Center of pressure excursion capability in performance of seated lateral-reaching tasks.

BACKGROUND: Seated center of pressure excursion capability can be used for patient evaluation in a clinical setting and in universal design. A quantification of excursion capability across age and anthropometry has not been previously reported, although some research suggests that the ischial tuberosities are the support structure limiting the excursion. METHODS: Thirty-eight neurologically healthy adults ranging in age from 21 to 74 years and including 12 obese persons performed a series of 6 lateral-reaching tasks. Participants sat on a platform such that their feet did not touch the ground, leaving their legs free to provide counterbalancing support. Data recorded from a force plate under the platform allowed calculation of the center of pressure throughout the trial and the maximum excursion for each condition was recorded. FINDINGS: The average excursion capability for the healthy, experimental population was 148 mm or 37% of seated hip breadth. Taller participants had larger maximum excursions, on average, than shorter participants, and older participants had smaller excursions than younger participants. INTERPRETATION: The greater trochanter of the femur-rather than the ischial tuberosities-appears to be the primary support structure limiting center of pressure excursion in lateral, balance-limited reaches without contralateral support. These measures and concepts can be used for design, accommodation, and clinically for patient assessment.

Evaluation of muscle force prediction models of the lumbar trunk using surface electromyography.

Optimization-based models for prediction of muscle forces in the lumbar region of the torso are used to estimate the forces acting on spinal motion segments, especially for asymmetric tasks. The objectives of this study were to determine (a) which of four torso model formulations best predicted the electromyographic data, (b) the difference in muscular contribution to spinal compression force for the four models, and (c) the effect of using the lowest possible muscle stress bound in the model formulation. An approach for the investigation of competing optimization model formulations was developed and was illustrated with electromyographic data from static asymmetric loading conditions. This method is based on (a) the choice of experimental conditions in which models predict decidedly different muscle forces, and (b) the ability to ensure that the experimental conditions are such that the minimum number of assumptions about the force-electromyogram relationship must be made in order to choose between competing model predictions. Of the four models analyzed, only the formulation with an objective function that was the sum of cubed muscle stresses predicted the electromyographic data acceptably. The muscular contribution to spinal compression force predicted by these models differed by as much as 160% for some experimental conditions. The use of the lowest possible muscle stress bound does not appear to predict muscle forces that are in agreement with electromyographic data.

Validation of a biodynamic model of pushing and pulling.

Pushing and pulling during manual material handling can increase the compressive forces on the lumbar disc region while creating high shear forces at the shoe-floor interface. A sagittal plane dynamic model derived from previous biomechanical models was developed to predict L5/S1 compressive force and required coefficients of friction during dynamic cart pushing and pulling. Before these predictions could be interpreted, however, it was necessary to validate model predictions against independently measured values of comparable quantities. This experiment used subjects of disparate stature and body mass, while task factors such as cart resistance and walking speed were varied. Predicted ground reaction forces were compared with those measured by a force platform, with correlations up to 0.67. Predicted erector spinae and rectus abdominus muscle forces were compared with muscle forces derived from RMS-EMGs of the respective muscle groups, using a static force build-up regression relationship to transform the dynamic RMS-EMGs to trunk muscle forces. Although correlations were low, this was attributed in part to the use of surface EMG on subjects of widely varied body mass. The biodynamic model holds promise as a tool for analysis of actual industrial pushing and pulling tasks, when carefully applied.

Lumbar muscle force estimation using a subject-invariant 5-parameter EMG-based model.

The use of electromyographic measures, in concert with modeled or empirical representations of muscle physiology, is a common approach for estimation of muscle force. Existing models of the lumbar musculature have allowed model parameters to vary for an individual subject. While this approach improves apparent predictive ability, it loses some degree of construct validity since parameter variability may not be physiologically justifiable. An EMG-based five-parameter model, adapted and generalized from earlier reports, is presented here. Inherent in the model is the requirement of subject-invariant modeling parameters. As a practical analysis tool was desired, the model relies on relatively few calibration constants whose determination is described. Empirical evaluation was undertaken using a database of 398 experimental trials involving lifting and transferring objects of moderate mass. Model performance, evaluated by comparison of measured and predicted lumbar moments, was comparable to earlier models, with r2 mean (S.D.) values of 0.76(0.15) for sagittal plane moments, and rms mean (S.D.) errors of 14.1(7.4), 9.7(5.3), and 8.6(3.6) Nm in the sagittal, frontal, and horizontal planes, respectively. These empirical results and the argument of physiological veracity support the use of a subject-invariant model.

The effect of strict muscle stress limits on abdominal muscle force predictions for combined torsion and extension loadings.

The objective of this study was to determine to what extent the central nervous system activates torso muscles so as to equalize the largest muscle stresses. Two optimization models that treat large muscle stresses differently were formulated. One model minimized spinal compression force subject to the lowest possible muscle stress limit, and the other model minimized the sum of cubed muscle stresses. Experimental conditions were determined for which the two models made different muscle force predictions. Specifically, the models predicted different rectus abdominis activity levels for tasks involving torsion and extension moment loadings. Surface electromyography was used to evaluate the model predictions. Applied loads were chosen to assure that the rectus abdominis EMG exceeded 30% MVC. Analysis of variance indicated that rectus abdominis activity was not affected by torsion loading at the p < 0.05 level of significance in a statistical design having 90% power, which was consistent with the predictions of the model that minimized the sum of cubed muscle stresses. Thus, it was concluded that equalization of the largest muscles stress was not the paramount objective of the central nervous system in the tasks studied.

Support for a linear length-tension relation of the torso extensor muscles: an investigation of the length and velocity EMG-force relationships.

This study investigated the hypothesis that the length-tension relation of the torso erectors would be linear, mirroring the observed linear increase in extension strength capability toward full flexion. The effect of torso extension velocity on the tension capability of these muscles was also investigated for common motion speeds. A myoelectric-based approach was used wherein a dynamic biomechanical model incorporating active and passive tissue characteristics provided muscle kinematic estimates during controlled sagittal plane extension motions. A double linear optimization formulation from the literature provided muscle tension estimates. The data of five male subjects supported the hypothesis of a linear length-tension relation toward full flexion for both the erector spinae and latissimus muscles. Velocity trends agreed with the predicted by Hill's exponential relation, although linear trends were found to fit the data almost as well. The results have implications for muscle tension estimation in biomechanical torso modeling, and suggest a possible low back pain injury mechanism through tissue strain while lifting in fully flexed postures.

Optimization-based differential kinematic modeling exhibits a velocity-control strategy for dynamic posture determination in seated reaching movements.

We proposed a velocity control strategy for dynamic posture determination that underlay an optimization-based differential inverse kinematics (ODIK) approach for modeling three-dimensional (3-D) seated reaching movements. In this modeling approach, a four-segment seven-DOF linkage is employed to represent the torso and right arm. Kinematic redundancy is resolved efficiently in the velocity domain via a weighted pseudoinverse. Weights assigned to individual DOF describe their relative movement contribution in response to an instantaneous postural change. Different schemes of posing constraints on the weighting parameters, by which various motion apportionment strategies are modeled, can be hypothesized and evaluated against empirical measurements. A numerical optimization procedure based on simulated annealing estimate the weighting parameter values such that the predicted movement best fits the measurement. We applied this approach to modeling 72 seated reaching movements of three distinctive types performed by six subjects. Results indicated that most of the movements were accurately modeled (time-averaged RMSE < 5 degrees) with a simple time-invariant four-weight scheme which represents a time-constant, inter-joint motion apportionment strategy. Modeling error could be further reduced by using less constrained schemes, but notably only for the ones that were relatively poorly modeled with a time-invariant four-weight scheme. The fact that the current modeling approach was able to closely reproduce measured movements and do so in a computationally advantageous way lends support to the proposed velocity control strategy.

Distributed moment histogram: a neurophysiology based method of agonist and antagonist trunk muscle activity prediction.

A neurocortical-based technique of muscle recruitment is presented to solve the muscle indeterminacy problem for lumbar torso modeling. Cortical recordings from behaving primates have established motor cortex cells that respond to a wide range of task directions, but are tuned to a preferred direction. A characteristic activity pattern of these neurons seems to be associated with effort direction. It was hypothesized that a model which recruits muscles based on a similar distribution would predict antagonistic muscle activity with greater realism than a widely referenced optimization formulation. The predictions of the Distributed Moment Histogram (DMH) method were evaluated under common speed (< 30 degrees s-1) sagittal plane lifting conditions using five subjects. The predicted forces showed high correspondence with agonist and antagonist myoelectric patterns. The mean coefficient of determination for the erector spinae was r2 = 0.91, and 0.41 for the latissimus. For the antagonistic muscles, the rectus abdominus was found to be electrically silent (< 3% MVC) and no activity was predicted by the method. The external oblique muscle was observed to be minimally active (< 16% MVC), and the DMH method predicted its mostly constant activity with a mean standard error of 1.6% MVC. The realistic antagonistic predictions supported the hypothesis and justify this cortical based technique as an alternative for muscle tension estimation in biomechanical torso modeling. A primary advantage of this method is its computational simplicity and direct physiologic analog.

Rectifying postures reconstructed from joint angles to meet constraints.

Postures are often described and modeled using angles between body segments rather than joint coordinates. Models can be used to predict these angles as a function of anthropometry and postural requirements. Postural representation, however, requires the joint coordinates. The use of conventional forward kinematics to derive joint coordinates from predicted angles may violate task constraints, such as the placement of a hand on a target or a foot on a pedal. Errors arise because the anthropometry or other motion characteristics of a subject, for which the prediction is to be made, may differ from the data from which the prediction model was derived. We describe how to rectify model-predicted postures to exactly satisfy such task constraints. We require that the model used for predicting the angles also produce estimates of the variation in these predictions. We show how to alter the initial angle predictions, with the amount of perturbation at each angle dependent on the accuracy of its estimation, so as to exactly satisfy the joint coordinate constraints. Finally, we show in an empirical example that this correction usually produces better overall predictions of posture than those obtained initially.

Back lift versus leg lift: an index and visualization of dynamic lifting strategies.

The description of a lifting strategy is typically provided in qualitative terms. A quantitative static descriptor or index differentiates the starting postures but not the primary moving segments. This technical note proposes an index that quantitatively characterizes different dynamic postural strategies employed during sagittal plane lifting. Dynamic lifting strategies are modeled in the velocity domain as different schemes of partitioning postural changes between the torso and leg segments. The index consists of two parameters, assigned to two leg segments, quantifying their contributions relative to the torso. Given a measured lifting movement, its index parameters values, ranging from 0.1 to 10, are estimated through an enumeration search process with the objective of minimizing the fitting error. The use of this index is illustrated by applying it to 24 lifting movements performed by six subjects assuming either a back-lift or a leg-lift strategy. Results indicate that a lifting strategy, in terms of whether the leg or the back is generally the prime mover, can be differentiated and visualized using this simple two-parameter index. In addition, indistinct intermediate strategies are also discerned, as the involvement of each segment in a lifting movement is quantified. The index is however limited in that it does not accommodate arm motion contributions to a lift nor possible time-dependent strategic changes during a lift. Potential future applications include time-efficient movement prediction and simulation for computerized biomechanical or ergonomic analysis.

Isometric and isokinetic back and arm lifting strengths: device and measurement.

This study was conducted to measure isometric (static) and isokinetic (dynamic) back and arm lifting strengths at 20, 60 and 100 cm s-1 of young adults. Ten male and ten female volunteers without a history of back pain participated. The isokinetic lifting task was achieved by controlled motorized dynamic strength tester (DST). A regression analysis and analysis of variance was carried out on the strength data. The peak static strength values were significantly greater from the peak dynamic strength values. The peak dynamic strength was inversely related to the speed of motion. There were significant differences between the dynamic strengths at different stages of lift.

Evaluating the effect of co-contraction in optimization models.

The effect of co-contraction of antagonist muscles on spinal compression force is estimated using Karush-Kuhn-Tucker (K-K-T) multipliers. Co-contraction is modelled as an incremental increase in the lower bounds on the allowable muscle forces in an optimization model formation. The K-K-T multipliers associated with each lower bound provide an estimate of the partial derivate of the optimal objective function value with respect to a change in the lower bound. A model whose objective function is spinal compression force is analyzed to estimate the effect of co-contraction on spinal compression force. While the effect depends on the specific muscle and task under consideration, the marginal effect of co-contraction on spinal compression force can be as high as 5.52 N additional spinal compression force for every additional N of muscle force. Paradoxically, the co-contraction may slightly decrease predicted spinal compression in special circumstances.

A back-propagation neural network model of lumbar muscle recruitment during moderate static exertions.

A model employing artificial neural networks (ANNs) is developed for the prediction of lumbar muscle activity in response to steady-state static external moment loads. The model is constructed using standard feedforward networks and trained with available data using the standard back-propagation algorithm. Training with a limited set of exemplars allowed accurate prediction of muscle activity for novel moment loads (generalization). Sensitivity analyses during training and testing phases showed that the choice of specific network parameters was not critical except at extreme values of those parameters. Model predictions were better correlated with experimental data than predictions made using two optimization-based methods (average r2 = 0.83 using ANNs and 0.65 using optimization). The results suggest that lumbar muscle response varies smoothly and consistently with respect to the magnitude and orientation of external moments, and they also imply an upper limit on the accuracy of muscle activity prediction using only moment loads as input. ANNs present a useful alternative to EMG- and optimization-based approaches by being both 'reality-based' and predictive.

Muscle lines-of-action affect predicted forces in optimization-based spine muscle modeling.

This study describes the effects of varied torso muscle geometries commonly assumed in optimization-based muscle force prediction models. Specifically, the sensitivity of predicted muscle and spinal forces to assumed muscle lines-of-action (LOA) is systematically examined. The practical significance of varied muscle LOAs is addressed by determining the relative precision needed for individual muscle LOAs and assessing which muscles are more critical to accurate prediction of spinal forces. To perform this analysis a nonlinear optimization model was used to generate muscle force predictions during combined frontal and sagittal plane moment loadings with an assumed erect posture. The LOAs of the erector spinae, rectus abdominus, internal and external oblique, and latissimus dorsi were systematically varied in the frontal and sagittal planes over an anatomically feasible range. The results indicated that moderate changes in the assumed LOA could substantially alter the magnitudes of predicted muscle and spinal forces. The estimated activity level of a muscle, as well as the predicted active/silent state could be affected by the LOA of that muscle and others. The patterns of predicted muscle activity, with respect to load orientation, underwent only minor alterations with changing LOA. The relative activation of several muscles, however, was dependent on LOA, and frequently led to variations in predicted spinal compression (> 100 N change) and shear forces (> 50 N change). This dependence of estimated spinal forces on assumed muscle geometry was most pronounced for the obliques and minimal for the more vertically oriented muscles and when loads were sagittally symmetric. This study suggests that muscle LOAs are critical inputs when interpreting absolute muscle and spinal force values predicted by models of physical exertions.

A neural network model for simulation of torso muscle coordination.

An artificial neural network (ANN) was created to simulate lumbar muscle response to static moment loads. The network model was based on an abstract representation of a motor control system in which muscle activity is driven primarily to maintain moment equilibrium. The network model parameters were obtained by an iterative method (trained), using a modification of the standard backpropagation algorithm and moment equilibrium constraints. In contrast to previous ANN models of muscle activity, patterns of muscle activity are not target (training) values, but rather emerge as a result of moment equilibrium constraints. Assumptions regarding the moment generating capacity muscles and competitive interactions between muscles were employed and enabled the prediction of realistic patterns of muscle activity upon comparison with experimental electromyographic (EMG) data sets (r2: 0.4-0.9). The success of the simulation model suggests that a motor recruitment plan can be mimicked with relatively simple systems and that 'competition' between responsive units (muscles) may be intrinsic to the learning process. Prediction of alternative recruitment patterns and differing magnitudes of co-contractile activity were achieved by varying competition parameters within and between units.

Biomechanical model calculation of muscle contraction forces: a double linear programming method.

This paper presents a novel scheme for the use of linear programming to calculate muscle contraction forces in models describing musculoskeletal system biomechanics. Models of this kind are frequently found in the biomechanics literature. In most cases they involve muscle contraction force calculations that are statically indeterminate, and hence use optimization techniques to make those calculations. We present a linear programming optimization technique that solves a two-objective problem with two sequential linear programs. We use the technique here to minimize muscle intensity and joint compression force, since those are commonly used objectives. The two linear program model has the advantages of low computation cost, ready implementation on a micro-computer, and stable solutions. We show how to solve the model analytically in simple cases. We also discuss the use of the dual problem of linear programming to gain understanding of the solution it provides.

A dynamic biomechanical evaluation of lifting maximum acceptable loads.

A biomechanical evaluation of the job-related stresses imposed upon a worker is a potential means of reducing the high incidence rates of manual material handling injuries in industry. A biomechanical model consisting of seven rigid links joined at six articulations has been developed for this purpose. Using data from cinematographic analysis of lifting motions the model calculates: (1) body position from articulation angles, (2) angular velocities and accelerations, (3) inertial moments and forces, and (4) reactive moments and forces at each articulation, including the L5/S1 joint. Results indicated effects of the common task variables. Larger load and box sizes increased the rise times and peak values of both vertical ground reaction forces and predicted L5/S1 compressive forces. However, boxes with handles resulted in higher L5/S1 compressive forces than for boxes without handles. Also, in lifting the larger boxes the subjects did not sufficiently compensate with reduced box weights in order to maintain uniform L5/S1 compressive forces. Smoothed and rectified EMG of erector spinae muscles correlated significantly with L5/S1 compressive forces, while predicted and measured vertical ground reaction forces also correlated significantly, indicating the validity of the model as a tool for predicting job physical stresses.

A biomechanical model of the lumbosacral joint during lifting activities.

A biomechanical model of the lumbosacral region was constructed for the purpose of systematically studying the combined stresses and strains on the local ligaments, muscles and disc tissue during sagittal plane two-handed lifting. The model was validated in two ways. The first validation was a comparison of experimental study results with model predictions. In general predictions compared very reasonably with observed values of several authors with the exception of strain predictions on the articular ligaments. Second, a sensitivity analysis was performed over a wide range of lifting tasks. The predicted stress/strain values followed anticipated patterns and were of reasonable magnitudes. On the basis of the results of the sensitivity analysis it was concluded that typical lifting tasks can lead to excessive disc compressive forces, muscle moment generation requirements, and possibly lumbodorsal fascia strains. Conversely, annulus rupture of a healthy disc due to overstrain appears very unlikely.

Analysis of cumulative strain in tendons and tendon sheaths.

Twenty-five fresh frozen flexor digitorum profundus tendons stratified by sex were subjected to uniaxial step stress and cyclic loads in twelve intact human cadaver hands. By attaching specially designed clip strain gage transducers on tendons just proximal and distal to an undisrupted carpal tunnel, the interactions of the tendons, tendon sheath and retinacula were measured. The elastic and viscous response of the tendon composites to step stresses were found to fit fractional power functions of stress and time respectively. A significant and quantifiable decrease in strain from the proximal to the distal tendon segment was found to be a function of wrist deviation. The results indicate that an accumulation of strain does occur in tendinous tissues during physiologic loading.

Using principal-components regression to stabilize EMG-muscle force parameter estimates of torso muscles.

Models for estimating muscle force from surface electromyographic (EMG) recordings require parameter estimates with low intertrial variability. The inclusion of multiple muscles in multivariate statistical models can lead to multicollinearity, especially when there are significant correlations between synergist muscles. One result of multicollinearity is that parameter estimates are very sensitive to changes in the independent variables. This study compared the parameter variability of multiple regression and principal-components regression techniques when applied to a six muscle EMG analysis of the lumbar region of the torso. Nine subjects participated. Twenty-three percent of the traditional multiple-regression parameters had incorrect signs, but none of the principal-components regression parameters did. The principal-components regression technique also produced parameter estimates having an order of magnitude smaller parameter variability. It was concluded that principal-components regression is an effective method of mitigating the effect of multicollinearity in torso EMG models.