Grants and Contributions:

Grant or Award spanning more than one fiscal year (2017-2018 to 2018-2019).

The forest industry is a major economic sector in Canada. It is also one of the most dangerous industries forx000D
workers. The use of mechanized mobile forestry harvesting machines has successfully reduced the incidence ofx000D
injuries in forest workers related to manual labor. However, these machines have also created additionalx000D
concerns, including a high machine operation learning curve, increased length of the workday, repetitive strainx000D
injury, cognitive load, physical and mental fatigue, and increased postural loads due to sitting in a confinedx000D
space. Measuring and analyzing these factors is critical for employers to facilitate appropriate work practices,x000D
reduce the risk of injury, develop optimal recruitment criteria for employees, and establish design guidelinesx000D
for mechanized mobile forestry machines. However, ergonomic field studies of this industry are lacking mainlyx000D
due to the difficulties in obtaining comprehensive data while operators are working in the field.x000D
At JDI, harvester operators are trained on simulation equipment prior to working in the field. This provides anx000D
excellent opportunity for researchers to measure biomechanical and physiological data of operators underx000D
standard training procedures during simulated Canadian forest harvesting operations. The purpose of thisx000D
collaborative study is to examine the effects of worker skill level and movement training speed on harvestingx000D
performance using a forestry simulator. The information collected will help to determine those factors thatx000D
separate skill levels of operators, optimal training speeds, and length of training required to bring new operatorsx000D
to an efficient skill level effectively. Improved training procedures and knowledge of the physical and mentalx000D
demands on workers will lead to highly trained and efficient personnel, reduced risk of injury, and optimalx000D
work protocols. Knowledge gained from this work will contribute to our current understanding of motorx000D
learning and biomechanics and will be highly applicable to various other tasks and industries. This researchx000D
collaboration will provide HQP training to an undergraduate and graduate student, and the lab manager in thex000D
fields of engineering and kinesiology.