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

林业是加拿大的主要经济部门。这也是对工人最危险的行业之一。机械化移动的林业采伐机械的使用成功地降低了林业工人与体力劳动有关的伤害发生率。然而，这些机器也产生了额外的问题，包括高机器操作学习曲线，工作日长度增加，重复性劳损，认知负荷，身心疲劳以及由于坐在有限空间内而增加的姿势负荷。测量和分析这些因素对于雇主促进适当的工作实践，降低受伤风险，制定最佳的员工招聘标准以及建立机械化移动的林业机械的设计指南至关重要。然而，由于操作员在田间工作时难以获得全面的数据，该行业的人机工程学现场研究还很缺乏。在JDI，收割机操作员在田间工作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，在模拟加拿大森林采伐作业期间，测量标准培训程序下操作员的生物力学和生理数据。本合作研究的目的是使用林业模拟器检查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这些因素，这些因素将操作员的技能水平分开，最佳培训速度和培训时间长度，以使新操作员有效地达到有效的技能水平。改进的培训程序和对工人身体和精神需求的了解将导致训练有素和高效的人员，减少受伤的风险和最佳的工作协议。从这项工作中获得的知识将有助于我们目前对运动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和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.