A Sliding Window Based Voting Classifier for Activity Sensor Based User Identification
| dc.contributor.advisor | Sha, Kewei | |
| dc.contributor.committeeMember | Wei, Wei | |
| dc.contributor.committeeMember | Yue, Kwok-Bun | |
| dc.creator | Vallam Sudhakar, Sai Ram | |
| dc.creator.orcid | 0000-0002-8264-6969 | |
| dc.date.accessioned | 2021-07-16T14:35:40Z | |
| dc.date.available | 2021-07-16T14:35:40Z | |
| dc.date.created | 2021-05 | |
| dc.date.issued | 2021-04-30 | |
| dc.date.submitted | May 2021 | |
| dc.date.updated | 2021-07-16T14:35:40Z | |
| dc.description.abstract | Identification 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.mimetype | application/pdf | |
| dc.identifier.uri | https://hdl.handle.net/10657.1/2572 | |
| dc.language.iso | en | |
| dc.subject | wearables, sensors, biometric, feature evaluation, feature selection, identification, classification | |
| dc.title | A Sliding Window Based Voting Classifier for Activity Sensor Based User Identification | |
| dc.type | Thesis | |
| dc.type.material | text | |
| thesis.degree.grantor | University of Houston-Clear Lake | |
| thesis.degree.level | Masters | |
| thesis.degree.name | Master of Science |
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