标题： 人工智能如何减少工作场所的跌倒伤害？ 显示英文标题

标题： How can AI reduce fall injuries in the workplace?

摘要： 跌倒造成的伤害在各种工作环境中都很常见，包括办公室。它们是超过三天的缺勤的主要原因，尤其是在小型和中型企业（SMEs）中。然而，数据量、数据异构性和严格的处理时间约束继续对实时跌倒检测构成挑战。本文提出了一种基于循环神经网络（RNN）的跌倒检测新方法，以及一个用于估计跌倒冲击时间的Kolmogorov-Arnold网络（KAN）。该方法在SisFall数据集上进行了测试，该数据集由三传感器记录的2706个日常生活活动（ADLs）和1798次跌倒组成。结果显示，所提出的方案对跌倒序列的平均TPR达到82.6%，TNR达到98.4%，而在ADL中的为94.4%。此外，估算冲击时间的均方根误差约为160毫秒。 显示英文摘要

摘要： 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.