An improved 3D skeletons UP-Fall dataset : enhancing data quality for efficient impact fall detection

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 were conducted using various machine
learning and deep learning algorithms to benchmark the improved dataset. The results demonstrate substantial improvements
in the performance of fall detection models trained on the enhanced dataset. This contribution aims to enhance
the safety and well-being of the elderly population at risk. To support further research and development of building more
reliable impact fall detection systems, we have made the improved 3D skeleton UP-Fall dataset publicly available at this
link https://zenodo.org/records/12773013.