The investigation of human factors that affect work productivity among Canadian forest workers

森林业是加拿大的一个主要经济部门。对工人来说，这也是最危险的行业之一。机械化移动式林业采伐机的使用成功地减少了林业工人与体力劳动有关的伤害发生率。然而，这些机器也带来了额外的担忧，包括机器操作学习曲线较高，一天的工作时间增加，重复劳损，认知负荷，体力和精神疲劳，以及由于坐在有限的空间中而增加的姿势负荷。衡量和分析这些因素对于雇主促进适当的工作做法、减少受伤风险、为雇员制定最佳招聘标准以及为机械化移动林业机械制定设计准则至关重要。然而，该行业缺乏符合人体工程学的现场研究，主要是因为操作员在现场工作时难以获得全面的数据。在JDI，收割机操作员在现场工作之前接受模拟设备培训。这为研究人员提供了一个极好的机会来测量在模拟加拿大森林采伐操作中不符合标准培训程序的操作员的生物力学和生理数据。这项合作研究的目的是利用林业模拟器考察工人技能水平和运动训练速度对收获性能的影响。收集的信息将有助于确定将操作员的技能水平、最佳培训速度和培训时间区分开来的因素，以有效地将新操作员带到有效的技能水平。改进的培训程序和对工人体力和精神需求的了解将导致训练有素、效率高的人员，降低受伤风险，并优化工作方案。从这项工作中获得的知识将有助于我们目前对汽车学习和生物力学的理解，并将高度适用于各种其他任务和行业。这项研究合作将为本科生和研究生以及工程学和人体运动学领域的实验室经理提供HQP培训。

The forest industry is a major economic sector in Canada. It is also one of the most dangerous industries forworkers. The use of mechanized mobile forestry harvesting machines has successfully reduced the incidence ofinjuries in forest workers related to manual labor. However, these machines have also created additionalconcerns, including a high machine operation learning curve, increased length of the workday, repetitive straininjury, cognitive load, physical and mental fatigue, and increased postural loads due to sitting in a confinedspace. Measuring and analyzing these factors is critical for employers to facilitate appropriate work practices,reduce the risk of injury, develop optimal recruitment criteria for employees, and establish design guidelinesfor mechanized mobile forestry machines. However, ergonomic field studies of this industry are lacking mainlydue to the difficulties in obtaining comprehensive data while operators are working in the field.At JDI, harvester operators are trained on simulation equipment prior to working in the field. This provides anexcellent opportunity for researchers to measure biomechanical and physiological data of operators understandard training procedures during simulated Canadian forest harvesting operations. The purpose of thiscollaborative study is to examine the effects of worker skill level and movement training speed on harvestingperformance using a forestry simulator. The information collected will help to determine those factors thatseparate skill levels of operators, optimal training speeds, and length of training required to bring new operatorsto an efficient skill level effectively. Improved training procedures and knowledge of the physical and mentaldemands on workers will lead to highly trained and efficient personnel, reduced risk of injury, and optimalwork protocols. Knowledge gained from this work will contribute to our current understanding of motorlearning and biomechanics and will be highly applicable to various other tasks and industries. This researchcollaboration will provide HQP training to an undergraduate and graduate student, and the lab manager in thefields of engineering and kinesiology.