key: cord-0968774-4t2ip95j authors: Ren, Jia-Wei; Yao, Jun; Wang, Ju; Jiang, Hao-Yun; Zhao, Xue-Cheng title: Recognition efficiency of atypical cardiovascular readings on ECG devices through fogged goggles date: 2022-01-05 journal: Displays DOI: 10.1016/j.displa.2021.102148 sha: a1a0787977606776fcdcb30dbe6bcc87769d831f doc_id: 968774 cord_uid: 4t2ip95j In their continuing battle against the COVID-19 pandemic, medical workers in hospitals worldwide need to wear safety glasses and goggles to protect their eyes from the possible transmission of the virus. However, they work for long hours and need to wear a mask and other personal protective equipment, which causes their protective eye wear to fog up. This fogging up of eye wear, in turn, has a substantial impact in the speed and accuracy of reading information on the interface of electrocardiogram (ECG) machines. To gain a better understanding of the extent of the impact, this study experimentally simulates the fogging of protective goggles when viewing the interface with three variables: the degree of fogging of the goggles, brightness of the screen, and color of the font of the cardiovascular readings. This experimental study on the target recognition of digital font is carried out by simulating the interface of an ECG machine and readability of the ECG machine with fogged eye wear. The experimental results indicate that the fogging of the lenses has a significant impact on the recognition speed and the degree of fogging has a significant correlation with the font color and brightness of the screen. With a reduction in screen brightness, its influence on recognition speed shows a v-shaped trend, and the response time is the shortest when the screen brightness is 150 cd/m2. When eyewear is fogged, yellow and green font colors allow a quicker response with a higher accuracy. On the whole, the subjects show a better performance with the use of green font, but there are inconsistencies. In terms of the interaction among the three variables, the same results are also found and the same conclusion can be made accordingly. This research study can act as a reference for the interface design of medical equipment in events where medical staff wear protective eyewear for a long period of time. Electrocardiogram (ECG) machines are a commonly used electronic medical device for clinical diagnoses of heart abnormalities. They are used to assess heart rhythm, track cardiovascular health, or evaluate heart conditions after acute angina. Amidst the COVID-19 pandemic, ECG machines have an important role in tracking the heart health of patients with the virus, as severe COVID-19 patients may experience cardiac arrest. As such, medical staff who care and treat COVID-19 patients need to wear a complete set of personal protective equipment (PPE) which includes masks and protective eyewear, such as goggles, for long periods of time to reduce the risk of transmission. [1] [2] However, the protective lenses of the goggles can fog up due to the difference between the body and the ambient temperatures as shown in Figure 1 , which tends to be more prevalent when one is perspiring as is the case with medical staff when they are wearing a mask and other PPE. The fogging of the lenses could very well lead to erroneous readings of information on ECG monitoring devices and impacts whether the information can be read accurately and quickly. In similar situations where vision is impacted with reduced visibility, for instance, driving in dense fog, studies have found that light with a certain spectrum can penetrate fog better. The amount of penetration depends on the wavelength of the light [3] . Colors with short wavelengths, such as blue and violet, scatter more and are less visible to the eye. Conversely, colors with longer wavelengths scatter the least and penetrate through objects more strongly. The color with the longest wavelength is red [4] . Therefore, the color red universally conveys danger or serves as a warning of hazards, risk to life situations, or emergencies. Red traffic signal lights mean stop, and red flashing lights on an ambulance mean that it is on its way to an emergency. However, the focus of this study is the ECG. This device is often used to assess heart function and detect abnormal heart rhythms. Normal cardiovascular readings are usually shown in green or yellow and red is only used to alert or flag an emergency. Since red is an abnormality, and does not frequently appear on the ECG screen, this color will not be discussed here. The literature indicates that the type of material and wavelength of light will both impact the permeability of light [5] [6] . This study explores whether the results of previous studies in the literature on interface-related information and typography can change when fogged goggles are worn. A number of studies have already proven that the display layout affects the efficiency of information recognition [ To a certain extent, the brightness of the screen influences the efficiency of recognizing information on the interface. When the screen brightness is reduced, the size of the characters needs to be increased to ensure reading efficiency [11] . When the screen is very bright, the number of blinks is fewer, which increases visual fatigue and requires a higher vigilance performance [12] . This behavior can be explored through eye movement experiments [13] . However, the screen brightness is often highly correlated with icon brightness [14] and ambient light intensity [15] . Although the contrast between icon and screen brightness will not affect search accuracy [16] , a screen that is lower in brightness will cause a slower response and reduced vigilance [17] . As the ratio of the text brightness to screen brightness increases, the recognition performance will improve. However, this improvement has a threshold [18] [19] . When the screen brightness is much higher than the ambient light, glare will cause reading difficulties [20] . When the ambient brightness and the screen brightness are low or the screen brightness exceeds the brightness ratio threshold, visual fatigue will be higher. In addition, chroma and brightness also have impacts [21] [22] , so they need to be considered during the development of color classifiers [23] . In terms of text and background color contrast, Ling and van Schaik [24] found that when the text and background color have a higher contrast, the search speed (in their case, web pages) is faster. Even color combinations might have a more important role in addition to brightness and contrast [25] . Pilarczyk et al. [26] found that using different image colors will cause different pupillary responses and changes in pupil size [27] . As such, when designing an interface, the colors need to be determined with care, as it is an important means of increasing the recognition efficiency [28] [29] [30] [31] . The use of colors should always have a purpose, and appropriate color combinations can ensure the effectiveness of a display and improve cognitive efficiency [32] . However, color can be culturally specific, and the wrong color used can lead to unpleasant misunderstandings [33] . In contrast to the other colors, the three primary colors (red, green and blue) facilitate a higher accuracy rate during tasks [34] , with yellow font on a black background offering a higher degree of legibility as found in Wu et al. [35] and Huang [36] . Similarly, color variations can also improve search efficiency and reduce cognitive strain [37] . The subjects participated on a voluntary basis but received a notebook as a token of appreciation that is worth 5 dollars. Before the start of the experiment, the demographic information of the subjects was recorded, including their gender, age, study major, vision acuity and other relevant information. Subsequently, the experimental procedures were explained to them in detail. The experiment was approved by the ethics committee of the author's university, and all of the participants provided written consent prior to participating in the study. A single-task experiment was carried out which used a visual change detection paradigm and required the subjects to remember the search target first, and then subsequently perform target recognition. The stimulus in the visual recognition was parameterized information of the cardiovascular system, including the various parameter names and Arabic numbers. Ten different digital stimuli were randomly presented, and the subjects had to identify the stimulus as quickly as possible. In order to control the influence of environmental illuminance on screen brightness, the national hospital lighting standard for wards of 100 lx was adopted. The ground was used as the reference point. A 3 × 3 × 4 factorial design was used for the experiment, which considers the degree of fogging of the goggle lenses, screen brightness and font color to determine the recognition task efficiency in a short period of time. The first factor is the screen brightness, which has three levels (independent variables): 70 cd/m2, 150 cd/m2, and 350 cd/m2. The screen brightness was measured by using a screen brightness tester (SM208 portable screen luminance meter with mini light detector) against a white background. The second factor is the font color. Three font colors (independent variables) are used: yellow, green, and blue. The third factor is the degree of fogging of the goggles. Three different levels of fogging are used, and unfogged googles are used as the control (total of 4 factors). Based on these three dimensions of the initial item pool, the experiment was divided into 12 (3×4) trials which focused on screen brightness and degree of fogging of the goggles. The stimuli were presented on a simulated ECG interface at a resolution of 1920×1080 px. The standard and simulated ECG interfaces are shown in Figure 2 . The parameter font is Arial, as shown in Figure The design of the experimental program is the same as that discussed in Ho et al. [38] . In this study, the response time and accuracy are examined. The experimental program was written by using E-Prime, a software used for behavioral research. The program was installed onto a computer with a CPU frequency of 2.7 GHz. The stimulus appeared on a 14-inch, 3:2 display with a screen resolution of 1920 × 1080 pixels. Normal lighting was used for the experimental conditions. In order to control the influence of the ambient illuminance on the screen brightness variables, a standard used for lighting hospital building, The results are shown in Table 2 , where F represents the significant difference level, and P represents the significance level of the test [39] . Since the influence of screen brightness on the reaction time has no significant interaction with other factors, an ANOVA was performed on screen brightness, as shown in Figure 10 . The result shows that when the brightness is 150 cd/m2, the response time is the shortest (mean=743 ms). The response time is the longest when the screen brightness is 50 cd/m2 (mean=863 ms), and the response time is moderate at the highest screen brightness. However, the rate of accuracy with the yellow font is the highest (mean=0.948) when the degree of fogging is 11. Although the highest accuracy rate is found when the yellow font is used, this rate also fluctuates greatly. The accuracy rate is the lowest at fogging levels of 9 and 120, and the accuracy rate is second only to blue when the degree of fogging is 7. The green font allows the viewer to provide a relatively stable performance within the different degrees of fogginess, and the accuracy rate is around 0.9, and even the highest when the lenses are very fogged up at a level of 9 (mean=0.906) and 120 (mean=0.917). The overall accuracy rate when using a blue font is always low, and the accuracy rate even deteriorates to the lowest value at a fogginess of 7 (mean=0.667). Green-70cd/m2 Yellow-70cd/m2 Blue-70cd/m2 Green-150cd/m2 Yellow-150cd/m2 Blue-150cd/m2 Green-350cd/m2 Yellow-350cd/m2 Blue-350cd/m2 The degree of fogging of the goggles Colour-Luminance Figure 13 . Interaction among font color, degree of fogging of the goggles and screen brightness in terms of reaction time. The effect of the interactions among font color, screen brightness and degree of fogging on the reaction time is shown in Figure 13 . Since the interactions among these three factors are more complicated, the focus here is mainly on how they can improve the recognition efficiency in a fogged environment. A degree of fogginess of 7 along with a screen brightness of 50 cd/m2 consists of a very extreme environment and would affect the surrounding environment anyway, and as such, will not be further elaborated. When the degree of fogginess is 11, the screen brightness is 350 cd/m2, and color of the font is yellow, the recognition rate is the fastest (mean=642). However, when the degree of fogginess is 9, the recognition rate will be reduced (mean=734). At this time, the recognition efficiency of the green font with a brightness of 150 cd/m2 is the highest (mean=665). Finally, when the degree of fogginess is 11, the recognition efficiency is still relatively fast (mean=671). The recognition efficiency is the poorest with a blue font color and brightness of 350 cd/m2 even when the lenses are not fogged up (mean=633), but when the lenses start to fog up or the brightness is reduced, the reaction time is significantly higher. The purpose of this study is to examine the effect of character coding on ECG interfaces on the recognition efficiency of medical staff who wear goggles for a long differ from those of Buchner et al. [40] and bin Zaini et al. [4] who showed that high levels of screen brightness enhance visual performance and proofreading task performance. with a brightness of 150cd/m2 as discussed above, green font provides better results than yellow and blue fonts. This is inconsistent with previous research results. Luria et al. [43] and Al-Harkan and Ramadan [44] both found significant differences in the impact of different font colors on the error rate. The former indicated that yellow target letters on a dark background produce the fewest errors while the latter found that green is more superior unless the character contains a square. Shieh and Chen [45] found that blue and black backgrounds allow for better cognitive efficiency. The reason for the difference between the results of previous research and those of this research study may be that yellow and blue could have better legibility, but the subjects are not sensitive to these two colors, and the experiment is carried out in a fogged environment. Green light penetrates fog more readily than blue because it has a higher wavelength and excels the penetration ability of yellow. Of course, the difference between yellow and green is not significant, but yellow is weakly correlated with different fogged conditions and screen brightness. [46] and Kurniawan et al. [47] [48] found that the error rates are This study uses an analog ECG interface to carry out visual target recognition experiments with digital font. Variables include the font color, brightness of the device screen, and degree of fogging of the protective goggles. The interactions among these three variables have significant impacts on recognition efficiency. Green is recommended as the font color for ECGs with a screen brightness of 150 cd/m2 if protective goggles are worn for a low error rate, consistency in reading the information, and the fastest response time. With increased fogging and therefore less visibility, the recognition efficiency of digital characters is significantly reduced. Therefore, appropriately reducing the display brightness of an ECG device and using green as the font color while viewing the interface with fogged up glasses will enhance the efficiency of reading the digital information. 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The authors declare no potential conflict of interest. All authors have approved the manuscript for publication. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.