Biomechanical risk assessment of curbside waste collection round through heart rate and GPS data
Authors: Alessio Silvetti, Lorenzo Fiori, Antonella Tatarelli, Adriano Papale, Alberto Ranavolo, Giorgia Chini, Tiwana Varrecchia, Ari Fiorelli, Francesco Draicchio
Abstract: Many scientific papers report that curbside waste collection is a work with high biomechanical load. Our previous papers highlighted the biomechanical risk in this activity and we identified these main risk factors: bad design of equipment, landscape and the number of households covered. The high variability of this task makes it hard to apply standardized protocols for biomechanical risk assessment. For this new experience, we used pulse rate monitors to assess cardiac effort during a full day of pick-up in two different days for two operators gathering bio-waste and glass. The first worker pick-up the waste in the same area on both days. The second worker pick-up waste on zones with different morphology and different urban density. We also recorded GPS data from the second worker. We analyzed heart rate calculating Relative Cardiac Cost (RCC) and heart rate distribution. The first worker performed the task in the municipal urban area and reported a RCC of 43 and 45%. Both values correspond to a heavy work level on the Chamoux scale. The second worker reported RCC values of 36% when collecting in the municipal urban area (quite-heavy work level) and 23% in the non-urban hilly area (moderate work level). The heart rate of the second worker exceeded 140 bpm 7.7% of the time and ranged from 110 to 130 bpm for 72.6% of the time in the municipal area. In the non-urban area, 140 bpm heart rate was never reached and heart rate values were between 110 and 130 bpm for 16.6% of the time. No differences resulted in the first worker due to the type of waste collected. For the second worker, we found a relevant difference in heart rate distribution, probably not related to the type of waste but rather to the number of households and the morphology of the landscape. GPS data seem to support these findings. In the municipal area, the worker moved 17.6 km at a mean speed of 4.8 km/h. In the non-urban area, the worker moved 40.84 Km at a mean velocity of 7.8 Km/h. The higher speed and more than twice the distance covered highlight that the worker spent much more time driving the truck in the non-urban zone than in the municipal one resulting in a reduced biomechanical workload. In conclusion, combined data from heart rate monitor and GPS allowed us to highlight the different workloads between the two zones, municipal and non-urban. We suggest alternating the workers between them to reduce biomechanical risk.
Keywords: musculoskeletal disorders, MSDs, ergonomic, physiological parameters
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