TY  - JOUR
T1  - Resizing Method for Applying RF-based Data to ViT in Human Activity Recognition
AU  - Park, Jeongjun 
AU  - Bahk, Saewoong 
JO  - The Journal of Korean Institute of Communications and Information Sciences
PY  - 2025
DA  - 2025/1/1
DO  - 10.7840/kics.2025.50.5.725
KW  - Vision Transformer
KW  - RF-based data
AB  - 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%.