Ren Jia-Wei, Yao Jun, Wang Ju, Jiang Hao-Yun, Zhao Xue-Cheng
School of Architecture and Design, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China.
Xuzhou Yongkang Electronic Science Technology Co., Ltd, Xuzhou, Jiangsu 221004, China.
Displays. 2022 Apr;72:102148. doi: 10.1016/j.displa.2021.102148. Epub 2022 Jan 5.
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.
在全球医院的医护人员持续抗击新冠疫情的过程中，他们需要佩戴安全眼镜和护目镜，以保护眼睛免受病毒的可能传播。然而，他们长时间工作，还需要佩戴口罩和其他个人防护装备，这导致他们的护目镜起雾。而这种护目镜起雾反过来又对读取心电图（ECG）机界面信息的速度和准确性产生重大影响。为了更好地了解影响程度，本研究通过三个变量对观看界面时护目镜的起雾情况进行实验模拟：护目镜的起雾程度、屏幕亮度以及心血管读数字体的颜色。这项关于数字字体目标识别的实验研究是通过模拟心电图机的界面以及起雾护目镜下心电图机的可读性来进行的。实验结果表明，镜片起雾对识别速度有显著影响，且起雾程度与屏幕的字体颜色和亮度有显著相关性。随着屏幕亮度降低，其对识别速度的影响呈V形趋势，当屏幕亮度为150 cd/m²时响应时间最短。当护目镜起雾时，黄色和绿色字体颜色能实现更快的响应且准确率更高。总体而言，受试者使用绿色字体时表现更佳，但也存在不一致情况。在这三个变量的相互作用方面，也发现了相同的结果并可据此得出相同结论。本研究可为医护人员长时间佩戴防护眼镜情况下的医疗设备界面设计提供参考。
