Fall-caused injuries are common in all types of work environments, including offices. They are the main cause of absences longer than three days, especially for small and medium-sized businesses (SMEs). However, data, data amount, data heterogeneity, and stringent processing time constraints continue to pose challenges to real-time fall detection. This work proposes a new approach based on a recurrent neural network (RNN) for Fall Detection and a Kolmogorov-Arnold Network (KAN) to estimate the time of impact of the fall. The approach is tested on SisFall, a dataset consisting of 2706 Activities of Daily Living (ADLs) and 1798 falls recorded by three sensors. The results show that the proposed approach achieves an average TPR of 82.6% and TNR of 98.4% for fall sequences and 94.4% in ADL. Besides, the Root Mean Squared Error of the estimated time of impact is approximately 160ms.
@article{arxiv.2505.24507,
title = {How can AI reduce fall injuries in the workplace?},
author = {Nicholas Cartocci and Antonios E. Gkikakis and Roberto F. Pitzalis and Fabio Pera and Maria Teresa Settino and Darwin G. Caldwell and Jesús Ortiz},
journal= {arXiv preprint arXiv:2505.24507},
year = {2025}
}