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Introduction: We examined generalized versus joint-specific influences on muscle coactivation., Methods: Muscle coactivation was assessed during maximal isometric and isokinetic knee and elbow joint extension moments in 48 healthy subjects (27 men). Local (joint-specific) and generalized (person-specific) contributions were examined using a combination of statistical tests, including regression with generalized estimating equations (GEEs), exploratory factor analysis, and cluster analysis., Results: GEEs produced similar significant coefficients for gender and joint; contraction type and test condition (angle or velocity) were not significant. Factor analysis indicated 2 joint-based factors, and cluster analysis indicated 2 groups of individuals, those with and without elevated coactivation at the knee and elbow. Women exhibited greater coactivation at both joints, but no consistent influences of angle or velocity were observed at either joint., Conclusion: Muscle coactivation is a neuromuscular control response determined by local, joint-specific, and generalized, individual-specific influences., (Copyright © 2013 Wiley Periodicals, Inc.)

Recognizing the importance of both the torque-angle and torque-velocity relations, three-dimensional (3D) human strength capabilities (i.e., peak torque as a function of both joint angle and movement velocity) have been increasingly reported. It is not clear, however, the degree to which these surfaces vary between joints, particularly between joints with similar biomechanical configurations. Thus, our goal was to compare 3D strength surfaces between the muscles about the elbow and knee hinge joints in men and women. Peak isometric and isokinetic strength was assessed in 54 participants (30 men) using the Biodex System 3 isokinetic dynamometer. Normalized peak torque surfaces varied significantly between flexion and extension (within each joint) and between joints; however, the normalized 3D torque surfaces did not differ between men and women. These findings suggest the underlying joint biomechanics are the primary influences on these strength surface profiles. Therefore, in applications such as digital human modeling, torque-velocity-angle relationships for each joint and torque direction must be uniquely represented to most accurately estimate human strength capability.

The development of localized muscle fatigue has classically been described by the nonlinear intensity-endurance time (ET) curve (Rohmert, 1960; El Ahrache et al., 2006). These empirical intensity-ET relationships have been well-documented and vary between joint regions. We previously proposed a three-compartment biophysical fatigue model, consisting of compartments (i.e. states) for active (M(A)), fatigued (M(F)), and resting (M(R)) muscles, to predict the decay and recovery of muscle force (Xia and Frey Law, 2008). The purpose of this investigation was to determine optimal model parameter values, fatigue (F) and recovery (R), which define the "flow rate" between muscle states and to evaluate the model's accuracy for estimating expected intensity-ET curves. Using a grid-search approach with modified Monte Carlo simulations, over 1 million F and R permutations were used to predict the maximum ET for sustained isometric tasks at 9 intensities ranging from 10% to 90% of maximum in 10% increments (over 9 million simulations total). Optimal F and R values ranged from 0.00589 (F(ankle)) and 0.0182 (R(ankle)) to 0.00058 (F(shoulder)) and 0.00168 (R(shoulder)), reproducing the intensity-ET curves with low mean RMS errors: shoulder (2.7s), hand/grip (5.6s), knee (6.7s), trunk (9.3s), elbow (9.9s), and ankle (11.2s). Testing the model at different task intensities (15-95% maximum in 10% increments) produced slightly higher errors, but largely within the 95% prediction intervals expected for the intensity-ET curves. We conclude that this three-compartment fatigue model can be used to accurately represent joint-specific intensity-ET curves, which may be useful for ergonomic analyses and/or digital human modeling applications., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

A three-compartment model is presented to describe muscle activation, fatigue, and recovery under a variety of loading conditions. Muscle is considered to be in one of three states: resting (M(R)), activated (M(A)), or fatigued (M(F)). A bounded proportional controller represents muscle activation-deactivation, the transfer between M(R) and M(A). The fatigue and recovery rates determine the transfer to/from M(F) state. The model qualitatively demonstrates empirically based fatigue behavior, known as Rohmert's curves, with isometric loading conditions. An expanded version of the model utilizes the properties of three muscle fiber types and a last-in-first-out stack mechanism to represent the known muscle recruitment hierarchy. Additionally, a novel yet practical approach is introduced to quantitatively evaluate task-related muscle fatigue for complex and/or dynamic movements at the joint level, encompassing the nonlinear influences of joint angle and velocity. This approach may have potential for digital human modeling, ergonomics, and other real-time applications due to its computational efficiency.

Chronic spinal cord injury (SCI) induces detrimental musculoskeletal adaptations that adversely affect health status, ranging from muscle paralysis and skin ulcerations to osteoporosis. SCI rehabilitative efforts may increasingly focus on preserving the integrity of paralyzed extremities to maximize health quality using electrical stimulation for isometric training and/or functional activities. Subject-specific mathematical muscle models could prove valuable for predicting the forces necessary to achieve therapeutic loading conditions in individuals with paralyzed limbs. Although numerous muscle models are available, three modeling approaches were chosen that can accommodate a variety of stimulation input patterns. To our knowledge, no direct comparisons between models using paralyzed muscle have been reported. The three models include 1) a simple second-order linear model with three parameters and 2) two six-parameter nonlinear models (a second-order nonlinear model and a Hill-derived nonlinear model). Soleus muscle forces from four individuals with complete, chronic SCI were used to optimize each model's parameters (using an increasing and decreasing frequency ramp) and to assess the models' predictive accuracies for constant and variable (doublet) stimulation trains at 5, 10, and 20 Hz in each individual. Despite the large differences in modeling approaches, the mean predicted force errors differed only moderately (8-15% error; P=0.0042), suggesting physiological force can be adequately represented by multiple mathematical constructs. The two nonlinear models predicted specific force characteristics better than the linear model in nearly all stimulation conditions, with minimal differences between the two nonlinear models. Either nonlinear mathematical model can provide reasonable force estimates; individual application needs may dictate the preferred modeling strategy.

Objective: The purpose of this study was to estimate the loading environment for the distal femur during a novel standing exercise paradigm for people with spinal cord injury., Design: A mathematical model based on experimentally derived parameters., Background: Musculoskeletal deterioration is common after spinal cord injury, often resulting in osteoporotic bone and increased risk of lower extremity fracture. Potential mechanical treatments have yet to be shown to be efficacious; however, no previous attempts have been made to quantify the lower extremity loading during passive, active, and active-resistive stance., Methods: A static, 2-D model was developed to estimate the external forces; the activated quadriceps forces; and the overall bone compression and shear forces in the distal femur during passive (total support of frame), active (quadriceps activated minimally), and active-resistive (quadriceps activated against a resistance) stance., Results: Passive, active, and active-resistive stance resulted in maximal distal femur compression estimates of approximately 45%, approximately 75%, and approximately 240% of body weight, respectively. Quadriceps force estimates peaked at 190% of body weight with active-resistive stance. The distal femur shear force estimates never exceeded 24% of body weight with any form of stance., Conclusions: These results support our hypothesis that active-resistive stance induces the highest lower extremity loads of the three stance paradigms, while keeping shear to a minimum., Relevance: This model allows clinicians to better understand the lower extremity forces resulting from passive, active, and active-resistive stance in individuals with spinal cord injury.