Bimanual Motor Skill in Recruitment of Forest Harvest Machine Operators

Abstract

The great complexity of the operation of wood harvesting machines and unpredictable differences of performance between operators must be reflected in the industry recruitment techniques. This work aimed to carry out an evaluation of the bimanual motor skill in candidates for the position of harvester operators using a virtual reality simulator to generate information that can contribute to and improve the selection process. The work was developed at the Forest Operators Training Center (CENFOR), at the State University of the Center–West, in Irati, PR. A sample of 12 individuals was studied and distributed into three levels of performance. The motor ability of the individuals was evaluated through the variables: »run time«, »fall direction«, and »cutting height«, assessed at different points during a 4-hour practice – 0.5; 1.0; 1.5; 2.0; 3.0 and 4.0 hours – practice in a virtual harvester simulator. The data were analyzed by variance and means, as well as compared to a Tukey test at the 5% level of significance. The individuals had a significant difference in the variables »run time« and »cutting height«, and could be accurately used to predict bimanual motor skill/performance. There was a significant gain in the performance of the operators up to 1.5 hours after the beginning of the skill test, and all those who demonstrated greater and lesser ability in the first half hour of the test maintained this behavior until the end of the training period. The virtual reality simulator can be used as a tool to assess bimanual motor skills during the selection of harvester operators.

A Systematic Review of Virtual Reality Features for Skill Training

“…In addition, it induces empathy among users in real-world environments (Mallam et al, 2017). In the reviewed articles, 3D visuals coupled with other VR features have been reported to have positive effects on procedural (Lohre et al, 2020b), motor (Lohre et al, 2020b), problem-solving skills , and teaching skills (Ke et al, 2016) in HMD environments, and basic to advanced motor skills (Casso et al, 2019;Pagnussat et al, 2020;Pahuta et al, 2012;Rölfing et al, 2020;Serrano et al, 2020;Won et al, 2018), procedural skills (Rölfing et al, 2020), technical skills (Casso et al, 2019) and visuospatial skills (Pahuta et al, 2012) in non-HMD environments. In rare contexts, 3D visual features coupled with real-time feedback negatively affect motor skill training (Stefanidis et al, 2007) in non-HMD environments.…”

Formalizing the Professional Selection of Forest Machine Operators

“…Применительно к операторскому труду описаны подходы и методики первичного отбора операторов путем оценки времени умственной активности [8; 9], тестов действия, в том числе в индивидуальной и групповой реализации [10], отдельных параметров, характеризующих уровень сенсомоторной координации [11; 12], моторных навыков [13], психологического состояния [14].…”

Exploring the feasibility of autonomous forestry operations: Results from the first experimental unmanned machine

“…Yet, standard machines come equipped with old‐fashioned open‐loop control systems, where every degree‐of‐freedom (DOF) and function is individually controlled through joysticks and buttons. Therefore, the ability to demonstrate a good working performance depends on the multitasking skills of each individual operator, resulting in large productivity variation (Pagnussat & Hauge, 2020). Apart of this, machine operators need to take thousands of multiple forest management decisions every day, while they control the machine's functions simultaneously.…”