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.

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 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.

Three-dimensional asymmetric maximum weight lifting prediction considering dynamic joint strength.

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.

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.

Computational Methods for Skeletal Muscle Strain Injury: A Review.

In this article, we review skeletal muscle strain injury with computational methods for strain injury analysis, prevention, and recovery. We first review the theory of muscle strain injury at both the microscopic and macroscopic levels. Next, we discuss simulation models, including kinematics, dynamics, and finite-element method. Finally, we introduce predictive approaches for muscle strain injury prevention. Topics including recovery, rehabilitation, muscle-tendon remodeling, and experimental methods are described. We also suggest areas for future research.

An Inverse Dynamics Optimization Formulation With Recursive B-Spline Derivatives and Partition of Unity Contacts: Demonstration Using Two-Dimensional Musculoskeletal Arm and Gait.

In this study, an inverse dynamics optimization formulation and solution procedure is developed for musculoskeletal simulations. The proposed method has three main features: high order recursive B-spline interpolation, partition of unity, and inverse dynamics formulation. First, joint angle and muscle force profiles are represented by recursive B-splines. The formula for high order recursive B-spline derivatives is derived for state variables calculation. Second, partition of unity is used to handle the multicontact indeterminacy between human and environment during the motion. The global forces and moments are distributed to each contacting point through the corresponding partition ratio. Third, joint torques are inversely calculated from equations of motion (EOM) based on state variables and contacts to avoid numerical integration of EOM. Therefore, the design variables for the optimization problem are joint angle control points, muscle force control points, knot vector, and partition ratios for contacting points. The sum of muscle stress/activity squared is minimized as the cost function. The constraints are imposed for human physical constraints and task-based constraints. The proposed formulation is demonstrated by simulating a trajectory planning problem of a planar musculoskeletal arm with six muscles. In addition, the gait motion of a two-dimensional musculoskeletal model with sixteen muscles is also optimized by using the approach developed in this paper. The gait optimal solution is obtained in about 1 min central processing unit (CPU) time. The predicted kinematics, kinetics, and muscle forces have general trends that are similar to those reported in the literature.

Human carrying simulation with symmetric and asymmetric loads using optimization.

Human carrying is simulated in this work by using a skeletal digital human model with 55 degrees of freedom. An optimization-based approach is used to predict the carrying motion with symmetric and asymmetric loads. In this process, the model predicts joint dynamics using optimization schemes and task-based physical constraints. The results indicate that the model can predict different carrying strategies during symmetric and asymmetric load-carrying tasks. The model can also indicate the risk factors for extreme loading situations. With such robust prediction capability, the model could be used for biomedical and ergonomic studies.

Optimization-based prediction of asymmetric human gait.

An optimization-based formulation and solution method are presented to predict asymmetric human gait for a large-scale skeletal model. Predictive dynamics approach is used in which both the joint angles and joint torques are treated as unknowns in the equations of motion. For the optimization formulation, the joint angle profiles are treated as the primary unknowns, and velocities and accelerations are calculated using them. In numerical implementation, the joint angle profiles are discretized using the B-spline interpolation. An algorithm is presented to inversely calculate the joint torques and the ground reaction forces. The sum of the joint-torques squared, called the dynamic effort, is minimized as the human performance measure. Constraints are imposed on the joint strengths (torques) and joint ranges of motion along with other physical constraints. The formulation is validated by simulating a symmetric gait and comparing the results with the experimental data. Then asymmetric gait motion is simulated, where the left and right step lengths are different. The kinematics and kinetics results from the simulation are presented and discussed. Predicted ground reaction forces are explained by using the inverted pendulum model. Predicted kinematics and kinetics have trends that are similar to those reported in the literature. Potential practical applications of the formulation and the solution approach are discussed.

Determining the three-dimensional relation between the skeletal elements of the human shoulder complex.

In this paper, we present an inverse kinematics method to determining human shoulder joint motion coupling relationship based on experimental data in the literature. This work focuses on transferring Euler-angle-based coupling equations into a relationship based on the Denavit-Hartenberg (DH) method. We use analytical inverse kinematics to achieve the transferring. For a specific posture, we can choose points on clavicle, scapula, and humerus and represent the end-effector positions based on Euler angles or DH method. For both Euler and DH systems, the end-effectors have the same Cartesian positions. Solving these equations related to end-effector positions yields DH joint angles for that posture. The new joint motion coupling relationship is obtained by polynomial and cosine fitting of the DH joint angles for all different postures.