Electronic Theses and Dissertations
Date
2025
Document Type
Thesis
Degree Name
Master of Science
Department
Electrical & Computer Engineering
Committee Chair
Bonny Banerjee
Committee Member
Peter Lau
Committee Member
Shelby Peel
Abstract
Our goal is to develop a uniform, relatively lightweight model that can yield high accuracy on a wide variety of multimodal time-series classification problems. To that end, we propose a model that fuses multiple convolutional neural networks (CNNs), one for each modality, at the decision level using a multilayer perceptron. The model is trained on randomized (as opposed to sequential) signal windows, which enhances generalization across diverse time segments. We evaluate the model on two real-world problems: emotion recognition using physiological signals and action recognition using wearable sensor data. In both cases, our model achieves accuracy comparable to state-of-the-art approaches while being considerably smaller in size. This efficiency makes it well-suited for edge computing scenarios, where memory and compute resources are limited. The modular design also allows for seamless scaling across additional modalities or domains without requiring much architectural changes. Overall, our method provides a practical and adaptable framework for multimodal time-series classification.
Library Comment
Dissertation or thesis originally submitted to ProQuest.
Notes
Embargoed until 2027-07-15
Recommended Citation
Nepal, Rabin, "Multimodal time-series classification using CNN" (2025). Electronic Theses and Dissertations. 3820.
https://digitalcommons.memphis.edu/etd/3820
Comments
Data is provided by the student