Assessment of isometric contractions performed with maximal subjective effort: corresponding results for EEG changes and force measurements

Optimization Reduces Knee-Joint Forces During Walking and Squatting: Validating the Inverse Dynamics Approach for Full Body Movements on Instrumented Knee Prostheses

Because of the redundancy of our motor system, movements can be performed in many ways. While multiple motor control strategies can all lead to the desired behavior, they result in different joint and muscle forces. This creates opportunities to explore this redundancy, for example, for pain avoidance or reducing the risk of further injury. To assess the effect of different motor control optimization strategies, a direct measurement of muscle and joint forces is desirable, but problematic for medical and ethical reasons. Computational modeling might provide a solution by calculating approximations of these forces. In this study, we used a full-body computational musculoskeletal model to (a) predict forces measured in knee prostheses during walking and squatting and (b) study the effect of different motor control strategies (i.e., minimizing joint force vs. muscle activation) on the joint load and prediction error. We found that musculoskeletal models can accurately predict knee joint forces with a root mean squared error of <0.5 body weight (BW) in the superior direction and about 0.1 BW in the medial and anterior directions. Generally, minimization of joint forces produced the best predictions. Furthermore, minimizing muscle activation resulted in maximum knee forces of about 4 BW for walking and 2.5 BW for squatting. Minimizing joint forces resulted in maximum knee forces of 2.25 BW and 2.12 BW, that is, a reduction of 44% and 15%, respectively. Thus, changing the muscular coordination strategy can strongly affect knee joint forces. Patients with a knee prosthesis may adapt their neuromuscular activation to reduce joint forces during locomotion.

Model-based analysis of fatigued human knee extensors : Effects of isometrically induced fatigue on Hill-type model parameters and ballistic contractions

This study investigated the effect of isometrically induced fatigue on Hill-type muscle model parameters and related task-dependent effects. Parameter identification methods were used to extract fatigue-related parameter trends from isometric and ballistic dynamic maximum voluntary knee extensions. Nine subjects, who completed ten fatiguing sets, each consisting of nine 3 s isometric maximum voluntary contractions with 3 s rest plus two ballistic contractions with different loads, were analyzed. Only at the isometric task, the identified optimized model parameter values of muscle activation rate and maximum force generating capacity of the contractile element decreased from [Formula: see text] to [Formula: see text] Hz and from [Formula: see text] to [Formula: see text] N, respectively. For all tasks, the maximum efficiency of the contractile element, mathematically related to the curvature of the force-velocity relation, increased from [Formula: see text] to [Formula: see text]. The model parameter maximum contraction velocity decreased from [Formula: see text] to [Formula: see text] m/s and the stiffness of the serial elastic element from [Formula: see text] to [Formula: see text] N/mm. Thus, models of fatigue should consider fatigue dependencies in active as well as in passive elements, and muscle activation dynamics should account for the task dependency of fatigue.

An improved method to determine neuromuscular properties using force laws - From single muscle to applications in human movements

We evaluate an improved method for individually determining neuromuscular properties in vivo. The method is based on Hill's equation used as a force law combined with Newton's equation of motion. To ensure the range of validity of Hill's equation, we first perform detailed investigations on in vitro single muscles. The force-velocity relation determined with the model coincides well with results obtained by standard methods (r=.99) above 20% of the isometric force. In addition, the model-predicted force curves during work loop contractions very well agree with measurements (mean difference: 2-3%). Subsequently, we deduce theoretically under which conditions it is possible to combine several muscles of the human body to model muscles. This leads to a model equation for human leg extension movements containing parameters for the muscle properties and for the activation. To numerically determine these invariant neuromuscular properties we devise an experimental method based on concentric and isometric leg extensions. With this method we determine individual muscle parameters from experiments such that the simulated curves agree well with experiments (r=.99). A reliability test with 12 participants revealed correlations r=.72-.91 for the neuromuscular parameters (p<.01). Predictions of similar movements under different conditions show mean errors of about 5%. In addition, we present applications in sports practise and theory.

Biomechanical muscle properties and angiotensin-converting enzyme gene polymorphism: a model-based study

Previous studies reported an association of angiotensin-converting enzyme (ACE) I/D gene polymorphism with physical performance. The study was based on the hypothesis that certain individual biomechanical muscle properties could be associated with ACE genotype and that they could influence athletes' physical performance. Movement-independent individual biomechanical muscle properties of 62 sports students were determined by applying a mathematical model to experimental data. Subjects exerted concentric and isometric contractions at a leg-press. The model was based on a Hill-type muscle model, a function describing the geometrical arrangement of human leg extensor muscles, and an exponential function describing muscle activation. Mouthwash samples were taken to determine the ACE genotypes. Several combinations of experimentally determined biomechanical properties served as input variables for a discriminant analysis. We were able to show that individual biomechanical muscle properties correlated with ACE I/D gene polymorphism. With a combination of certain individual muscle parameters based on a Hill-type muscle model, we were able to separate three individual ACE genotypes (II, ID, DD) in a significant way (P<0.03) and correctly classify 89% of the cases using a discriminant analysis. We conclude that local biomechanical muscle properties are influenced by ACE genotype.

Power of theta waves in the EEG of human subjects increases during recall of haptic information

Several studies have reported a functional relationship between spectral power within the theta-band of the EEG (theta-power) and memory load while processing visual or semantic information. We investigated theta power during the processing of different complex haptic stimuli using a delayed recall design. The haptic explorations consisted of palpating the structure of twelve sunken reliefs with closed eyes. Subjects had to reproduce each relief by drawing it 10 s after the end of the exploration. The relationship between mean theta power and mean exploration time was analysed using a regression model. A linear relationship was found between the exploration time and theta power over fronto-central regions (Fp1, Fp2, F3, F7, F8, Fz, C3) directly before the recall of the relief. This result is interpreted in favour of the hypothesis that fronto-central theta power of the EEG correlates with the load of working memory independent of stimulus modality.