Graduation Year
2018
Document Type
Thesis
Degree
M.A.
Degree Name
Master of Arts (M.A.)
Degree Granting Department
Mathematics and Statistics
Major Professor
Kandethody Ramachandran, Ph.D.
Committee Member
Gangaram S. Ladde, Ph.D.
Committee Member
Seung-Yeop Lee, Ph.D.
Keywords
Convolutional neural network, Deep learning, Human activity recognition, Transfer Learning
Abstract
Human activity recognition (HAR) based on time series data is the problem of classifying various patterns. Its widely applications in health care owns huge commercial benefit. With the increasing spread of smart devices, people have strong desires of customizing services or product adaptive to their features. Deep learning models could handle HAR tasks with a satisfied result. However, training a deep learning model has to consume lots of time and computation resource. Consequently, developing a HAR system effectively becomes a challenging task. In this study, we develop a solid HAR system using Convolutional Neural Network based on transfer learning, which can eliminate those barriers.
Scholar Commons Citation
Pang, Jinyong, "Human Activity Recognition Based on Transfer Learning" (2018). USF Tampa Graduate Theses and Dissertations.
https://digitalcommons.usf.edu/etd/7558
