{"title":"VibroTactor: low-cost placement-aware technique using vibration echoes on mobile devices","authors":"Sungjae Hwang, K. Wohn","doi":"10.1145/2451176.2451206","DOIUrl":null,"url":null,"abstract":"In this paper, we present a low-cost placement-aware technique, called VibroTactor, which allows mobile devices to determine where they are placed (e.g., in a pocket, on a phone holder, on the bed, or on the desk). This is achieved by filtering and analyzing the acoustic signal generated when the mobile device vibrates. The advantage of this technique is that it is inexpensive and easy to deploy because it uses a microphone, which already embedded in standard mobile devices. To verify this idea, we implemented a prototype and conducted a preliminary test. The results show that this system achieves an average success rate of 91% in 12 different real-world placement sets.","PeriodicalId":253850,"journal":{"name":"IUI '13 Companion","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"16","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IUI '13 Companion","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2451176.2451206","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 16
Abstract
In this paper, we present a low-cost placement-aware technique, called VibroTactor, which allows mobile devices to determine where they are placed (e.g., in a pocket, on a phone holder, on the bed, or on the desk). This is achieved by filtering and analyzing the acoustic signal generated when the mobile device vibrates. The advantage of this technique is that it is inexpensive and easy to deploy because it uses a microphone, which already embedded in standard mobile devices. To verify this idea, we implemented a prototype and conducted a preliminary test. The results show that this system achieves an average success rate of 91% in 12 different real-world placement sets.