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Author Blasco, Jorge ♦ Chen, Thomas M. ♦ Tapiador, Juan ♦ Peris-Lopez, Pedro
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©2016
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword ECG ♦ PPG ♦ Wearables ♦ Accelerometer ♦ Authentication ♦ Biometrics ♦ Biosignals ♦ Heart sound ♦ Machine learning ♦ Sensor
Abstract The growing popularity of wearable devices is leading to new ways to interact with the environment, with other smart devices, and with other people. Wearables equipped with an array of sensors are able to capture the owner’s physiological and behavioural traits, thus are well suited for biometric authentication to control other devices or access digital services. However, wearable biometrics have substantial differences from traditional biometrics for computer systems, such as fingerprints, eye features, or voice. In this article, we discuss these differences and analyse how researchers are approaching the wearable biometrics field. We review and provide a categorization of wearable sensors useful for capturing biometric signals. We analyse the computational cost of the different signal processing techniques, an important practical factor in constrained devices such as wearables. Finally, we review and classify the most recent proposals in the field of wearable biometrics in terms of the structure of the biometric system proposed, their experimental setup, and their results. We also present a critique of experimental issues such as evaluation and feasibility aspects, and offer some final thoughts on research directions that need attention in future work.
Description Author Affiliation: Department of Electrical and Electronic Engineering, City University London, London, United Kingdom (Blasco, Jorge; Chen, Thomas M.); Department of Computer Science, Universidad Carlos III de Madrid, Leganes, Spain (Tapiador, Juan; Peris-Lopez, Pedro)
ISSN 03600300
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2016-09-01
Publisher Place New York
e-ISSN 15577341
Journal ACM Computing Surveys (CSUR)
Volume Number 49
Issue Number 3
Page Count 35
Starting Page 1
Ending Page 35

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Source: ACM Digital Library