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.

Sensitivity analysis of sex- and functional muscle group-specific parameters for a three-compartment-controller model of muscle fatigue.

The three-compartment-controller with enhanced recovery (3CC-r) model of fatigue has been validated, in multiple stages and by different methods, for sustained (SIC) and intermittent isometric contractions (IIC). It has also been validated using a common methodology for both contraction types simultaneously to derive sex-specific representative model parameters for each functional muscle group, at the expense of reducing the sample size used to estimate each parameter set. In this study, a sensitivity analysis of the model to both variations in experimental measurements and to variations in the parameter values is carried out to estimate the robustness of the parameter sets. Torque decline prediction error is found to increase only slowly with increasing randomness injected into experimental data, with <1 % increases in error for 8-29 % variation in experimental endurance times. The results demonstrate that the obtained parameters from our previous study are reliable and can be used for fatigue prediction in multiple scenarios without significant loss of accuracy. For all sexes and functional muscle groups examined, the fatigue process dominates recovery in the experimental conditions examined. Finer estimates of the model's recovery parameter will likely require changes to the experiment design in future studies.

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 model's rapid motion prediction with OpenSim's complex muscular dynamics assessment, and it can serve as a new generic tool for motion prediction and injury analysis in ergonomics and biomechanics.

Functional muscle group- and sex-specific parameters for a three-compartment controller muscle fatigue model applied to isometric contractions.

The three-compartment controller with enhanced recovery (3CC-r) model of muscle fatigue has previously been validated separately for both sustained (SIC) and intermittent isometric contractions (IIC) using different objective functions, but its performance has not yet been tested against both contraction types simultaneously using a common objective function. Additionally, prior validation has been performed using common parameters at the joint level, whereas applications to many real-world tasks will require the model to be applied to agonistic and synergistic muscle groups. Lastly, parameters for the model have previously been derived for a mixed-sex cohort not considering the differece in fatigabilities between the sexes. In this work we validate the 3CC-r model using a comprehensive isometric contraction database drawn from 172 publications segregated by functional muscle group (FMG) and sex. We find that prediction errors are reduced by 19% on average when segregating the dataset by FMG alone, and by 34% when segregating by both sex and FMG. However, minimum prediction errors are found to be higher when validated against both SIC and IIC data together using torque decline as the outcome variable than when validated sequentially against hypothesized SIC intensity-endurance time curves with endurance time as the outcome variable and against raw IIC data with torque decline as the outcome variable.

Dynamic-joint-strength-based two-dimensional symmetric maximum weight-lifting simulation: Model development and validation.

This article presents an optimization formulation and experimental validation of a dynamic-joint-strength-based two-dimensional symmetric maximum weight-lifting simulation. Dynamic joint strength (the net moment capacity as a function of joint angle and angular velocity), as presented in the literature, is adopted in the optimization formulation to predict the symmetric maximum lifting weight and corresponding motion. Nineteen participants were recruited to perform a maximum-weight-box-lifting task in the laboratory, and kinetic and kinematic data including motion and ground reaction forces were collected using a motion capture system and force plates, respectively. For each individual, the predicted spine, shoulder, elbow, hip, knee, and ankle joint angles, as well as vertical and horizontal ground reaction force and box weight, were compared with the experimental data. Both root-mean-square error and Pearson's correlation coefficient (r) were used for the validation. The results show that the proposed two-dimensional optimization-based motion prediction formulation is able to accurately predict all joint angles, box weights, and vertical ground reaction forces, but not horizontal ground reaction forces.

Modelling muscle recovery from a fatigued state in isometric contractions for the ankle joint.

Current models of localized muscular fatigue are capable of predicting performance in isometric tasks with reasonable accuracy. However, they do not account for the effect of continuously-varying task intensities on muscular recovery from a fatigued state. In this work, we propose and evaluate three continuous functions for modelling recovery to replace a dichotomous step-function in the three-compartment controller (3CC-r) model of muscle fatigue (Looft et al., 2018) and validate their predictions with previously collected data in the literature for intermittent and sustained isometric tasks of the ankle joint performed at different intensities. When compared to experimental data the accuracy of one of the three proposed models of recovery is found to be nearly the same as that yielded by the original step-function, but this seemingly-identical accuracy may be a limitation of the dataset used. A superelliptical curve relating recovery factor to task intensity is proposed to be the closest replacement for the step function as it depicts both the elevated value of recovery factor for near-rest activities as well as a nearly-constant value for low-to-high-intensity tasks.