Editorial
The Future of Fall Prevention: Integrating OpenPose with Cutting-Edge ML Models
@ARTICLE{10.4108/eetpht.11.9013, author={Shina Samuel Kolawole and Gautam Siddharth Kashyap and Olamide Emmanuel Kolawole and Miao Yu}, title={The Future of Fall Prevention: Integrating OpenPose with Cutting-Edge ML Models}, journal={EAI Endorsed Transactions of Pervasive Health and Technology}, volume={11}, number={1}, publisher={EAI}, journal_a={PHAT}, year={2025}, month={4}, keywords={Deep Learning, Fall, Healthcare, Machine Learning, OpenPose}, doi={10.4108/eetpht.11.9013} }- Shina Samuel Kolawole
Gautam Siddharth Kashyap
Olamide Emmanuel Kolawole
Miao Yu
Year: 2025
The Future of Fall Prevention: Integrating OpenPose with Cutting-Edge ML Models
PHAT
EAI
DOI: 10.4108/eetpht.11.9013
Abstract
The research paper aims to assess ML models for video-recorded gaits with an aim of classifying people into high or low risks to fall groups. Several ML algorithms were tried employing OpenPose for CV, with RF showing the best outcomes: 93% accuracy along with F1-score as well as balanced sensitivity (93.50%) as well as specificity (92.50%). Some important determining factors were speed per unit distance, angle among other statistical measures. In comparison to wearables-based DL approaches plus traditional fall detection methods, this study’s approach showed higher accuracy and adaptability within health care settings.
Copyright © 2025 S. S. Kolawole et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NCSA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
