The Future of Fall Prevention: Integrating OpenPose with Cutting-Edge ML Models

Shina Samuel Kolawole, Gautam Siddharth Kashyap, Olamide Emmanuel Kolawole, Miao Yu

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.