Predicting the influence of hip and lumbar flexibility on lifting motions using optimal control

Quantifying how functional and structural personal factors influence biomechanical exposures in paramedic lifting tasks

It is unknown how structural (sex, stature, body mass) and functional (strength, flexibility) personal factors influence lifting strategy in paramedic work. We explored whether variance in peak low back forces and kinematic coordination patterns could be explained by structural and functional personal factors in paramedic lifting tasks. Seventy-two participants performed backboard and stretcher lifts. Peak low back forces normalised to body mass, as well as kinematic coordination patterns, were calculated as dependent variables. Being female, stronger, shorter, having higher body mass, and/or having greater lower body range of motion (ROM) were all independently associated with lower normalised low back forces across backboard and stretcher lifting. Females and stronger individuals seemed to define a movement objective to consistently minimise compressive forces, while individuals with greater hip ROM consistently minimised anteroposterior shear forces. The efficacy of improving strength and hip ROM to reduce low back forces in paramedic lifting should be investigated.Practitioner summary: Females, stronger individuals, and individuals with greater hip range of motion consistently exhibited lower normalised low back forces in paramedic lifting. Improving strength and hip range of motion via training is a potential proactive ergonomics approach to reduce peak low back forces in paramedic lifting tasks.

Multi-phase optimisation model predicts manual lifting motions with less reliance on experiment-based posture data

Optimisation-based predictive models are widely-used to explore the lifting strategies. Existing models incorporated empirical subject-specific posture constraints to improve the prediction accuracy. However, over-reliance on these constraints limits the application of predictive models. This paper proposed a multi-phase optimisation method (MPOM) for two-dimensional sagittally symmetric semi-squat lifting prediction, which decomposes the complete lifting task into three phases-the initial posture, the final posture, and the dynamic lifting phase. The first two phases are predicted with force- and stability-related strategies, and the last phase is predicted with a smoothing-related objective. Box-lifting motions of different box initial heights were collected for validation. The results show that MPOM has better or similar accuracy than the traditional single-phase optimisation (SPOM) of minimum muscular utilisation ratio, and MPOM reduces the reliance on experimental data. MPOM offers the opportunity to improve accuracy at the expense of efforts to determine appropriate weightings in the posture prediction phases. Practitioner summary: Lifting optimisation models are useful to predict and explore the human motion strategies. Existing models rely on empirical subject-specific posture constraints, which limit their applications. A multi-phase model for lifting motion prediction was constructed. This model could accurately predict 2D lifting motions with less reliance on these constraints.

Hybrid musculoskeletal model-based 3D asymmetric lifting prediction and comparison with symmetric lifting

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.

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.

Hybrid Predictive Model for Lifting by Integrating Skeletal Motion Prediction with an OpenSim Musculoskeletal Model

OBJECTIVE

In this study, a novel hybrid predictive musculoskeletal model is proposed which has both motion prediction and muscular dynamics assessment capabilities.

METHODS

First, a two-dimensional (2D) skeletal model with 10 degrees of freedom is used to predict a symmetric lifting motion, outputting joint angle profiles, ground reaction forces (GRFs), and center of pressure (COP). These intermediate outputs are input to the scaled musculoskeletal model in OpenSim for muscle activation and joint reaction load analysis. Finally, the experimental validation is carried out.

RESULTS

Static Optimization tool is used to estimate the muscle activation data in OpenSim for the predicted lifting motion. Joint reaction forces of the lumbosacral joint (L5-S1) are generated using the OpenSim Joint Reaction analysis tool. The predicted joint angles, muscle activations, and peak joint reaction forces are compared with experimental data and data from literature to validate the hybrid model.

CONCLUSION

The proposed hybrid model combines the skeletal models rapid motion prediction with OpenSims complex muscular dynamics assessment, and it can serve as a new generic tool for motion prediction and injury analysis in ergonomics and biomechanics.