REPETITIVE MOTION AND POSTURAL ANALYSIS OF MACHINE OPERATORS IN MECHANIZED WOOD HARVESTING OPERATIONS

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Anderson de Costa Paini Eduardo da Silva Lopes Amaury Paulo de Souza Carla Krulikowski Rodrigues Felipe Martins de Oliveira

Abstract

The  objective  of  this  study  was  to  evaluate  operators’  posture  and  repetitive  motions  in  the  mechanized  wood  harvesting  operations,  aiming  comfort,  safety,  and  health  of  forest operators. The study was carried out in the clearcutting of pine stands located in Paraná  State,  Brazil.  Data  were  obtained  in  tree  cutting  operations  with  feller  buncher and wood  processing  with  harvesters,  in  which  three  operators  in  each  machine  were  filmed during their workday. The typical postures were evaluated by Rapid Whole-Body Assessment (REBA) and Rapid Upper-Limb Assessment (RULA) methods, while repetitive motions were evaluated by Latko, Silverstein and Strain Index (SI) methods. The results showed the feller buncher operators remained long period seated in static position, with fists turning outside the neutral line and without pauses for recovery, although REBA and RULA methods had identified low postural risk. In wood processing operation, the spinal column and neck were the most affected body parts, presenting medium postural risk  and  the  need  for  investigations  and  quickly  changes  by  REBA  and  RULA  methods, respectively. Besides that, wood harvesting operations with feller buncher and harvester were classified as high repeatability, showing more than 30 thousand repetitive motions in a workday, indicating high risk of Repetitive Strain Injuries (RSIs) and Musculoskeletal Disorders (MSDs) in the operators. Therefore, it is concluded the ergonomic measures are necessary to improve operators’ comfort and health.

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How to Cite
PAINI, Anderson de Costa et al. REPETITIVE MOTION AND POSTURAL ANALYSIS OF MACHINE OPERATORS IN MECHANIZED WOOD HARVESTING OPERATIONS. CERNE, [S.l.], v. 25, n. 2, p. 214-220, july 2019. ISSN 2317-6342. Available at: <http://cerne.ufla.br/site/index.php/CERNE/article/view/2017>. Date accessed: 27 jan. 2020.
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