Minimizing Low Back Cumulative Loading during Design of Manual Material Handling Tasks: An Optimization Approach

Background: Excessive exposure to low-back cumulative loading (LBCL) has been implicated as a risk factor for developing pain or injury during manual material handling (MMH) tasks. However, addressing LBCL during conceptual work design is challenging because of a lack of an established and widely accepted LBCL threshold limit value. We therefore formulate the design challenge using an optimization framework aided by digital human modeling (DHM). Methods: We constructed a hypothetical MMH task requiring lifting, carrying, and placement of boxes into 16 storage locations. External loads were composed of four different mass categories handled 250 times, with four different relative handling frequencies. Resulting low back compressive force time series were integrated according to four suggested methods. Subsequently, we defined our objective function and constraints, and obtained a solution using an evolutionary algorithm. Results: The percentage agreement between the four different relative handling frequencies and integration methods ranged between 89.5% and 100%. Kendall's coefficient of concordance values ranged between 0.74 and 1.0, indicating good to perfect agreement among the solutions. Conclusion: There is consensus is that minimizing LBCL exposure is beneficial, particularly during task design phases. Our results show that, irrespective of the theoretical background pertaining to LBCL quantification, the method proposed produces a robust and largely similar solution, at least for the MMH scenarios we simulated. Our proposed approach takes advantage of DHM capabilities to simulate diverse MMH scenarios and provides solution estimates at the conceptual design phase. The proposed method can be expanded using multi-objective optimizations schemes and additional constraints to provide a solution that addresses multiple injury and fatigue pathways.

Automated summary

Minimizing LBCL exposure is beneficial during task design phases, and the proposed method takes advantage of DHM capabilities to simulate diverse MMH scenarios and provide solution estimates at the conceptual design phase. The results show a robust and largely similar solution, at least for the MMH scenarios we simulated.