@article{ART003120905},
author={Beom Kwon},
title={Improving BMI Classification Accuracy with Oversampling and 3-D Gait Analysis on Imbalanced Class Data},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2024},
volume={29},
number={9},
pages={9-23},
doi={10.9708/jksci.2024.29.09.009}
TY - JOUR
AU - Beom Kwon
TI - Improving BMI Classification Accuracy with Oversampling and 3-D Gait Analysis on Imbalanced Class Data
JO - Journal of The Korea Society of Computer and Information
PY - 2024
VL - 29
IS - 9
PB - The Korean Society Of Computer And Information
SP - 9
EP - 23
SN - 1598-849X
AB - In this study, we propose a method to improve the classification accuracy of body mass index (BMI) estimation techniques based on three-dimensional gait data. In previous studies on BMI estimation techniques, the classification accuracy was only about 60%. In this study, we identify the reasons for the low BMI classification accuracy. According to our analysis, the reason is the use of the undersampling technique to address the class imbalance problem in the gait dataset. We propose applying oversampling instead of undersampling to solve the class imbalance issue. We also demonstrate the usefulness of anthropometric and spatiotemporal features in gait data-based BMI estimation techniques. Previous studies evaluated the usefulness of anthropometric and spatiotemporal features in the presence of undersampling techniques and reported that their combined use leads to lower BMI estimation performance than when using either feature alone. However, our results show that using both features together and applying an oversampling technique achieves state-of-the-art performance with 92.92% accuracy in the BMI estimation problem.
KW - Anthropometric Feature;Body Mass Index;Class Imbalance;Machine Learning;Oversampling Technique;Spatiotemporal Feature
DO - 10.9708/jksci.2024.29.09.009
ER -
Beom Kwon. (2024). Improving BMI Classification Accuracy with Oversampling and 3-D Gait Analysis on Imbalanced Class Data. Journal of The Korea Society of Computer and Information, 29(9), 9-23.
Beom Kwon. 2024, "Improving BMI Classification Accuracy with Oversampling and 3-D Gait Analysis on Imbalanced Class Data", Journal of The Korea Society of Computer and Information, vol.29, no.9 pp.9-23. Available from: doi:10.9708/jksci.2024.29.09.009
Beom Kwon "Improving BMI Classification Accuracy with Oversampling and 3-D Gait Analysis on Imbalanced Class Data" Journal of The Korea Society of Computer and Information 29.9 pp.9-23 (2024) : 9.
Beom Kwon. Improving BMI Classification Accuracy with Oversampling and 3-D Gait Analysis on Imbalanced Class Data. 2024; 29(9), 9-23. Available from: doi:10.9708/jksci.2024.29.09.009
Beom Kwon. "Improving BMI Classification Accuracy with Oversampling and 3-D Gait Analysis on Imbalanced Class Data" Journal of The Korea Society of Computer and Information 29, no.9 (2024) : 9-23.doi: 10.9708/jksci.2024.29.09.009
Beom Kwon. Improving BMI Classification Accuracy with Oversampling and 3-D Gait Analysis on Imbalanced Class Data. Journal of The Korea Society of Computer and Information, 29(9), 9-23. doi: 10.9708/jksci.2024.29.09.009
Beom Kwon. Improving BMI Classification Accuracy with Oversampling and 3-D Gait Analysis on Imbalanced Class Data. Journal of The Korea Society of Computer and Information. 2024; 29(9) 9-23. doi: 10.9708/jksci.2024.29.09.009
Beom Kwon. Improving BMI Classification Accuracy with Oversampling and 3-D Gait Analysis on Imbalanced Class Data. 2024; 29(9), 9-23. Available from: doi:10.9708/jksci.2024.29.09.009
Beom Kwon. "Improving BMI Classification Accuracy with Oversampling and 3-D Gait Analysis on Imbalanced Class Data" Journal of The Korea Society of Computer and Information 29, no.9 (2024) : 9-23.doi: 10.9708/jksci.2024.29.09.009
