{"title":"Foot position as indicator of spatial interest at public displays","authors":"Bernd Huber","doi":"10.1145/2468356.2479495","DOIUrl":null,"url":null,"abstract":"Motivated by and grounded in observations of foot patterns in a human-human dialogue, this study explores expressions of spatial interest through feet at public displays. We conducted an observation and recorded user foot orientation and position in a public information display environment leading to data about 84 interaction sessions. Our observations show that characteristic foot patterns can be matched with two user intentions: (A) Users who seek access to specific information, and (B) users who don't seek specific information. With the goal to detect intention through foot patterns, we classified characteristic foot patterns with a SVM pattern recognition algorithm, which resulted in a detection accuracy of 84.4%. This work can be valuable for researchers designing context-aware public displays.","PeriodicalId":228717,"journal":{"name":"CHI '13 Extended Abstracts on Human Factors in Computing Systems","volume":"39 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"CHI '13 Extended Abstracts on Human Factors in Computing Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2468356.2479495","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
Motivated by and grounded in observations of foot patterns in a human-human dialogue, this study explores expressions of spatial interest through feet at public displays. We conducted an observation and recorded user foot orientation and position in a public information display environment leading to data about 84 interaction sessions. Our observations show that characteristic foot patterns can be matched with two user intentions: (A) Users who seek access to specific information, and (B) users who don't seek specific information. With the goal to detect intention through foot patterns, we classified characteristic foot patterns with a SVM pattern recognition algorithm, which resulted in a detection accuracy of 84.4%. This work can be valuable for researchers designing context-aware public displays.