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dc.contributor.advisorErdi, Peter
dc.contributor.authorNorgaard, Skyler J.
dc.date.accessioned2018-05-05T16:35:10Z
dc.date.available2018-05-05T16:35:10Z
dc.date.issued2018
dc.identifier.urihttp://hdl.handle.net/10920/35740
dc.descriptioniv, 34 p.en_US
dc.description.abstractWearable technologies such as iPhones and smart watches have become ubiquitous in our modern world. As a result, massive amounts of sensor data are collected on a daily basis. This data can be leveraged to recognize human activity. Namely, neural networks have shown promise in recognizing activity with minimal data transformation and preprocessing. In this work, a novel deep neural network architecture is proposed to do just this. An active learning submodule is also included to reconfigure the algorithm when the setting changes. The effectiveness of this approach is validated using two real world human activity recognition datasets. A brief overview of machine learning, the history of neural networks, and the specific neural network architectures used are also included in this work.en_US
dc.format.mimetypeapplication/pdf
dc.language.isoen_USen_US
dc.relation.ispartofKalamazoo College Physics Senior Individualized Projects Collection
dc.relation.ispartofseriesSenior Individualized Projects. Physics.;
dc.rightsU.S. copyright laws protect this material. Commercial use or distribution of this material is not permitted without prior written permission of the copyright holder. All rights reserved.
dc.titleAn Active Deep Neural Network Architecture for Human Activity Recognitionen_US
dc.typeThesisen_US
KCollege.Access.ContactIf you are not a current Kalamazoo College student, faculty, or staff member, email dspace@kzoo.edu to request access to this thesis.


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  • Physics Senior Individualized Projects [317]
    This collection includes Senior Individualized Projects (SIP's) completed in the Physics Department. Abstracts are generally available to the public, but PDF files are available only to current Kalamazoo College students, faculty, and staff.

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