Human identification based on accelerometer sensors obtained by mobile phone data

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dc.authoridOGUZ, ABDULHALIK/0000-0003-4912-7697
dc.contributor.authorOguz, Abdulhalik
dc.contributor.authorErtugrul, Omer Faruk
dc.date.accessioned2024-12-24T19:25:22Z
dc.date.available2024-12-24T19:25:22Z
dc.date.issued2022
dc.departmentSiirt Üniversitesi
dc.description.abstractIn order to achieve secure usage digitally, many different methodologies (i.e., pin code, fingerprint, face recognition) have been employed. In this study, a novel way of user identification, which can be expressed as a biometrical method, has been proposed. The proposed approach was based on the characteristics of mobile phone usage (position changes in carrying, talking, and other actions). To assess and validate the proposed method, a dataset, which consists of millions of data collected from users with the help of accelerometers for several months during their ordinary smartphone usage, was obtained. This large dataset was reduced by randomly taking 3000 samples from each of the 387 devices in the dataset. The arbitrarily selected signals were labeled according to one against all (or one vs. all) strategies. Extracted features were classified by the k nearest neighbor (kNN) and the randomized neural network (RNN), machine learning methods. It has been seen that behavior-based biometric recognition can be accomplished with mobile phone accelerometer data, with 99.994% success rates for kNN and 99.97% for RNN.
dc.identifier.doi10.1016/j.bspc.2022.103847
dc.identifier.issn1746-8094
dc.identifier.issn1746-8108
dc.identifier.scopus2-s2.0-85132592095
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.bspc.2022.103847
dc.identifier.urihttps://hdl.handle.net/20.500.12604/6385
dc.identifier.volume77
dc.identifier.wosWOS:000814367300001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier Sci Ltd
dc.relation.ispartofBiomedical Signal Processing and Control
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241222
dc.subjectAccelerometer
dc.subjectMobile phone
dc.subjectBiometric recognition
dc.subjectk nearest neighbor
dc.subjectRandomized neural network
dc.subjectOne against all
dc.titleHuman identification based on accelerometer sensors obtained by mobile phone data
dc.typeArticle

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