The ongoing trend towards Industry 4.0 has revolutionised ordinary workplaces, profoundly changing the role played by humans in the production chain. Research on ergonomics in industrial settings mainly focuses on reducing the operator's physical fatigue and discomfort to improve throughput and avoid safety hazards. However, as the production complexity increases, the cognitive resources demand and mental workload could compromise the operator's performance and the efficiency of the shop floor workplace. State-of-the-art methods in cognitive science work offline and/or involve bulky equipment hardly deployable in industrial settings. This paper presents a novel method for online assessment of cognitive load in manufacturing, primarily assembly, by detecting patterns in human motion directly from the input images of a stereo camera. Head pose estimation and skeleton tracking are exploited to investigate the workers' attention and assess hyperactivity and unforeseen movements. Pilot experiments suggest that our factor assessment tool provides significant insights into workers' mental workload, even confirmed by correlations with physiological and performance measurements. According to data gathered in this study, a vision-based cognitive load assessment has the potential to be integrated into the development of mechatronic systems for improving cognitive ergonomics in manufacturing.
翻译:工业4.0的持续趋势使普通工作场所发生了革命性的变化,深刻改变了人类在生产链中的作用。工业环境下的人类工程学研究主要侧重于减少操作者的身体疲劳和不适,以改善吞吐量和避免安全危险。然而,随着生产复杂性的提高,认知资源需求和心理工作量可能损害操作者的工作表现和商店楼层工作场所的效率。在离线的认知科学工作中采用最先进的方法,并(或)涉及难以在工业环境中部署的散装设备。本文介绍了一种新型的方法,通过直接从立体照相机的输入图像中探测人类运动模式,对制造业的认知负荷进行在线评估,主要是组装。头部进行估计和骨架跟踪,用来调查工人的注意力,评估过度活跃性和意外移动。试点实验表明,我们的要素评估工具对工人的心理工作量提供了重要的洞察力,即使与生理和绩效测量有关。根据这项研究收集的数据,基于视觉的认知负荷评估也有可能纳入用于改进制造业认知元力学系统的开发中。