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Development of a seat selection algorithm to match industrial seats with specific forestry vibration exposures

Low back injury is a major health issue among heavy machinery operators, and matching seats to the vibration environment may reduce vibration exposure. We developed neural network (NN) models to characterize the vibration attenuation properties of five commercial industrial seats. Each of the seat NN models strongly predicted vertical seatpan root mean square (r.m.s.) accelerations from the chassis accelerations (r2 > 0.93 for all models). Daily vibration exposure A(8) and the number of working hours to reach the upper limit of the International Standards Organization (ISO) 2631–1 health guidance caution zone (HGCZ) for an 8-hour work duration were calculated for each of the five industrial seats based on chassis vibrations measured for eight specific skidders and three specific subject anthropometrics (BMI values of 18.5, 25 and 30). There were meaningful differences between seats in the amount of time to reach the upper limit of the HGCZ (from 2.43 up to 22.86 hours). As expected, the models predicted that operators with lower BMI had higher A(8) accelerations, and needed lower amounts of time to reach the upper limit of HGCZ. All of the seats exceeded the lower limit of the HGCZ, and only one of the five seats almost completely limited the vibration exposures to below the upper limit of the HGCZ. We have shown that the vibration properties of industrial seats can be successfully modeled using a NN approach and that A(8) values can be interrogated as part of a seat selection algorithm to match individual seat with specific vibration exposures.