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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.

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Few studies have examined high-level motor plans underlying cognitive-motor performance during practice of complex action sequences. These investigations have assessed performance through fairly simple metrics without examining how practice affects the structures of action sequences. By adapting the Levenshtein distance (LD) method to the motor domain, we propose a computational approach to accurately capture performance dynamics during practice of action sequences. Practice performance dynamics were assessed by computing the LD based on the number of insertions, deletions, and substitutions of actions needed to transform any sequence into a reference sequence (having a minimal number of actions to complete the task). Also, combining LD-based performance with mental workload metrics allowed assessment of cognitive-motor efficiency dynamics. This approach was tested on the Tower of Hanoi task. The findings revealed that throughout practice this method could capture: i) action sequence performance improvements as indexed by a reduced LD (decrease of insertions and substitutions), ii) structural modifications of the high-level plans, iii) an attenuation of mental workload, and iv) enhanced cognitive-motor efficiency. This effort complements prior work examining the practice of complex action sequences in healthy adults and has potential for probing cognitive-motor impairment in clinical populations as well as the development/assessment of cognitive robotic controllers.

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