Institute for Occupational Safety and Health of the German Social Accident Insurance (IFA), 53757 Sankt Augustin, Germany.
RheinAhrCampus, Koblenz University of Applied Sciences, 53424 Remagen, Germany.
Sensors (Basel). 2024 Aug 20;24(16):5381. doi: 10.3390/s24165381.
Slip, trip, and fall (STF) accidents cause high rates of absence from work in many companies. During the 2022 reporting period, the German Social Accident Insurance recorded 165,420 STF accidents, of which 12 were fatal and 2485 led to disability pensions. Particularly in the traffic, transport and logistics sector, STF accidents are the most frequently reported occupational accidents. Therefore, an accurate detection of near-falls is critical to improve worker safety. Efficient detection algorithms are essential for this, but their performance heavily depends on large, well-curated datasets. However, there are drawbacks to current datasets, including small sample sizes, an emphasis on older demographics, and a reliance on simulated rather than real data. In this paper we report the collection of a standardised kinematic STF dataset from real-world STF events affecting parcel delivery workers and steelworkers. We further discuss the use of the data to evaluate dynamic stability control during locomotion for machine learning and build a standardised database. We present the data collection, discuss the classification of the data, present the totality of the data statistically, and compare it with existing databases. A significant research gap is the limited number of participants and focus on older populations in previous studies, as well as the reliance on simulated rather than real-world data. Our study addresses these gaps by providing a larger dataset of real-world STF events from a working population with physically demanding jobs. The population studied included 110 participants, consisting of 55 parcel delivery drivers and 55 steelworkers, both male and female, aged between 19 and 63 years. This diverse participant base allows for a more comprehensive understanding of STF incidents in different working environments.
滑倒、绊倒和跌倒（STF）事故在许多公司导致高缺勤率。在 2022 年报告期内，德国社会保险记录了 165420 起 STF 事故，其中 12 起是致命的，2485 起因导致残疾抚恤金。特别是在交通、运输和物流部门，STF 事故是最常报告的职业事故。因此，准确检测接近跌倒对于提高工人安全至关重要。为此，需要高效的检测算法，但它们的性能严重依赖于大型、精心策划的数据集。然而，当前数据集存在一些缺点，包括样本量小、对老年人群的重视、以及对模拟数据而非真实数据的依赖。在本文中，我们报告了从影响包裹投递员和钢铁工人的真实 STF 事件中收集的标准化运动学 STF 数据集。我们进一步讨论了使用该数据评估机器学习中运动时的动态稳定性控制，并构建了一个标准化数据库。我们介绍了数据收集过程，讨论了数据的分类，从统计学角度展示了数据的总体情况，并将其与现有数据库进行了比较。以前的研究中存在一个显著的研究差距，即参与者人数有限，且重点关注老年人群，以及对模拟数据而非真实世界数据的依赖。我们的研究通过提供来自体力要求高的工作人群的更大规模的真实 STF 事件数据集来解决这些差距。所研究的人群包括 110 名参与者，其中 55 名是包裹投递司机，55 名是钢铁工人，男女皆有，年龄在 19 至 63 岁之间。这个多样化的参与者基础允许更全面地了解不同工作环境中的 STF 事件。
