Investigation of 3D dynamic and quasistatic models for spinal moments during combined manual material handling tasks

Optimization-based biomechanical lifting models for manual material handling: A comprehensive review

Lifting is a main task for manual material handling (MMH), and it is also associated with lower back pain. There are many studies in the literature on predicting lifting strategies, optimizing lifting motions, and reducing lower back injury risks. This survey focuses on optimization-based biomechanical lifting models for MMH. The models can be classified as two-dimensional and three-dimensional models, as well as skeletal and musculoskeletal models. The optimization formulations for lifting simulations with various cost functions and constraints are reviewed. The corresponding equations of motion and sensitivity analysis are briefly summarized. Different optimization algorithms are utilized to solve the lifting optimization problem, such as sequential quadratic programming, genetic algorithm, and particle swarm optimization. Finally, the applications of the optimization-based lifting models to digital human modeling which refers to modeling and simulation of humans in a virtual environment, back injury prevention, and ergonomic safety design are summarized.

Effects of a Passive Back-Support Exoskeleton on Knee Joint Loading during Simulated Static Sorting and Dynamic Lifting Tasks

Due to the load shifting mechanism of many back-support exoskeletons (BSEs), this study evaluated possible side effects of using a BSE on knee joint loading. Twenty-nine subjects (25.9 (±4.4) years, 179.0 (±6.5) cm; 73.6 (±9.4) kg) performed simulated static sorting and dynamic lifting tasks, including stoop and squat styles and different trunk rotation postures. Ground reaction force, body posture and the force between the chest and the BSE's contact interface were recorded using a force plate, two-dimensional gravimetric position sensors, and a built-in force sensor of the BSE, respectively. Using these parameters and the subject's anthropometry, median and 90th percentile horizontal (HOR₅₀, HOR₉₀) and vertical (VERT₅₀, VERT₉₀) tibiofemoral forces were calculated via a self-developed inverse quasi-static biomechanical model. BSE use had a variable effect on HOR₅₀ dependent on the working task and body posture. Generally, VERT₅₀ increased without significant interaction effects with posture or task. HOR₉₀ and VERT₉₀ were not affected by using the BSE. In conclusion, utilizing the investigated exoskeleton is likely to induce side effects in terms of changed knee joint loading. This may depend on the applied working task and the user's body posture. The role of these changes in the context of a negative contribution to work-related cumulative knee exposures should be addressed by future research.

Effect of using real motion versus predicted motion as input for digital human modeling of back and shoulder loads during manual material handling

Digital human modeling (DHM) technology is considered the state of the art in designing and evaluating workstations. Previous studies examined the differences between DHM's posture and motion prediction relative to human experimental data. Yet, the effect the two different inputs on biomechanical loads was not assessed. Therefore, this study evaluates the differences in L4/L5 compression force and shoulder torques during a work process calculated using DHM with motion prediction (Jack by Siemens) and DHM with experimental data. The work process is a sequential removing, carrying, and depositing task performed by nine females and nine males and recorded using a motion capture system. The analysis shows that using experimental data results in larger back compression force during the removing task (average 15.4%), similar force during the depositing task (average 0.68%), and less force during the carrying task (19.875%). Using experimental data resulted in larger shoulder torque during all tasks (average 24.97%).