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In this study, a hybrid predictive model is used to predict 3D asymmetric lifting motion and assess potential musculoskeletal lower back injuries for asymmetric lifting tasks. The hybrid model has two modules: a skeletal module and an OpenSim musculoskeletal module. The skeletal module consists of a dynamic joint strength based 40 degrees of freedom spatial skeletal model. The skeletal module can predict the lifting motion, ground reaction forces (GRFs), and center of pressure (COP) trajectory using an inverse dynamics based optimization method. The equations of motion are built by recursive Lagrangian dynamics. The musculoskeletal module consists of a 324-muscle-actuated full-body lumbar spine model. Based on the generated kinematics, GRFs and COP data from the skeletal module, the musculoskeletal module estimates muscle activations using static optimization and joint reaction forces through the joint reaction analysis tool. Muscle activation results between simulated and experimental EMG are compared to validate the model. Finally, potential lower back injuries are evaluated for a specific-weight asymmetric lifting task. The shear and compression spine loads are compared to NIOSH recommended limits. At the beginning of the dynamic lifting process, the simulated compressive spine load beyond the NIOSH action limit but less than the permissible limit. This is due to the fatigue factors considered in NIOSH lifting equation.

 

Abstract Symmetric lifting is a common manual material handling strategy in daily life and is the main cause of low back pain. In the literature, symmetric lifting is mainly simulated by using two-dimensional (2D) models because of their simplicity and low computational cost. In practice, however, symmetric lifting can generate asymmetric kinetics especially when the lifting weight is heavy and symmetric lifting based on 2D models misses this important asymmetric kinetics information. Therefore, three-dimensional (3D) models are necessary for symmetric lifting simulation to capture asymmetric kinetics. The purpose of this single-subject case study is to compare the optimization formulations and simulation results for symmetric lifting by using 2D and 3D human models and to identify their pros and cons. In this case study, a 10-degrees-of-freedom (DOFs) 2D skeletal model and a 40-DOFs 3D skeletal model are employed to predict the symmetric maximum weight lifting motion, respectively. The lifting problem is formulated as a multi-objective optimization (MOO) problem to minimize the dynamic effort and maximize the box weight. An inverse dynamic optimization approach is used to determine the optimal lifting motion and the maximum lifting weight considering dynamic joint strength. Lab experiments are carried out to validate the predicted motions. The predicted lifting motion, ground reaction forces (GRFs), and maximum box weight from the 2D and 3D human models for Subject #8 are compared with the experimental data. Recommendations are given. 

In this study, a 3D asymmetric lifting motion is predicted by using a hybrid predictive model to prevent potential musculoskeletal lower back injuries for asymmetric lifting tasks. The hybrid model has two modules: a skeletal module and an OpenSim musculoskeletal module. The skeletal module consists of a dynamic joint strength based 40 degrees of freedom spatial skeletal model. The skeletal module can predict the lifting motion, ground reaction forces (GRFs), and center of pressure (COP) trajectory using an inverse dynamics-based motion optimization method. The musculoskeletal module consists of a 324-muscle-actuated full-body lumbar spine model. Based on the predicted kinematics, GRFs and COP data from the skeletal module, the musculoskeletal module estimates muscle activations using static optimization and joint reaction forces through the joint reaction analysis tool in OpenSim. The predicted asymmetric motion and GRFs are validated with experimental data. Muscle activation results between the simulated and experimental EMG are also compared to validate the model. Finally, the shear and compression spine loads are compared to NIOSH recommended limits. The differences between asymmetric and symmetric liftings are also compared.

 

In this study, the three-dimensional (3D) asymmetric maximum weight lifting is predicted using an inverse-dynamics-based optimization method considering dynamic joint torque limits. The dynamic joint torque limits are functions of joint angles and angular velocities, and imposed on the hip, knee, ankle, wrist, elbow, shoulder, and lumbar spine joints. The 3D model has 40 degrees of freedom (DOFs) including 34 physical revolute joints and 6 global joints. A multi-objective optimization (MOO) problem is solved by simultaneously maximizing box weight and minimizing the sum of joint torque squares. A total of 12 male subjects were recruited to conduct maximum weight box lifting using squat-lifting strategy. Finally, the predicted lifting motion, ground reaction forces, and maximum lifting weight are validated with the experimental data. The prediction results agree well with the experimental data and the model’s predictive capability is demonstrated. This is the first study that uses MOO to predict maximum lifting weight and 3D asymmetric lifting motion while considering dynamic joint torque limits. The proposed method has the potential to prevent individuals’ risk of injury for lifting. 

Lifting heavy weight is one of the main reasons for manual material handling related injuries which can be mitigated by determining the limiting lifting weight of a person. In this study, a 40 degrees of freedom (DOFs) spatial skeletal model was employed to predict the symmetric maximum weight lifting motion. The lifting problem was formulated as a multi-objective optimization (MOO) problem to minimize the dynamic effort and maximize the box weight. An inverse-dynamics-based optimization approach was used to determine the optimal lifting motion and the maximum lifting weight considering dynamic joint strength. The predicted lifting motion, ground reaction forces (GRFs), and maximum box weight were shown to match well with the experimental results. It was found that for the three-dimensional (3D) symmetric lifting the left and right GRFs were not same.

 

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.