An automated simulation approach for productive and ergonomic workplace design

与工作有关的肌肉骨骼疾病（WMSDs）是最常见的伤害之一。WMSD与雇主的高成本负担有关，这是由于缺勤、生产力损失、医疗保健和工人赔偿成本增加。作为一家大型钢铁制造和建筑公司，Waiward也面临着与WMSD相关的各种挑战。大量的任务是由工人在怀沃德的六个制造车间在两个十小时轮班每天完成。由于钢铁制造过程的性质，许多这些任务构成了高度的人体工程学风险，并可能导致WMSD。该公司的统计数据估计，WMSD占其工伤总数的40%以上。因此，拟议的研究的目的是提高工人的安全在Waiward通过整合生产力提高技术和人体工程学分析，通过自动化。具体而言，本研究提出了基于运动数据的人机工程学分析和微运动水平模拟，以考虑生产率和安全性的操作规划和监控。本研究的目标将通过以下四个阶段实现：（1）身体需求分析，以确定符合人体工程学的笨拙姿势;（2）自动生物力学模拟，以确定具有过度身体需求的特定身体关节;（3）时间研究和模拟建模，以评估工作效率;以及（4）模拟平台开发，将生产力和安全性纳入操作分析。预期的贡献是：（1）提供洞察安全作为生产系统的紧急属性，（2）探索微运动水平的仿真方法，确保生产和可靠的操作分析，（3）调查使用运动捕捉数据的人体工程学分析的自动化手段，和（4）提出一种新的方法来衡量工作效率的标准化运动时间数据的基础上。这项研究的更广泛影响包括在劳动力是重要资源的各个行业预防伤害和维护人类福祉。它还提供了一种创新的研究方法，有助于理解工作场所中的人类行为。

Work-related Musculoskeletal Disorders (WMSDs) is one of the most common injuries. WMSDs are associated with high cost burdens to employers due to absenteeism, lost productivity, and increased health care, and workers' compensation costs. As a large steel fabrication and construction firm, Waiward also faces various challenges related to WMSDs. A high number of tasks are performed by workers at Waiward's six fabrication shops in two ten-hour shifts per day. Due to the nature of the steel fabrication process, many of these tasks pose a high degree of ergonomic risk and can lead to WMSDs. The company statistics estimate WMSDs to account for more than 40% of their total workplace injuries. The aim of the proposed research is thus to improve worker safety at Waiward by integrating productivity improvement techniques and ergonomic analysis through automation. Specifically, this research proposes motion data-based ergonomic analysis and micro-motion level simulation to take both productivity and safety into account for operation planning and monitoring. The objective of this study will be achieved through the following four phases: (1) physical demand analysis to identify ergonomically awkward postures, (2) automated biomechanical simulation to identify specific body joints with excessive physical demand, (3) time studies and simulation modelling to assess job efficiency, and (4) simulation platform development to integrate productivity and safety into operation analysis. The anticipated contribution is (1) to provide insight into safety as an emergent property of a production system, (2) to explore a micro-motion level simulation approach that ensures productive and reliable operation analysis, (3) to investigate an automated means of ergonomic analysis using motion capture data, and, (4) to propose a novel method to measure job efficiency based on standardized motion time data. The broader impacts of this research include the prevention of injuries and the preservation of human well-being in various industries where labour is an important resource. It also provides an innovative research methodology that will aid understanding of human behavior in workplaces.