Predictors of whole body vibration exposure in motorcycle riders
DOI:
https://doi.org/10.17533/udea.redin.13541Keywords:
motorcycle, exposure time, ISO 2631, neural networks, whole body vibrationAbstract
Some occupations like police officers, paramedics, delivery and courier services, require professional motorcycle riders that may ride for many hours each day. In these cases, the employers, the riders themselves, and the occupational health physicians would benefit from the knowledge of the vibration transmitted to riders and the exposure time limits recommended for their activities. This paper presents an experimental design (24 Factorial Design) to determine the main effects caused on whole body vibration exposure limits by the following factors: motorcycle age, engine size, road type and rider weight. The results show that newer motorcycles allow 36.3% more riding time than older motorcycles before the Exposure Action time Value (EAV) is reached. Motorcycles with larger engine size (125 cc) allow 22.5% more riding time than motorcycles with small engine size. It is possible to ride 44% more time using fast roads than slow roads. The vibration measurements were performed according to ISO-2631 and, as a novel contribution, an artificial neural network was derived for estimating the vibration exposure time using the predictors as input variables. Neural Networks presented better correlation than multiple regressions.
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