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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.24507 |
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| _version_ | 1866909629215145984 |
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| author | Cartocci, Nicholas Gkikakis, Antonios E. Pitzalis, Roberto F. Pera, Fabio Settino, Maria Teresa Caldwell, Darwin G. Ortiz, Jesús |
| author_facet | Cartocci, Nicholas Gkikakis, Antonios E. Pitzalis, Roberto F. Pera, Fabio Settino, Maria Teresa Caldwell, Darwin G. Ortiz, Jesús |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_24507 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | How can AI reduce fall injuries in the workplace? Cartocci, Nicholas Gkikakis, Antonios E. Pitzalis, Roberto F. Pera, Fabio Settino, Maria Teresa Caldwell, Darwin G. Ortiz, Jesús Signal Processing 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. |
| title | How can AI reduce fall injuries in the workplace? |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2505.24507 |