An Active Deep Neural Network Architecture for Human Activity Recognition
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.
Collections
Related items
Showing items related by title, author, creator and subject.
-
Cloning, expression and activity of B cell Activating Factor
Paul, Caitlin (Kalamazoo College, 2008)The preimmune antibody repertoire plays an important role in immune protection through out the life of the organism. In rabbits, B cells develop in the bone marrow and migrate to the gut associated lymphoid tissues (GALT), ... -
Active Transport of Sodium across Isolated Frog Skin and Toad Bladder
McKnight, J. Robert (John Robert) (Kalamazoo College, 1967) -
Improving the Cost of Producing Lignocellulosic Bioethanol: Screening Soil Microbes for Secreted Cellulolytic and Xylanolytic Activity
Lapka, Alexander C. (Kalamazoo College, 2010)Fuel ethanol is already produced in the United States from corn and other grains rich in starch. A new generation of biofuels, made from lignocellulosic biomass such as wood, grass and agricultural waste, is currently being ...