual fatigue speed Vs1. Compare the target visual fatigue speed Vs1 with 1, and determine the degree of difficulty of the tested device inducing visual fatigue relative to the standard device based on the comparison result.

[0123] Specifically, in this method, if the target visual fatigue speed Vs1 is greater than 1, it is determined that the tested device is more likely to cause visual fatigue than the standard device; if the target visual fatigue speed Vs1 is equal to 1, it is determined that the tested device is comparable to the standard device in causing visual fatigue; and if the target visual fatigue speed Vs1 is less than 1, it is determined that the tested device is less likely to cause visual fatigue than the standard device.

[0124] Please see Figure 2 , Figure 2 A functional block diagram of a visual fatigue speed determination device provided in an embodiment of this application is shown. Figure 2 As shown, the device includes:

[0125] The first acquisition module 800 is used to acquire first test data and second test data of multiple test subjects. The first test data is the vital sign monitoring data of multiple test subjects when watching the standard equipment during a preset viewing time period to characterize the degree of visual fatigue. The second test data is the vital sign monitoring data of multiple test subjects when watching the test equipment during a preset viewing time period. The vital sign monitoring data includes the vital sign indicator monitoring values ​​of each test subject for each viewing duration within the preset viewing time period.

[0126] The first cumulative height processing module 810 is used to perform cumulative height processing on the vital sign monitoring data indicated by the first test data within a preset viewing time period to obtain the first cumulative height index value corresponding to each viewing duration. The cumulative height processing includes: integrating the vital sign monitoring data within the preset viewing time period and calculating the ratio between the integration result and the preset viewing time period.

[0127] The second accumulation height processing module 820 is used to perform accumulation height processing on the vital sign monitoring data indicated by the second test data within a preset viewing time period to obtain the second accumulation height index value corresponding to each viewing duration.

[0128] The construction module 830 is used to fit and construct a first product height function corresponding to the first test data based on the first product height index value corresponding to each viewing duration, and to fit and construct a second product height function corresponding to the second test data based on the second product height index value corresponding to each viewing duration. The product height function is used to characterize the mapping relationship between the product height index value and the viewing duration.

[0129] The second acquisition module 840 is used to obtain the baseline vital signs monitoring value at the baseline time based on the first ideal state function. The first ideal state function is determined by the first product height function. The ideal state function refers to a function that eliminates noise terms and is used to characterize the cumulative change of vital signs with viewing time.

[0130] The third acquisition module 850 is used to obtain the comparison time corresponding to the baseline vital sign monitoring value based on the second ideal state function. The second ideal state function is determined by the second product height function.

[0131] The detection module 860 is used to perform visual fatigue detection based on the reference time and the comparison time.

[0132] Based on the same application concept, please refer to Figure 3 , Figure 3 This diagram illustrates the structure of an electronic device according to an embodiment of this application. The electronic device 900 includes a processor 910, a memory 920, and a bus 930. The memory 920 stores machine-readable instructions executable by the processor 910. When the electronic device 900 is running, the processor 910 and the memory 920 communicate via the bus 930. The machine-readable instructions are executed by the processor 910 to perform the steps of the visual fatigue speed determination method provided in any of the above embodiments.

[0133] Based on the same concept, this application also provides a computer-readable storage medium storing a computer program, which, when run by a processor, executes the steps of the visual fatigue speed determination method provided in the above embodiments.

[0134] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and devices described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division; in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection may be through some communication interfaces; the indirect coupling or communication connection of devices or units may be electrical, mechanical, or other forms.

[0135] 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 units can be selected to achieve the purpose of this embodiment according to actual needs.

[0136] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0137] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, 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 this application. 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.

[0138] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for detecting visual fatigue, characterized in that, The method includes: Acquire first test data and second test data from multiple test subjects. The first test data consists of vital sign monitoring data used to characterize the degree of visual fatigue when the multiple test subjects watch the standard device during a preset viewing time period. The second test data consists of vital sign monitoring data when the multiple test subjects watch the test device during a preset viewing time period. The vital sign monitoring data includes the vital sign index monitoring values ​​of each test subject for each viewing duration within the preset viewing time period. Within a preset viewing time period, the vital sign monitoring data indicated by the first test data is processed by integration to obtain the first integration index value corresponding to each viewing duration. The integration process includes: integrating the vital sign monitoring data within the preset viewing time period and calculating the ratio between the integration result and the viewing duration used. Within a preset viewing time period, the vital sign monitoring data indicated by the second test data are processed by accumulation to obtain the second accumulation index value corresponding to each viewing duration; A first product height function corresponding to the first test data is constructed by fitting the first product height index value corresponding to each viewing duration, and a second product height function corresponding to the second test data is constructed by fitting the second product height index value corresponding to each viewing duration. The product height function is used to characterize the mapping relationship between the product height index value and the viewing duration. Based on the first ideal state function, the baseline vital signs monitoring values ​​at the baseline time are obtained. The first ideal state function is determined by the first product height function. The ideal state function refers to a function that eliminates noise terms and is used to characterize the cumulative change of vital signs with viewing time. Based on the second ideal state function, the comparison time corresponding to the baseline vital sign monitoring value is obtained, and the second ideal state function is determined by the second product height function; Visual fatigue detection is performed based on the reference time and the comparison time. The product height function is represented by the following formula: In this formula, This represents the cumulative height index value corresponding to the viewing time t for the test subject. This indicates the number of power function terms corresponding to the product height function. The first element that forms the product height function is... One power function term The corresponding fitting coefficients.

2. The method according to claim 1, characterized in that, Vital signs monitoring data are determined in the following ways: If the vital signs are continuous measurements, then the vital signs monitoring values ​​generated by each subject within the preset viewing time period will be collected at the preset collection frequency. The vital signs monitoring data is formed by the monitoring values ​​of multiple vital signs indicators for each subject within a preset viewing time period and the viewing duration corresponding to each vital signs indicator monitoring value.

3. The method according to claim 1, characterized in that, Vital signs monitoring data are determined in the following ways: If the vital signs are non-continuous measurements, the preset viewing time period is divided into multiple viewing periods according to the preset time interval; Based on the number of viewing time periods, the multiple test subjects are divided into multiple test groups, and each test group is assigned to a corresponding viewing time period to watch the test of standard equipment or test equipment. Each test group has an equal number of people. For each participant, during the viewing period associated with the test group to which the participant belongs, the corresponding vital signs monitoring values ​​of the participant are collected; The vital signs monitoring data is formed by the monitoring values ​​of the vital signs indicators for each subject and the viewing time corresponding to the monitoring values ​​of the vital signs indicators.

4. The method according to claim 1, characterized in that, The first product height function is determined in the following manner: For the first test data, for each viewing duration, the weighted average of the vital signs monitoring values ​​of all test subjects under that viewing duration is calculated to obtain the comprehensive index value corresponding to that viewing duration. Establish the first mapping relationship curve between viewing time and comprehensive index value; Within a preset viewing time period, the first mapping relationship curve is processed by the product height to obtain the second mapping relationship curve. The second mapping relationship curve describes the mapping relationship between the viewing time and the first product height index value. The first product height function is obtained by fitting the second mapping relationship curve.

5. The method according to claim 1, characterized in that, The ideal state function can be expressed by the following formula: In this formula, This represents the ideal vital sign values ​​of the subject during viewing time t under ideal conditions with no noise interference, where... The first state function represents the state of the ideal state. Power exponent terms The corresponding fitting coefficients, where, .

6. The method according to claim 1, characterized in that, The steps for visual fatigue detection based on the reference time and the comparison time include: Calculate the first ratio between the baseline vital sign monitoring value and the baseline time, and determine the first ratio as the baseline visual fatigue speed corresponding to the standard equipment; Calculate the second ratio between the baseline vital sign monitoring value and the comparison time, determine the second ratio as the target visual fatigue speed corresponding to the tested device, compare the target visual fatigue speed with the baseline visual fatigue speed, and determine the degree of difficulty of the tested device inducing visual fatigue relative to the standard device based on the comparison result; Alternatively, calculate a third ratio between the reference time and the comparison time, determine the third ratio as the target visual fatigue speed corresponding to the tested device, compare the target visual fatigue speed with 1, and determine the degree of difficulty of the tested device inducing visual fatigue relative to the standard device based on the comparison result.

7. A visual fatigue detection device, characterized in that, The device includes: The first acquisition module is used to acquire first test data and second test data of multiple test subjects. The first test data is the vital sign monitoring data of the multiple test subjects when watching the standard device during a preset viewing time period to characterize the degree of visual fatigue. The second test data is the vital sign monitoring data of the multiple test subjects when watching the test device during a preset viewing time period. The vital sign monitoring data includes the vital sign indicator monitoring values ​​of each test subject for each viewing duration within the preset viewing time period. The first cumulative height processing module is used to perform cumulative height processing on the vital sign monitoring data indicated by the first test data within a preset viewing time period to obtain the first cumulative height index value corresponding to each viewing duration. The cumulative height processing includes: integrating the vital sign monitoring data within the preset viewing time period and calculating the ratio between the integration result and the preset viewing time period. The second accumulation height processing module is used to perform accumulation height processing on the vital sign monitoring data indicated by the second test data within a preset viewing time period to obtain the second accumulation height index value corresponding to each viewing duration. The construction module is used to fit and construct a first product height function corresponding to the first test data based on the first product height index value corresponding to each viewing duration, and to fit and construct a second product height function corresponding to the second test data based on the second product height index value corresponding to each viewing duration. The product height function is used to characterize the mapping relationship between the product height index value and the viewing duration. The second acquisition module is used to obtain the baseline vital signs monitoring value at the baseline time based on the first ideal state function. The first ideal state function is determined by the first product height function. The ideal state function refers to a function that eliminates noise terms and is used to characterize the cumulative change of vital signs with viewing time. The third acquisition module is used to obtain the comparison time corresponding to the benchmark vital sign monitoring value based on the second ideal state function, wherein the second ideal state function is determined by the second product height function; The detection module is used to perform visual fatigue detection based on the reference time and the comparison time; The product height function is represented by the following formula: In this formula, This represents the cumulative height index value corresponding to the viewing time t for the test subject. This indicates the number of power function terms corresponding to the product height function. The first element that forms the product height function is... One power function term The corresponding fitting coefficients.

8. An electronic device, characterized in that, include: The device includes a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via the bus. The machine-readable instructions are executed by the processor to perform the steps of the visual fatigue detection method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the visual fatigue detection method as described in any one of claims 1 to 6.

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