Manual material handling is common in daily life and is the main cause of lower back pain. Therefore, it is critical to establish a lifting limit for workers. However, it is difficult to obtain each individual's maximum lifting weight through experiments. This study presents a multi-objective optimization (MOO) for two-dimensional maximum weight lifting prediction. Minimizing the dynamic effort (joint torque square) and maximizing the box weight are the two objective functions. Fourteen human subjects were recruited to collect motion and ground reaction force data in the laboratory. Twelve subjects’ data were used to determine cost function weights. The other two subjects’ data were used to validate the best MOO objective function weights through the root mean square errors and Pearson coefficients between the simulated and experimental data. The results show that the proposed MOO method and the best weighting coefficients could improve the accuracy of the simulation.
- Dynamic effort
- dynamic joint strength
- inverse dynamics optimization
- maximum lifting weight
- multi-objective optimization