Show simple item record

dc.contributor.advisorMeling, Hein
dc.contributor.advisorEftestøl, Trygve Christian
dc.contributor.advisorFreyer, Ståle
dc.contributor.authorBottenvik, Vebjørn Kaldahl
dc.date.accessioned2019-10-04T11:17:33Z
dc.date.available2019-10-04T11:17:33Z
dc.date.issued2019-06-14
dc.identifier.urihttp://hdl.handle.net/11250/2620311
dc.descriptionMaster's thesis in Automation and signal processingnb_NO
dc.description.abstractBiometric authentication is currently being used for numerous devices; such as mobile phones, computers, etc. However, for now, the only authentication methods for wearable devices are those of passwords and pin codes. The newest instance of the Apple Watch series 4. has an integrated Electrocardiogram (ECG) recording possibility that could be used for biometric authentication. Having the possibility for biometric authentication on wearable devices could potentially provide seamless authentication applications as the wearable device is always on standby. The objective of this thesis was to test biometric authentication based on ECG signals recorded on wearable/mobile devices. By collecting data from a set of volunteers with recordings performed under different circumstances such as; resting heart rate, increased heart rate after exercise, and noisy signals while in motion. By performing denoising and feature extraction, various machine learning models were trained and evaluated to provide a classification model that performed well on the variety of ECG signals. The classification model was further used to present a biometric authentication system. The biometric authentication system presented in this thesis was tested on three different sets of acquired ECG data. Biometric authentication based on ECG signals recorded with resting heart rates correctly authenticated 17/19 subjects, resulting in an acceptance rate of 89.5%. For the recordings after physical activity and in motion, the authentication system correctly authenticated 52.6% (10/19) and 31.6% (6/19) of the subjects. An additional subject that had been excluded from the system did not get authenticated for either of the different recordings. Overall, no subjects were misclassified as other subjects.nb_NO
dc.language.isoengnb_NO
dc.publisherUniversity of Stavanger, Norwaynb_NO
dc.relation.ispartofseriesMasteroppgave/UIS-TN-IDE/2019;
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.subjectelectrocardiogramnb_NO
dc.subjectinformasjonsteknologinb_NO
dc.subjectkybernetikk og signalbehandlingnb_NO
dc.subjectauthenticationnb_NO
dc.subjectbiometricsnb_NO
dc.subjectmachine learningnb_NO
dc.subjectautomatiseringnb_NO
dc.subjectbiometrinb_NO
dc.titleBiometric Authentication from ECG Signals on Wearable Devicesnb_NO
dc.typeMaster thesisnb_NO
dc.subject.nsiVDP::Technology: 500::Information and communication technology: 550nb_NO


Files in this item

Thumbnail

This item appears in the following Collection(s)

  • Master's theses (TN-IDE) [281]
    Masteroppgaver i Teknologi/sivilingeniør: informasjonsteknologi, datateknikk / Masteroppgaver i Teknologi/sivilingeniør: kybernetikk, signalbehandling

Show simple item record

Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal