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A framework for evaluating muscle activity during repetitive manual material handling in construction manufacturing

机译:用于评估建筑制造中重复性人工物料搬运过程中肌肉活动的框架

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Workers in construction sites are exposed to highly labor-intensive tasks. Ergonomic principles, in addition to engineering considerations, should thus be included in the design of workstations in order to minimize the risk of injury. The objective of this paper is to propose a framework to assess muscle force and muscle fatigue development due to manual lifting tasks using surface electromyography (sEMG) and human body modelling. Muscle forces are calculated using the human body model and compared qualitatively to sEMG muscle activities. The results show that sEMG is capable of visualizing muscle activity. However, sEMG application in identifying muscle fatigue development is limited to bulkier and superficial muscle bundles in low fat areas. The proposed human body model, which is driven by kinematic motion capture data, predicts muscle forces during the entire task maneuver. The predicted muscle forces from the human body model are compared with sEMG data from corresponding muscles as well as data available in the literature. In future research, the developed model will be used to determine optimal task maneuvers that minimize muscle forces with the ultimate goal of preventing muscle injuries in workstations. (C) 2017 Elsevier B.V. All rights reserved.
机译:建筑工地的工人面临劳动密集型任务。因此,除了工程上的考虑外,人体工学原理还应包括在工作站的设计中,以最大程度地减少受伤的风险。本文的目的是提出一个框架,用于评估由于使用表面肌电图(sEMG)和人体建模而进行的手动举重任务而引起的肌肉力量和肌肉疲劳的发展。使用人体模型计算肌肉力量,并与sEMG肌肉活动进行定性比较。结果表明,sEMG能够可视化肌肉活动。但是,sEMG在识别肌肉疲劳发展中的应用仅限于低脂区域中较大的和浅表的肌肉束。所提出的人体模型由运动学运动捕获数据驱动,可以预测整个任务操纵过程中的肌肉力量。将人体模型预测的肌肉力与相应肌肉的sEMG数据以及文献中可用的数据进行比较。在未来的研究中,将使用已开发的模型来确定最佳的任务演习,以最大程度地减少肌肉力量,最终目标是防止工作站中的肌肉受伤。 (C)2017 Elsevier B.V.保留所有权利。

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