论文标题
ERGOEXPLORER:视频收集的互动人体工程学风险评估
ErgoExplorer: Interactive Ergonomic Risk Assessment from Video Collections
论文作者
论文摘要
由于意识的提高,人体工程学风险评估现在比过去更频繁地进行。基于对工作场所的专家辅助观察和手动填写评分表的常规风险评估评估仍然是主要的。数据分析通常是在关键时刻进行的,尽管不支持上下文信息和随时间变化。在本文中,我们介绍了ErgoExplorer,这是一种用于风险评估数据的交互式视觉分析的系统。与当前的实践相反,我们专注于跨越多个动作和多个工人的数据,同时保留所有上下文信息。数据将自动从视频流中提取。基于经过仔细研究的分析任务,我们介绍了新的观点及其相应的相互作用。这些观点还结合了特定领域的得分表,以确保域专家轻松采用。所有视图都集成到ErgoExplorer中,该视图依赖于协调的多个视图来通过互动来促进分析。 Ergoexplorer使得在跨越多个操作的长时间会议中,首次可以检查各个身体部位的风险评估之间的复杂关系。新引入的方法支持几个详细级别的分析和探索,从一般概述到如有必要的话,请直到检查视频流中的单个帧。我们说明了将其应用于几个数据集的新提出的方法的有用性。
Ergonomic risk assessment is now, due to an increased awareness, carried out more often than in the past. The conventional risk assessment evaluation, based on expert-assisted observation of the workplaces and manually filling in score tables, is still predominant. Data analysis is usually done with a focus on critical moments, although without the support of contextual information and changes over time. In this paper we introduce ErgoExplorer, a system for the interactive visual analysis of risk assessment data. In contrast to the current practice, we focus on data that span across multiple actions and multiple workers while keeping all contextual information. Data is automatically extracted from video streams. Based on carefully investigated analysis tasks, we introduce new views and their corresponding interactions. These views also incorporate domain-specific score tables to guarantee an easy adoption by domain experts. All views are integrated into ErgoExplorer, which relies on coordinated multiple views to facilitate analysis through interaction. ErgoExplorer makes it possible for the first time to examine complex relationships between risk assessments of individual body parts over long sessions that span multiple operations. The newly introduced approach supports analysis and exploration at several levels of detail, ranging from a general overview, down to inspecting individual frames in the video stream, if necessary. We illustrate the usefulness of the newly proposed approach applying it to several datasets.