Adriano Arra, Alessio Bianchini, Joana Chavez, Pietro Ciravolo, Fatjon Nebiu, Martina Olivelli, Gabriele Scoma, Simone Tavoletta, Matteo Zagaglia, Alessio Vecchio
{"title":"Personalized Gait-based Authentication Using UWB Wearable Devices","authors":"Adriano Arra, Alessio Bianchini, Joana Chavez, Pietro Ciravolo, Fatjon Nebiu, Martina Olivelli, Gabriele Scoma, Simone Tavoletta, Matteo Zagaglia, Alessio Vecchio","doi":"10.1145/3320435.3320473","DOIUrl":null,"url":null,"abstract":"Passive and effortless authentication of the owner of wearable devices can be achieved by building a personalized model of his/her movements during gait periods. In this paper, an authentication method based on the distances between a set of body-worn devices is proposed. The method assumes that no prior information is available about users different from the legitimate one. One-class classification methods are used to distinguish the gait segments of the owner from the gait segments of possible impostors. Experimental results show that accuracy values as high as ~87-91% can be obtained. The impact of different walking styles (normal, fast, slow, and carrying a bag) is also evaluated.","PeriodicalId":254537,"journal":{"name":"Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization","volume":"76 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3320435.3320473","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
Passive and effortless authentication of the owner of wearable devices can be achieved by building a personalized model of his/her movements during gait periods. In this paper, an authentication method based on the distances between a set of body-worn devices is proposed. The method assumes that no prior information is available about users different from the legitimate one. One-class classification methods are used to distinguish the gait segments of the owner from the gait segments of possible impostors. Experimental results show that accuracy values as high as ~87-91% can be obtained. The impact of different walking styles (normal, fast, slow, and carrying a bag) is also evaluated.
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