Publication Date

Spring 2019

Degree Type

Master's Project

Degree Name

Master of Science (MS)


Computer Science

First Advisor

Robert Chun

Second Advisor

Suneuy Kim

Third Advisor

Ashwin Vijaysai


Classification, dimensionality reduction, neural networks, human activity recognition


In the recent years, human activity recognition has been widely popularized by a lot of smartphone manufacturers and fitness tracking companies. It has allowed us to gain a deeper insight into our physical health on a daily basis. However, with the evolution of fitness tracking devices and smartphones, the amount of data that is being captured by these devices is growing exponentially. This paper aims at understanding the process of dimensionality reduction such as PCA so that the data can be used to make meaningful predictions along with novel techniques using autoencoders with different activation functions. The paper also looks into how using autoencoders allows us to better capture the relations between features in the data. It also covers some of the classification techniques such as k-Nearest Neighbors, SVM and Random forest that are currently being used for activity recognition that have shown promising results.