IEEE J Biomed Health Inform. 2021 Jul;25(7):2768-2776. doi: 10.1109/JBHI.2020.3046701. Epub 2021 Jul 27.
Falls are leading causes of nonfatal injuries in workplaces which lead to substantial injury and economic consequences. To help avoid fall injuries, safety managers usually need to inspect working areas routinely. However, it is difficult for a limited number of safety managers to inspect fall hazards instantly especially in large workplaces. To address this problem, a novel fall hazard identification method is proposed in this paper which makes it possible for all workers to report the potential hazards automatically. This method is based on the fact that people use different gaits to get across different floor surfaces. Through analyzing gait patterns, potential fall hazards could be identified automatically. In this research, Smart Insole, an insole shaped wearable system for gait analysis, was applied to measure gait patterns for fall hazard identification. Slips and trips are the focus of this study since they are two main causes of falls in workplaces. Five effective gait features were extracted to train a Support Vector Machine (SVM) model for recognizing slip hazard, trip hazard, and safe floor surfaces. Experiment results showed that fall hazards could be recognized with high accuracy (98.1%).
在工作场所,跌倒导致的非致命伤害是主要原因,会造成严重的伤害和经济后果。为了帮助避免跌倒伤害,安全经理通常需要定期检查工作区域。然而,有限数量的安全经理很难立即检查所有的跌倒危险,尤其是在大型工作场所。为了解决这个问题,本文提出了一种新的跌倒危险识别方法,使所有工人都能够自动报告潜在的危险。该方法基于人们使用不同步态穿过不同地面的事实。通过分析步态模式,可以自动识别潜在的跌倒危险。在这项研究中,应用了智能鞋垫(Smart Insole),这是一种用于步态分析的鞋垫形状可穿戴系统,用于测量步态模式以进行跌倒危险识别。滑倒和绊倒是本研究的重点,因为它们是工作场所跌倒的两个主要原因。提取了五个有效的步态特征来训练支持向量机(Support Vector Machine,SVM)模型,以识别滑倒危险、绊倒危险和安全地面。实验结果表明,跌倒危险可以被准确识别(98.1%)。