Contribution of non-extensor muscles of the leg to maximal-effort countermovement jumping

BACKGROUND: The purpose of this study was to determine the effects of non-extensor muscles of the leg (i.e., muscles whose primary function is not leg extension) on the kinematics and kinetics of human maximal-effort countermovement jumping. Although it is difficult to address this type of question through experimental procedures, the methodology of computer simulation can be a powerful tool. METHODS: A skeletal model that has nine rigid body segments and twenty degrees of freedom was developed. Two sets of muscle models were attached to this skeletal model: all (most of) major muscles in the leg ("All Muscles" model) and major extensor muscles in the leg (i.e., muscles whose primary function is leg extension; "Extensors Only" model). Neural activation input signal was represented by a series of step functions with a step duration of 0.05 s. Simulations were started from an identical upright standing posture. The optimal pattern of the activation input signal was searched through extensive random-search numerical optimization with a goal of maximizing the height reached by the mass centre of the body after jumping up. RESULTS: The simulated kinematics was almost two-dimensional, suggesting the validity of two-dimensional analyses when evaluating net mechanical outputs around the joints using inverse dynamics. A greater jumping height was obtained for the "All Muscles" model (0.386 m) than for the "Extensors Only" model (0.301 m). For the "All Muscles" model, flexor muscles developed force in the beginning of the countermovement. For the "All Muscles" model, the sum of the work outputs from non-extensor muscles was 47.0 J, which was 13% of the total amount (359.9 J). The quantitative distribution of the work outputs from individual muscles was markedly different between these two models. CONCLUSION: It was suggested that the contribution of non-extensor muscles in maximal-effort countermovement jumping is substantial. The use of a computer simulation model that includes non-extensor muscles seems to be more desirable for the assessment of muscular outputs during jumping

Effects of jump and balance training on knee kinematics and electromyography of female basketball athletes during a single limb drop landing: pre-post intervention study

<p>Abstract</p> <p>Background</p> <p>Some research studies have investigated the effects of anterior cruciate ligament (ACL) injury prevention programs on knee kinematics during landing tasks; however the results were different among the studies. Even though tibial rotation is usually observed at the time of ACL injury, the effects of training programs for knee kinematics in the horizontal plane have not yet been analyzed. The purpose of this study was to determine the effects of a jump and balance training program on knee kinematics including tibial rotation as well as on electromyography of the quadriceps and hamstrings in female athletes.</p> <p>Methods</p> <p>Eight female basketball athletes participated in the experiment. All subjects performed a single limb landing at three different times: the initial test, five weeks later, and one week after completing training. The jump and balance training program lasted for five weeks. Knee kinematics and simultaneous electromyography of the rectus femoris and Hamstrings before training were compared with those measured after completing the training program.</p> <p>Results</p> <p>After training, regarding the position of the knee at foot contact, the knee flexion angle for the Post-training trial (mean (SE): 24.4 (2.1) deg) was significantly larger than that for the Pre-training trial (19.3 (2.5) deg) (p < 0.01). The absolute change during landing in knee flexion for the Post-training trial (40.2 (1.9) deg) was significantly larger than that for the Pre-training trial (34.3 (2.5) deg) (p < 0.001). Tibial rotation and the knee varus/valgus angle were not significantly different after training. A significant increase was also found in the activity of the hamstrings 50 ms before foot contact (p < 0.05).</p> <p>Conclusions</p> <p>The jump and balance training program successfully increased knee flexion and hamstring activity of female athletes during landing, and has the possibility of producing partial effects to avoid the characteristic knee position observed in ACL injury, thereby preventing injury. However, the expected changes in frontal and transverse kinematics of the knee were not observed.</p

In Vivo Dynamics of the Musculoskeletal System Cannot Be Adequately Described Using a Stiffness-Damping-Inertia Model

Background: Visco-elastic properties of the (neuro-)musculoskeletal system play a fundamental role in the control of posture and movement. Often, these properties are described and identified using stiffness-damping-inertia (KBI) models. In such an approach, perturbations are applied to the (neuro-)musculoskeletal system and subsequently KBI-model parameters are optimized to obtain a best fit between simulated and experimentally observed responses. Problems with this approach may arise because a KBI-model neglects critical aspects of the real musculoskeletal system. Methodology/Principal Findings: The purpose of this study was to analyze the relation between the musculoskeletal properties and the stiffness and damping estimated using a KBI-model, to analyze how this relation is affected by the nature of the perturbation and to assess the sensitivity of the estimated stiffness and damping to measurement errors. Our analyses show that the estimated stiffness and damping using KBI-models do not resemble any of the dynamical parameters of the underlying system, not even when the responses are very accurately fitted by the KBI-model. Furthermore, the stiffness and damping depend non-linearly on all the dynamical parameters of the underlying system, influenced by the nature of the perturbation and the time interval over which the KBI-model is optimized. Moreover, our analyses predict a very high sensitivity of estimated parameters to measurement errors. Conclusions/Significance: The results of this study suggest that the usage of stiffness-damping-inertia models t

Estimation of Ligament Loading and Anterior Tibial Translation in Healthy and ACL-Deficient Knees During Gait and the Influence of Increasing Tibial Slope Using EMG-Driven Approach

The purpose of this study was to develop a biomechanical model to estimate anterior tibial translation (ATT), anterior shear forces, and ligament loading in the healthy and anterior cruciate ligament (ACL)-deficient knee joint during gait. This model used electromyography (EMG), joint position, and force plate data as inputs to calculate ligament loading during stance phase. First, an EMG-driven model was used to calculate forces for the major muscles crossing the knee joint. The calculated muscle forces were used as inputs to a knee model that incorporated a knee–ligament model in order to solve for ATT and ligament forces. The model took advantage of using EMGs as inputs, and could account for the abnormal muscle activation patterns of ACL-deficient gait. We validated our model by comparing the calculated results with previous in vitro, in vivo, and numerical studies of healthy and ACL-deficient knees, and this gave us confidence on the accuracy of our model calculations. Our model predicted that ATT increased throughout stance phase for the ACL-deficient knee compared with the healthy knee. The medial collateral ligament functioned as the main passive restraint to anterior shear force in the ACL-deficient knee. Although strong co-contraction of knee flexors was found to help restrain ATT in the ACL-deficient knee, it did not counteract the effect of ACL rupture. Posterior inclination angle of the tibial plateau was found to be a crucial parameter in determining knee mechanics, and increasing the tibial slope inclination in our model would increase the resulting ATT and ligament forces in both healthy and ACL-deficient knees

Optimization of Muscle Activity for Task-Level Goals Predicts Complex Changes in Limb Forces across Biomechanical Contexts

Optimality principles have been proposed as a general framework for understanding motor control in animals and humans largely based on their ability to predict general features movement in idealized motor tasks. However, generalizing these concepts past proof-of-principle to understand the neuromechanical transformation from task-level control to detailed execution-level muscle activity and forces during behaviorally-relevant motor tasks has proved difficult. In an unrestrained balance task in cats, we demonstrate that achieving task-level constraints center of mass forces and moments while minimizing control effort predicts detailed patterns of muscle activity and ground reaction forces in an anatomically-realistic musculoskeletal model. Whereas optimization is typically used to resolve redundancy at a single level of the motor hierarchy, we simultaneously resolved redundancy across both muscles and limbs and directly compared predictions to experimental measures across multiple perturbation directions that elicit different intra- and interlimb coordination patterns. Further, although some candidate task-level variables and cost functions generated indistinguishable predictions in a single biomechanical context, we identified a common optimization framework that could predict up to 48 experimental conditions per animal (n = 3) across both perturbation directions and different biomechanical contexts created by altering animals' postural configuration. Predictions were further improved by imposing experimentally-derived muscle synergy constraints, suggesting additional task variables or costs that may be relevant to the neural control of balance. These results suggested that reduced-dimension neural control mechanisms such as muscle synergies can achieve similar kinetics to the optimal solution, but with increased control effort (≈2×) compared to individual muscle control. Our results are consistent with the idea that hierarchical, task-level neural control mechanisms previously associated with voluntary tasks may also be used in automatic brainstem-mediated pathways for balance