CN114504318B - Medical equipment interface optimization method based on cognitive mechanism in complex visual environment - Google Patents
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Abstract
A medical equipment interface optimization method based on a cognitive mechanism in a complex visual environment comprises the following steps: designing a physiological response experiment by taking attribute factors as variables, wherein the attribute factors comprise but are not limited to atomization degree, screen brightness and font color, setting the attribute factors into different independent variables, and combining the attribute factor changes provided with the different independent variables to form different characteristic parameters; combining the characteristic parameters with display information to form different types of stimulation; outputting the simulation display of the stimulus in the same time interval mode, and judging the stimulus by the tested person; establishing relation mapping between the judging efficiency of the tested person, including accuracy and response time, and different types of stimuli; and analyzing the influence of each attribute factor on the judging efficiency and the interaction among the attribute factors by taking the accuracy and the reaction time as dependent variables respectively so as to find out ideal parameter values and a combination mode of the attribute factors for improving the interface interaction effect.
Description
Technical Field
The invention relates to the field of computer interface interaction, in particular to a medical equipment interface optimization method under a complex visual environment based on a cognitive mechanism.
Background
The user interface of the medical equipment is an intuitive tool for medical staff to realize treatment operation and acquire treatment information, but because of special environments such as weather, temperature and the like, especially during a new epidemic situation, the medical staff needs to wear protective equipment such as masks, goggles and the like due to protective requirements, so that the problems of glasses or goggles fogging and the like are caused, and the use of the interactive interface of the medical equipment can also have higher requirements. Therefore, optimizing the interface of the medical device is a urgent problem to be solved in the face of a complex visual environment.
The cognitive problems of user information processing, learning, thinking, decision-making and the like in the human-computer interaction process are key links of human-computer interaction design; the cognitive mechanism is a generic term for mainly researching common psychological processes and psychological characteristics in the information processing process of human feel, perception, thinking, decision-making and the like in the cognitive psychology, and is also applicable to the cognitive activity when a human uses a digital interface. The cognition mechanism and the digital interface information design and the evaluation have a necessary relation, the cognition mechanism is an internal factor affecting the understanding, learning and using of the digital interface by a user, is a theoretical basis and a basis for developing the digital interface information design and the digital interface information design evaluation, the digital interface information design evaluation based on the cognition mechanism can further reveal the cognition process of the user on the digital interface information, and the evaluation result can assist and optimize the interface information design. When optimizing a medical device interface in consideration of a complex visual environment, it is necessary to start from the point of view of a human-based cognitive mechanism.
Disclosure of Invention
In order to solve the technical problem of non-ideal medical equipment interfaces in a complex environment, the invention provides a medical equipment interface optimization method based on a cognitive mechanism in a complex visual environment.
The technical scheme adopted by the invention is as follows: a medical equipment interface optimization method based on a cognitive mechanism in a complex visual environment comprises the following steps:
1) Designing a physiological response experiment by taking attribute factors as variables, wherein the attribute factors comprise but are not limited to atomization degree, screen brightness and font color, setting the attribute factors into different independent variables, and combining the attribute factor changes provided with the different independent variables to form different characteristic parameters;
2) Combining the characteristic parameters with display information to form different types of stimulation;
3) Outputting the simulation display of the stimulus in the same time interval mode, and judging the stimulus by the tested person;
4) Establishing relation mapping between the judging efficiency of the tested person, including accuracy and response time, and different types of stimuli;
5) And analyzing the influence of each attribute factor on the judging efficiency and the interaction among the attribute factors by taking the accuracy and the reaction time as dependent variables respectively so as to find out ideal parameter values and a combination mode of the attribute factors for improving the interface interaction effect.
Further, in the step 1), an NxNx (N+1) factorization design is adopted, and the screen brightness has N grades; the font color is provided with N colors respectively; the atomization degree is provided with N atomization mirror surfaces with different degrees and 1 control variable without atomization. For example, a 3 x 4 factorial design may be used, with three levels of screen brightness: 70 cd/m respectively 2 、150 cd/m 2 And 350 cd/m 2 The screen brightness is measured by the screen brightness tester SM208 in a white background; the font color has three colors: yellow, green, and blue; the atomization degree comprises three different degrees of atomization mirror surfaces and a control variable without atomization.
Further, in step 2), the display information is set to be a set of coding information including parameterized information and arabic numerals, where the coding information gives two ranges of normal information and abnormal information, so that a tested person can make a judgment by combining with a cognitive mechanism to simulate man-machine interface interaction in a real working environment, and in a set of experiments, the display information is randomly output according to the two ranges of normal information and abnormal information in a certain proportion.
Further, in step 2), the display information is given to the characteristic parameters of step 1) to form stimulus, and the number of the characteristic parameters is the number of the stimulus in a group of experiments.
Further, in step 3), the number of the testees is set to be not less than one, each testee repeats for N times on one stimulus, the average response time is taken as the response time of a single testee, and the response time and the accuracy rate of all testees on the same stimulus are collected.
Further, in step 4), after the abnormal value is removed, statistical analysis is performed on the reaction time and accuracy data, and single-factor analysis of variance (ANOVA) is performed on each independent variable to obtain an average value and a standard deviation; and (3) performing multivariate analysis of variance MANOVA by using a statistical product and service solution SPSS to obtain the significant difference level of the test and the significant level of the test.
Further, the conclusion of step 5) is: as the atomization degree of the mirror surface deepens, the slower the response speed of the tested person to the abnormal value is, the higher the error rate is; the yellow and green character colors have better vigilance performance under different atomization degrees, the blue color is poorer, and the green and yellow color have stronger vigilance compared with the blue color; the difference between yellow and green is not obvious, but the yellow character has weaker stability under the influence of different atomization environments and screen brightness, and the green is more stable in appearance; the brightness of the tested person at the medium screen is 150cd/m 2 The method has better visual performance, and has highest vigilance and recognition efficiency on abnormal values.
The beneficial effects of the invention are as follows: 1. because the medical equipment needs to continuously detect the physiological data of the patient when in use, compared with the prior experiment in which the target stimulus is identified by the stimulus, the stimulus appears one by one in the experiment, and the design of the stimulus accords with the actual use condition of the medical equipment. 2. The experiment is more realistic to restore the use situation of the medical equipment, simulate the fogging situation of goggles, explore the influence of interface codes on cognitive efficiency, and take environmental factors into consideration, so that the optimization of interfaces in the experiment is more in line with the actual situation.
The experimental study aims at the influence of screen brightness and character color on search efficiency under the condition of mirror surface atomization by combining with the trend of the current epidemic situation. The response time and the accuracy of visual recognition are used as important indexes, the purpose is to study the recognition efficiency of heart rate information of an electrocardiograph under the condition of mirror surface atomization through the influence of the mirror surface atomization on interface information coding, and experience recognition is carried out, so that the vigilance test of abnormal information is increased, and the method is more in line with actual use conditions.
Drawings
FIG. 1-electrocardiograph interface simulation;
FIG. 2-font selection;
FIG. 3-color selection;
FIG. 4-goggles of varying degrees of atomization;
FIG. 5-simulated human eye interface shots;
FIG. 6-results of photographing;
FIG. 7-experimental scenario;
FIG. 8-experimental procedure;
FIG. 9-ANOVA results screen brightness;
FIG. 10-interaction between character color and degree of fogging in terms of reaction time;
FIG. 11-interaction between color and degree of fogging in terms of accuracy;
fig. 12-a flowchart of a method for optimizing a medical device interface based on a cognitive mechanism in a complex visual environment.
Detailed Description
The invention will be further illustrated with reference to specific examples.
In this embodiment, the heart rate displayed on the electrocardiograph interface is identified by the subject, and the specific procedure is as follows.
1. Preparation before experiments
The study recruited a total of 30 study groups of 14 men, 16 women, between 21 and 26 years of age. They were recruited by internet questionnaires and voluntary. All subjects had normal or corrected vision and none had color blindness or weakness. A single task experiment was performed in which a visual change detection paradigm was used, which required the subject to first remember to identify the target, which was then followed by target identification. The stimulus in visual recognition is parameterized information of heart rate, including parameter names and arabic numerals. Ten different digital stimuli are arranged in a random fashion, requiring the test to determine the type of stimulus as soon as possible. In order to control the influence of ambient illuminance on the screen brightness variable, a hospital building illumination standard in national illumination standards is adopted, the ground is taken as a reference plane, the illuminance value is 100 lx, and the illuminance value is measured by an illuminometer (TES-1339) by using a four-corner point distribution method.
2. Simulation environment settings and parameter settings
And designing a physiological response experiment by taking the attribute factors as variables, wherein the attribute factors comprise but are not limited to atomization degree, screen brightness and font color, setting the attribute factors into different independent variables, and combining the attribute factor changes provided with the different independent variables to form different characteristic parameters. Adopting an NxNx (N+1) factorization design, wherein the screen brightness has N grades; the font color is provided with N colors respectively; the atomization degree is provided with N atomization mirror surfaces with different degrees and 1 control variable without atomization.
The experiment adopts a 3 multiplied by 4 factorial design, and considers the degree of mirror surface atomization, screen brightness and font color so as to determine the efficiency of task identification in a short time. The factor 1 is the screen brightness, which has three levels: 70 cd/m respectively 2 、150 cd/m 2 And 350 cd/m 2 . The screen brightness is measured by a screen brightness tester (SM 208) against a white background. The factor 2 is the font color, and three colors are respectively: yellow, green, and blue. The factor 3 is the atomization degree of the eye protection mirror surface, and the three atomization mirror surfaces with different degrees and a clear eye protection mirror surface control variable without atomization are adopted, the experimental variable blurring degree is respectively 7, 9 and 11, and the transparent mirror surface blurring degree is 120.
Combining the characteristic parameters with display information to form different types of stimulation, wherein the display information in the embodiment is a heart rate value. The display information is set as a group of coding information containing parameterized information and Arabic numerals, and the coding information gives two ranges of normal information and abnormal information, so that a tested person can judge by combining a cognitive mechanism to simulate man-machine interface interaction in a real working environment, and in a group of experiments, the display information is randomly output according to the two ranges of the normal information and the abnormal information in a certain proportion. The display information is endowed with characteristic parameters to form stimulus, and the number of the characteristic parameters is the number of the stimulus in a group of experiments.
The stimulus is presented through a simulated interface of the electrocardiograph. The display information is presented on the screen with a resolution of 1920×1080 px, and the original interface and the simulation interface are as shown in fig. 1. The parameter font is Arial, as in fig. 2, of the same size and in the same location. In the experiment, random heart rate values in 40-120 are used as input, random numbers are generated by means of a tool Excel, the range of normal heart rate values is 60-100, and the ratio of the number of normal heart rates to the number of abnormal heart rates adopted in the experiment is 8:2. The probability of the occurrence of the bias stimulus in the Oddball experimental paradigm should typically be around 20%; the probability of occurrence of a standard stimulus is typically around 80%. For example: at 70 cd/m 2 Setting the atomization degree to be 7 under the brightness background, and taking the color as a variable: the method comprises the following steps of sequentially setting 10 yellow stimuli, 10 green stimuli and 10 blue stimuli, wherein the 10 stimuli of each color comprise 8 normal heart rates and 2 abnormal heart rates. Then under the condition that the brightness background is unchanged, the atomization degree is changed to 9, then the color is used as a variable, the stimuli with different colors are sequentially output, and the like until all the atomization degrees are 11 and 120, the brightness background is sequentially set to 150cd/m 2 And 350 cd/m 2 The above steps are repeated.
The color standard in this experiment adopts RGB color mode, the RGB values of three color variables are yellow R: 255G: 255B: 0, green R: 0G: 255B: 0 and cyan R: 0G: 255B: 255, the RGB value of the interface black background is R: 255G: 255B: 255, and the brightness of these three colors is similar to the background brightness ratio under the same screen brightness, as shown in FIG. 3. In the analog interface, parameter information related to the variable heart rate changes color along with the parameters. In this study, the reaction time and accuracy were studied with emphasis.
The experimental program was written using software E-Prime for behavioral research work. The program was installed on a computer with a CPU frequency of 2.7 gigahertz. Stimulation occurs at 14 inches, 3:2, the screen resolution is 1920 x 1080 pixels.
The experimental lighting conditions are normal, in order to control the influence of the ambient illuminance on the screen brightness variable, the hospital building lighting standard GB 50034-2004 in the national lighting standard is adopted, the ground is used as a reference plane, and the illuminance value is 100 lx. Experimental variable blur degree the atomization simulation was performed by atomizing a sticker, as shown in fig. 4.
The ambiguity values are photographed by a camera 3cm behind the mirror surface on the screen as shown in fig. 5 and the result is shown in fig. 6. The operation code is cv2.Laplacian (image, cv2.CV_64F). Var ().
3. Experimental requirements to Experimental procedures
Outputting the simulation display of the stimulus in the same time interval mode, and judging the stimulus by the tested person; the judging efficiency of the tested person, including the accuracy and the response time, is mapped with different types of stimuli. Setting the number of the testees to be not less than one, repeating each tester for N times on one stimulus, taking the average response time as the response time of a single tester, and collecting the response time and the accuracy of all testers on the same stimulus.
The experiment required all 30 participants to view a target, parameterize the information and arabic numerals, and immediately press the "P" button on the keyboard, 5 repetitions per person. Then, the E-Prime program records the reaction time and calculates the average reaction time, i.e., 1030 ms (standard deviation= 304.556 ms). The gaze time of the recognition target is set to 3000ms herein. The preliminary experiment was first performed before the initiation of the main experiment, and the proficiency of the subject was increased as in the main experiment procedure, and the reaction time error due to the difference in proficiency during the experiment was eliminated, as shown in fig. 7. In a formal experiment, the participants first read the experimental description and were then informed to start the experiment by pressing any key on the keyboard. Firstly, a "+" is displayed in the center of a screen as a fixation point 500 ms, then, a target identification object is presented, after the testee observes stimulation, the testee judges whether the heart rate value is normal, if the heart rate value is 60-100, the heart rate value is normal, an F key is pressed, if the heart rate value is <60 or >100, the heart rate value is abnormal, a J key is pressed, the stimulation after the key is disappeared, and the next stimulation continues to appear after the 100ms of attack delay. There is a 3000ms attack delay between different color stimulus interfaces, eliminating the visual impact of the last color on the test. The experimental procedure is shown in FIG. 8. Before the experiment formally begins, the participants are required to be familiar with the process through experimental practices. Until then they did not conduct formal experiments. After the participants complete the reaction search, the software will automatically provide the next set of tests. Each participant performed a total of 12 groups of experiments, each group of experiments having three color variables, 10 stimulus interfaces per color, a normal to abnormal heart rate occurrence ratio of 8:2, each group of tests spaced one minute apart, each individual having a 20 minute time to complete all 12 (3 x 4) groups of 360 (12 x 10 x 3) targets.
4. Analysis of experimental results
And analyzing the influence of each attribute factor on the judging efficiency and the interaction among the attribute factors by taking the accuracy and the reaction time as dependent variables respectively so as to find out ideal parameter values and a combination mode of the attribute factors for improving the interface interaction effect.
After the abnormal value is removed, statistical analysis is carried out on the reaction time and the accuracy data. One-way analysis of variance (ANOVA) was performed on the levels in each independent variable or factorial experiment, with the mean and standard deviation shown in table one. Multiplex analysis of variance (MANOVA) using Statistical Product and Service Solutions (SPSS) results are shown in table two, with F representing the level of significant difference and P representing the level of significance tested.
Table-average and standard deviation of reaction time and accuracy
MANOVA results for time and accuracy of two reactions
With the reaction time as a dependent variable, it can be observed that both the character color f=9.737, p=0.002 <0.01 and the degree of specular fogging f=29.148, p=0.000 <0.01 have very significant effects, and the effect of the screen brightness f=3.319, p=0.048 <0.05 has statistical significance, but is less significant. In terms of interaction effect, the interaction effect f=2.986 between the fog level and the character color, p=0.021 <0.05 is significant, and the interaction f=2.986 between the three variables of screen brightness, character color and fog level, p=0.011 <0.05 is significant. There is no significant interaction between other factors.
From the results with accuracy as a dependent variable, it can be seen that the degree of atomization f=10.013, p=0.000 <0.01 has a very significant effect on accuracy. The main effect of the character color f=5.693, p=0.016 <0.05 is more pronounced, while the effect of the screen brightness f=2.366, p=0.130 <0.05 on the accuracy is insignificant. The interaction effect between the degree of fogging and the character color f=2.529, p=0.035 <0.05 is more pronounced. The interaction effect between the other variables is insignificant. Because the medical interface is simulated in the experiment, the time of each time the testee watches the stimulus is limited, the cognitive load is improved, the fitting degree of the experiment and the actual is improved, and the experimental data have reference significance.
Since the effect of screen brightness on reaction time has no significant interaction with other factors, one-way analysis of variance (ANOVA) was performed on screen brightness, as shown in fig. 9. The results show that when the luminance is 150cd/m 2 When the reaction time was the shortest, mean=743. When the screen brightness is 50cd/m 2 The reaction time is longest, mean=863 ms, and the reaction time is long at the highest screen brightness to a medium level.
Taking the reaction time as a dependent variable, the reaction time of different atomization degrees under different colors is different, as shown in fig. 10, the marginal average value of the reaction time under each interface color is in an ascending trend, which indicates that the more blurred the mirror surface, the longer the reaction time, the worse the user experience, and the user can not even see the interface information when the mirror surface is the most blurred. When the mirror surface is not fogged, the fastest reaction time can be observed when the character color is yellow, mean=695 ms. When the degree of atomization is 7 and the character color is blue, the reaction time is the slowest, mean=968 ms. The results show that the reaction time of the blue character is the longest for all different degrees of materialization conditions. When the degree of atomization is less than 9, yellow exhibits better performance than green, but when the degree of atomization is maximum, the reaction time of yellow is greatly increased to finally exceed green.
The accuracy is used as a dependent variable, as shown in fig. 11. As the degree of atomization increases, the accuracy of the color of each character generally tends to decrease. However, when the degree of atomization is 11, the accuracy of the yellow character is highest, mean=0.948. Although the yellow character has the highest accuracy, the accuracy fluctuates to a large extent, the accuracy is the lowest at the atomization degree of 120, 9, and the accuracy is inferior to that of blue at the atomization degree of 7. Green has more stable performance in different degrees of atomization, the accuracy is around 0.9, and even the accuracy is highest at 120, mean=0.917 and 9, mean=0.906 degrees of atomization. The overall accuracy of the blue color is always not high, and the accuracy even decreases to the minimum at a degree of atomization of 7, mean=0.667.
5. Conclusion(s)
The influence of different interface information codes on the identification efficiency to be tested under the atomization condition is experimentally obtained by the following conclusion:
1) As the degree of mirror surface atomization deepens, the slower the response speed of the tested to the abnormal heart rate, the higher the error rate;
2) The yellow and green character colors have better vigilance performance under different atomization degrees, the blue color is worse, and the green and yellow color have stronger vigilance than the blue color. The difference between yellow and green is not obvious, but the yellow character has weaker stability under the influence of different atomization environments and screen brightness, and the green is more stable in appearance;
3) The brightness of the tested person at the medium screen is 150cd/m 2 The method has better visual performance, and has highest vigilance and recognition efficiency on abnormal heart rate.
Claims (6)
1. A medical equipment interface optimization method based on a cognitive mechanism in a complex visual environment comprises the following steps:
1) Designing a physiological response experiment by taking attribute factors as variables, wherein the attribute factors comprise but are not limited to the atomization degree of glasses or goggles, the screen brightness and the font color, setting the attribute factors into different independent variables, and combining the attribute factor changes provided with the different independent variables to form different characteristic parameters;
2) Combining the characteristic parameters with display information to form different types of stimulation;
3) Outputting the simulation display of the stimulus in the same time interval mode, and judging the stimulus by the tested person;
4) Establishing relation mapping between the judging efficiency of the tested person, including accuracy and response time, and different types of stimuli;
5) Analyzing the influence of each attribute factor on judging efficiency and the interaction among the attribute factors by taking the accuracy and the reaction time as dependent variables respectively so as to find out ideal parameter values and combination modes of the attribute factors for improving the interface interaction effect;
in the above-mentioned steps, the step of,
in the step 1), adopting an NxNx (N+1) factorization design, wherein the screen brightness has N grades; the font color is provided with N colors respectively; the atomization degree of the glasses or goggles comprises N atomization mirror surfaces with different degrees and 1 control variable without atomization;
in the step 2), the display information is set as a group of coding information containing parameterized information and Arabic numbers, and the coding information gives two ranges of normal information and abnormal information, so that a tested person can make judgment by combining a cognitive mechanism so as to simulate man-machine interface interaction in a real working environment, and in a group of experiments, the display information is randomly output according to the two ranges of the normal information and the abnormal information in a certain proportion.
2. The method for optimizing a medical device interface based on a cognitive mechanism in a complex visual environment according to claim 1, wherein the screen brightness is set to three grades of 70 cd/m2, 150cd/m2 and 350 cd/m 2; the font color is provided with: yellow, green, and blue; the atomization degree of the glasses or goggles has 3 atomization mirror surfaces with different degrees and 1 control variable without atomization.
3. The method for optimizing a medical device interface based on a cognitive mechanism in a complex visual environment according to claim 1, wherein in the step 2), the display information gives the characteristic parameters of the step 1) to form stimulus, and the number of the characteristic parameters is the number of the stimulus in a group of experiments.
4. The method for optimizing the interface of the medical equipment based on the cognitive mechanism in the complex visual environment according to claim 1, wherein in the step 3), the number of the testees is set to be not less than one, each testee repeats N times on one stimulus, the average reaction time is taken as the reaction time of a single testee, and the reaction time and the accuracy rate of all testees on the same stimulus are collected.
5. The method for optimizing a medical equipment interface based on a cognitive mechanism in a complex visual environment according to claim 1, wherein in the step 4), after abnormal values are removed, statistical analysis is performed on reaction time and accuracy data, and single-factor variance analysis is performed on each independent variable to obtain an average value and a standard deviation; and (3) performing multivariate analysis of variance by using statistical products and service solutions to obtain the significant difference level of the test and the significant level of the test.
6. The method for optimizing a medical device interface based on a cognitive mechanism in a complex visual environment according to claim 2, wherein the conclusion of the step 5) is that: as the atomization degree of the glasses or goggles deepens, the slower the response speed of the tested to the abnormal value is, the higher the error rate is; the yellow and green character colors have better vigilance performance under different atomization degrees of glasses or goggles, the blue color is poorer, and the green and yellow colors have stronger vigilance than the blue color; the difference between yellow and green is not obvious, but the yellow character has weaker stability under the influence of different atomization environments and screen brightness, and the green is more stable in appearance; the tested person has better visual performance under the medium screen brightness of 150cd/m2, and has highest vigilance and recognition efficiency on abnormal values.
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