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dc.contributor.advisorSha, Kewei
dc.creatorVallam Sudhakar, Sai Ram
dc.date.accessioned2021-07-16T14:35:40Z
dc.date.available2021-07-16T14:35:40Z
dc.date.created2021-05
dc.date.issued2021-04-30
dc.date.submittedMay 2021
dc.identifier.urihttps://hdl.handle.net/10657.1/2572
dc.description.abstractIdentification is the core of any authentication protocol design as the purpose of the authentication is to verify the user’s identity. The efficient establishment and verification of identity remain a big challenge. Recently, biometrics-based identification algorithms gained popularity as a means of identifying individuals using their unique biological characteristics. In this thesis, we propose a novel and efficient identification framework, ActID, which can identify a user based on his/her hand motion while walking. ActID not only selects a set of high-quality features based on Optimal Feature Evaluation and Selection and Correlation-based Feature Selection algorithms but also includes a novel sliding window based voting classifier. Therefore, it achieves several important design goals for gait authentication based on resource-constrained devices, including lightweight and real-time classification, high identification accuracy, a minimum number of sensors, and a minimum amount of data collected. Performance evaluation shows that ActID is cost-effective and easily deployable, selects only a minimum number of 10 high-quality features, uses only accelerometer sensor and increases the cost efficiency of user identification, collects only a small amount of 10 seconds of activity data, satisfies real-time requirements, and achieves a high identification accuracy of 100% when applied to a 30 user dataset.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.subjectwearables, sensors, biometric, feature evaluation, feature selection, identification, classification
dc.titleA Sliding Window Based Voting Classifier for Activity Sensor Based User Identification
dc.typeThesis
dc.date.updated2021-07-16T14:35:40Z
thesis.degree.grantorUniversity of Houston-Clear Lake
thesis.degree.levelMasters
thesis.degree.nameMaster of Science
dc.contributor.committeeMemberWei, Wei
dc.contributor.committeeMemberYue, Kwok-Bun
dc.type.materialtext
dc.creator.orcid0000-0002-8264-6969


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