@article{MEE143111,
title = "Resizing Method for Applying RF-based Data to ViT in Human Activity Recognition",
journal = "The Journal of Korean Institute of Communications and Information Sciences",
year = "2025",
issn = "1226-4717",
doi = "10.7840/kics.2025.50.5.725",
author = "Jeongjun Park, Saewoong Bahk",
keywords = "Vision Transformer, RF-based data",
abstract = "This paper applies RF-based data, obtained through the commonly used Radio Frequency (RF) approach in human activity recognition (HAR), to the Vision Transformer (ViT), a state-of-the-art machine learning method for image classification.
Through this process, we analyze the challenges arising from applying RF-based data, which have different sizes compared to standard image dimensions, to ViT. To address these challenges, we propose various input resizing methods. Furthermore, through a comparison of these resizing methods, we identify the most effective resizing approach for RF-based data, achieving an average accuracy improvement of 9.57%."
}