Advances-in-Skeleton-Based-Fall-Detection-in-RGB-Videos-From-Handcrafted-to-Deep-Learning-Approaches

Introduction

This repository contains all the tables (in CSV format) from the paper "Advances in Skeleton-Based Fall Detection in RGB Videos: From Handcrafted to Deep Learning Approaches". The reviewed works in Table 2 of our review paper are listed in Table2_reviewed_works.bib.

Additionally, this repo also includes a Fall_detection_source_code.csv file that lists public source code for fall detection, encompassing various methods beyond just skeleton-based techniques.

Citation

If our work has contributed to your research, please cite the following paper:

@ARTICLE{hoang2023advances,
  author={Hoang, Van-Ha and Lee, Jong Weon and Piran, Md. Jalil and Park, Chun-Su},
  journal={IEEE Access},
  title={Advances in Skeleton-Based Fall Detection in RGB Videos: From Handcrafted to Deep Learning Approaches},
  year={2023},
  volume={11},
  number={},
  pages={92322-92352},
  keywords={Deep learning;Fall detection;Pose estimation;Skeleton;Aging;Feature detection;Deep Learning;fall detection;pose estimation;RGB video;skeleton sequence;skeleton-based fall detection},
  doi={10.1109/ACCESS.2023.3307138}}