Regression models for predicting peak and continuous three-dimensional spinal loads during symmetric and asymmetric lifting 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.

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.

Selecting the appropriate input variables in a regression approach to estimate actively generated muscle moments around L5/S1 for exoskeleton control

Back support exoskeletons are designed to prevent work-related low-back pain by reducing mechanical loading. For actuated exoskeletons, support based on moments actively produced by the trunk muscles appears a viable approach. The moment can be estimated by a biomechanical model. However, one of the main challenges here is the feasibility of recording the required input variables (kinematics, EMG data, ground reaction forces) to run the model. The aim of this study was to evaluate how accurate different selections of input variables can estimate actively generated moments around L5/S1. Different multivariate regression analyses were performed using a dataset consisting of spinal load, body kinematics and trunk muscle activation levels during different lifting conditions with and without an exoskeleton. The accuracy of the resulting models depended on the number and type of input variables and the regression model order. The current study suggests that third-order polynomial regression of EMG signals of one or two bilateral back muscle pairs together with exoskeleton trunk and hip angle suffices to accurately estimate the actively generated muscle moment around L5/S1, and thereby design a proper control system for back support exoskeletons.

Lumbopelvic rhythm during forward and backward sagittal trunk rotations: combined in vivo measurement with inertial tracking device and biomechanical modeling

BACKGROUND

The ratio of total lumbar rotation over pelvic rotation (lumbopelvic rhythm) during trunk sagittal movement is essential to evaluate spinal loads and discriminate between low back pain and asymptomatic population.

METHODS

Angular rotations of the pelvis and lumbar spine as well as their sagittal rhythm during forward flexion and backward extension in upright standing of eight asymptomatic males are measured using an inertial tracking device. The effect of variations in the lumbopelvic ratio during trunk flexion on spinal loads is quantified using a detailed musculoskeletal model.

FINDINGS

The mean of peak voluntary flexion rotations of the thorax, pelvis, and lumbar was 121° (SD 9.9), 53.0° (SD 5.2), and 60.2° (SD 8.6), respectively. The mean lumbopelvic ratios decreased from 2.51 in 0-30° of trunk flexion to 1.34 in 90-120° range during forward bending while it increased from 1.23 in 90-120° range to 2.86 in 0-30° range during backward extension. Variations in the lumbopelvic ratio from 0.5 to 3 (with an interval of 0.25) at any trunk flexion angle generally reduced the L5-S1 compression and shear forces by up to 21 and 45%, respectively. The measured lumbopelvic ratios resulted overall in near-optimal (minimal) L5-S1 compression forces.

INTERPRETATION

A simultaneous rhythm between the lumbar and pelvis movements was found during both forward and backward trunk movements. While the lumbar spine contributed more to the trunk rotation during early and final stages of forward flexion and backward extension, respectively, the pelvis contributed more during final and early stages of forward flexion and backward extension, respectively. Our healthy subjects adapted a lumbopelvic coordination that diminished L5-S1 compression force.

Alternatives to lifting concrete masonry blocks onto rebar: biomechanical and perceptual evaluations

This study examined the use of and barriers to H-block and high lift grouting, two alternatives to lifting concrete masonry blocks onto vertical rebar. Peak and cumulative shoulder motions were evaluated, as well as adoption barriers: H-block cost and stakeholder perceptions. Results indicated that using the alternatives significantly decreased peak shoulder flexion (p < 0.001). A case study indicated that building cost was higher with H-block, but the difference was less than 2% of the total cost. Contractors and specifiers reported important differences in perceptions, work norms, and material use and practices. For example, 48% of specifiers reported that use of high lift grouting was the contractor's choice, while 28% of contractors thought it must be specified. Use of H-block or high-lift grouting should be considered as methods to reduce awkward upper extremity postures. Cost and stakeholders' other perceptions present barriers that are important considerations when developing diffusion strategies for these alternatives.

PRACTITIONER SUMMARY

This study provides information from several perspectives about ergonomic controls for a high risk bricklaying task, which will benefit occupational safety experts, health professionals and ergonomists. It adds to the understanding of shoulder stresses, material cost and stakeholder perceptions that will contribute to developing effective diffusion strategies.

Predictive equations for lumbar spine loads in load-dependent asymmetric one- and two-handed lifting activities

BACKGROUND

Asymmetric lifting activities are associated with low back pain.

METHODS

A finite element biomechanical model is used to estimate spinal loads during one- and two-handed asymmetric static lifting activities. Model input variables are thorax flexion angle, load magnitude as well as load sagittal and lateral positions while response variables are L4-L5 and L5-S1 disc compression and shear forces. A number of levels are considered for each input variable and all their possible combinations are introduced into the model. Robust yet user-friendly predictive equations that relate model responses to its inputs are established.

FINDINGS

Predictive equations with adequate goodness-of-fit (R(2) ranged from ~94% to 99%, P≤0.001) that relate spinal loads to task (input) variables are established. Contour plots are used to identify combinations of task variable levels that yield spine loads beyond the recommended limits. The effect of uncertainties in the measurements of asymmetry-related inputs on spinal loads is studied.

INTERPRETATION

A number of issues regarding the NIOSH asymmetry multiplier are discussed and it is concluded that this multiplier should depend on the trunk posture and be defined in terms of the load vertical and horizontal positions. Due to an imprecise adjustment of the handled load magnitude this multiplier inadequately controls the biomechanical loading of the spine. Ergonomists and bioengineers, faced with the dilemma of using either complex but more accurate models on one hand or less accurate but simple models on the other hand, have hereby easy-to-use predictive equations that quantify spinal loads under various occupational tasks.

Prediction of forearm muscle activity during gripping

Occupational exposure is typically assessed by measuring forces and body postures to infer muscular loading. Better understanding of workplace muscle activity levels would aid in indicating which muscles may be at risk for overexertion and injury. However, electromyography collection in the workplace is often not practical. Therefore, a set of equations was developed and validated using data from two separate days to predict forearm muscle activity (involving six wrist and finger muscles) from grip force and posture of the wrist (flexed, neutral and extended) and forearm (pronated, neutral, supinated). The error in predicting activation levels of each forearm muscle across the range of grip forces, using the first day data (root mean square error; RMSEmodel), ranged from 8.9% maximal voluntary electrical activation (MVE) (flexor carpi radialis) to 11% MVE (extensor digitorum communis). Grip force was the main contributor to predicting muscle activity levels, explaining over 70% of the variance in flexor activation levels and up to 60% in extensor activation levels, respectively. Inclusion of gender as a variable in the model improved estimates of flexor but not extensor activity. While posture itself explained minimal variance in activation without grip force (< 10% MVE), wrist and forearm posture were required (with grip force) to explain over 70% of the variance of all six muscles. The validation process indicated good day-to-day reliability of each equation, with similar error for flexor muscle models but slightly higher error in the extensor models when predicting activity levels for the second day of data (RMSEvalid ranging from 8.9% to 12.7% MVE). Detailed error analysis during validation revealed that inclusion of posture in the model effectively decreased error at grip forces above 25% maximum, but was detrimental at very low grip forces. This study presents a potential new tool to estimate forearm muscle loading in the workplace using grip force and posture, as a surrogate to use of a complex biomechanical model.