An Improved 3D Skeletons UP-Fall Dataset: Enhancing Data Quality for Efficient Impact Fall Detection
Tresor Y. Koffi, Youssef Mourchid, Mohammed Hindawi, Yohan Dupuis

TL;DR
This paper improves the UP-Fall dataset by adding accurate 3D skeleton data, enhancing impact fall detection capabilities and benchmarking various algorithms to demonstrate performance gains.
Contribution
The study introduces a high-quality 3D skeleton version of the UP-Fall dataset, addressing previous data limitations for more reliable impact fall detection.
Findings
Significant performance improvements in fall detection models using the enhanced dataset
Enhanced dataset reduces confusion between impact and non-impact events
Benchmarking results validate the dataset's effectiveness for future research
Abstract
Detecting impact where an individual makes contact with the ground within a fall event is crucial in fall detection systems, particularly for elderly care where prompt intervention can prevent serious injuries. The UP-Fall dataset, a key resource in fall detection research, has proven valuable but suffers from limitations in data accuracy and comprehensiveness. These limitations cause confusion in distinguishing between non-impact events, such as sliding, and real falls with impact, where the person actually hits the ground. This confusion compromises the effectiveness of current fall detection systems. This study presents enhancements to the UP-Fall dataset aiming at improving it for impact fall detection by incorporating 3D skeleton data. Our preprocessing techniques ensure high data accuracy and comprehensiveness, enabling a more reliable impact fall detection. Extensive experiments…
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Taxonomy
TopicsContext-Aware Activity Recognition Systems · Balance, Gait, and Falls Prevention · Human Pose and Action Recognition
