Child, Daniel_MCS_2023

Title Child, Daniel_MCS_2023
Alternative Title Comparing Classifiers vs Clustering Algorithms on an Audio Gait Dataset
Creator Child, Daniel
Collection Name Master of Computer Science
Description The following masters of computer science thesis explores the use of machine learning to identify individuals based upon their gait.
Abstract Researchers have explored different methods for the identification of persons by the sound generated when they walk (i.e., gait). Using machine learning they can train a system to identify individuals. In buildings where only authorized personnel are allowed a system like this could provide an additional layer of authentication. In this thesis, audio was captured by participants walking past a series of microphones to capture their gait. The Fast Fourier Transform algorithm was used to process the data into a form usable by machine learning algorithms. A comparison is made between classification and clustering algorithms, and several machine learning algorithms are tested in each category. Also mentioned are three methods for feature elimination and their impact on performance. Lastly, tests are conducted to see how including a backpack can alter the results, which tests the ability for the algorithms to generalize under normal human behavioral changes.
Subject Algorithms; Computer programming; Machine learning; Computer science
Keywords gait; machine learning; classification; cluster; unsupervised; python; raspberry pi; Fourier; fast fourier transform
Digital Publisher Stewart Library, Weber State University, Ogden, Utah, United States of America
Date 2023
Medium Thesis
Type Text
Access Extent 49 page PDF; 2.6 MB
Language eng
Rights The author has granted Weber State University Archives a limited, non-exclusive, royalty-free license to reproduce their theses, in whole or in part, in electronic or paper form and to make it available to the general public at no charge. The author retains all other rights.
Source University Archives Electronic Records: Master of Computer Science. Stewart Library, Weber State University
OCR Text Show
Format application/pdf
ARK ark:/87278/s6mbp3mx
Setname wsu_smt
ID 96897
Reference URL https://digital.weber.edu/ark:/87278/s6mbp3mx