An Active Deep Neural Network Architecture for Human Activity Recognition

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Authors
Norgaard, Skyler J.
Issue Date
2018
Type
Thesis
Language
en_US
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Abstract
Wearable 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.
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iv, 34 p.
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U.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.
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