M. E. Perdomo, María José Fuentes, Dennis Roberto Banegas
{"title":"基于增强现实技术的程序化疏散路线实现","authors":"M. E. Perdomo, María José Fuentes, Dennis Roberto Banegas","doi":"10.1109/ICMLANT56191.2022.9996488","DOIUrl":null,"url":null,"abstract":"An AR second level-based application for general institutional evacuations was created with Unity 2020 in conjunction with Vuforia. Its function consisted of markers that serve as physical representations that mobile phones can identify to guide the user. The markers were designed with different patterns to be unique and thus be correctly recognized by the application. The objective of the implementation was to analyze the integration of RA in a scheduled general evacuation, namely, the comparison of evacuation times without application use and with application use. Based on the above, the evacuation was planned to define time, personnel, location of the markers, and time collection. Once the evacuation was completed, the statistical analysis was carried out using two sample T-tests, Dunnett's comparison, multiple regression analysis, and others. The result was that in an evacuation with many people, there was no statistical difference in the times between an evacuation with the use of the application and without the use of the application. Some of the aspects observed during the implementation that affected the evacuation time werethe processing capacity of each user's cell phone, the lighting, the trigger position, and the user's walking speed.","PeriodicalId":224526,"journal":{"name":"2022 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Implementation of Evacuation Routes with Augmented Reality in a Programmed Evacuation\",\"authors\":\"M. E. Perdomo, María José Fuentes, Dennis Roberto Banegas\",\"doi\":\"10.1109/ICMLANT56191.2022.9996488\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An AR second level-based application for general institutional evacuations was created with Unity 2020 in conjunction with Vuforia. Its function consisted of markers that serve as physical representations that mobile phones can identify to guide the user. The markers were designed with different patterns to be unique and thus be correctly recognized by the application. The objective of the implementation was to analyze the integration of RA in a scheduled general evacuation, namely, the comparison of evacuation times without application use and with application use. Based on the above, the evacuation was planned to define time, personnel, location of the markers, and time collection. Once the evacuation was completed, the statistical analysis was carried out using two sample T-tests, Dunnett's comparison, multiple regression analysis, and others. The result was that in an evacuation with many people, there was no statistical difference in the times between an evacuation with the use of the application and without the use of the application. Some of the aspects observed during the implementation that affected the evacuation time werethe processing capacity of each user's cell phone, the lighting, the trigger position, and the user's walking speed.\",\"PeriodicalId\":224526,\"journal\":{\"name\":\"2022 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT)\",\"volume\":\"18 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-12-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICMLANT56191.2022.9996488\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMLANT56191.2022.9996488","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Implementation of Evacuation Routes with Augmented Reality in a Programmed Evacuation
An AR second level-based application for general institutional evacuations was created with Unity 2020 in conjunction with Vuforia. Its function consisted of markers that serve as physical representations that mobile phones can identify to guide the user. The markers were designed with different patterns to be unique and thus be correctly recognized by the application. The objective of the implementation was to analyze the integration of RA in a scheduled general evacuation, namely, the comparison of evacuation times without application use and with application use. Based on the above, the evacuation was planned to define time, personnel, location of the markers, and time collection. Once the evacuation was completed, the statistical analysis was carried out using two sample T-tests, Dunnett's comparison, multiple regression analysis, and others. The result was that in an evacuation with many people, there was no statistical difference in the times between an evacuation with the use of the application and without the use of the application. Some of the aspects observed during the implementation that affected the evacuation time werethe processing capacity of each user's cell phone, the lighting, the trigger position, and the user's walking speed.