A Novel Explicit Analytical Multibody Approach for the Analysis of Upper Limb Dynamics and Joint Reactions Calculation Considering Muscle Wrapping

The aim of this paper is to present an explicit analytical biomechanical multibody procedure able to be implemented in the solution of the musculoskeletal systems inverse dynamics problems. The model is proposed in formal multibody analysis and implemented in the Matlab numerical environment. It is based on the constraint kinematical behaviour analysis and considers both linear muscle actuators and curved ones, by calculating the geodesic muscle path over wrapping surfaces fixed to the bodies. The model includes the Hill muscle approach in order to evaluate both the contractile elements’ actions and the passive ones. With the aim to have a first validation, the model was applied to the dynamical analysis of the “arm26” OpenSim model, an upper limb subjected to external forces of gravity and to known kinematics. The comparison of results shows interesting matching in terms of kinematical analysis, driving forces, muscles’ activations and joint reactions, proving the reliability of the proposed approach in all cases in which it is necessary to achieve in-silico explicit determinations of the upper limb dynamics and joint reactions (i.e., in joint tribological optimization).

Distinct Proximal and Distal Corticospinal Signals Embed Limb Dynamics Through the Modulation of Stiffness

The complexities of the human musculoskeletal system and its interactions with the environment creates a difficult challenge for the neural control of movement. The consensus is that the nervous system solves this challenge by embedding the dynamical properties of the body and the environment. However, the modality of control signals and how they are generated appropriately for the task demands are a matter of active debate. We used transcranial magnetic stimulation over the primary motor cortex to show that the excitability of the corticospinal tract is modulated to compensate for limb dynamics during reaching tasks in humans. Surprisingly, few profiles of corticospinal modulation in some muscles and conditions reflected Newtonian parameters of movement, such as kinematics or active torques. Instead, the overall corticospinal excitability was differentially modulated in proximal and distal muscles, which corresponded to different stiffness at proximal and distal joints. This suggests that the descending corticospinal signal determines the proximal and distal impedance of the arm for independent functional control of reaching and grasping.Significance StatementThe nervous system integrates both the physical properties of the human body and the environment to create a rich repertoire of actions. How these calculations are happening remains poorly understood. Neural activity is known to be correlated with different variables from the Newtonian equations of motion that describe forces acting on the body. In contrast, our data show that the overall activity of the descending neural signals is less related to the individual Newtonian variables and more related to limb impedance. We show that the physical properties of the arm are controlled by two distinct proximal and distal descending neural signals modulating components of limb stiffness. This identifies distinct neural control mechanisms for the transport and manipulation actions of reach.

The cost of being stable: Trade-offs between effort and stability across a landscape of redundant motor solutions

Current musculoskeletal modeling approaches cannot account for variability in muscle activation patterns seen across individuals, who may differ in motor experience, motor training, or neurological health. While musculoskeletal simulations typically select muscle activation patterns that minimize muscular effort, and generate unstable limb dynamics, a few studies have shown that maximum-effort solutions can improve limb stability. Although humans and animals likely adopt solutions between these two extremes, we lack principled methods to explore how effort and stability shape how muscle activation patterns differ across individuals. Here we characterized trade-offs between muscular effort and limb stability in selecting muscle activation patterns for an isometric force generation task in a musculoskeletal model of the cat hindlimb. We define effort as the sum of squared activation across all muscles, and limb stability by the maximum real part of the eigenvalues of the linearized musculoskeletal system dynamics, with more negative values being more stable. Surprisingly, stability increased rapidly with only small increases in effort from the minimum-effort solution, suggesting that very small amounts of muscle coactivation are beneficial for postural stability. Further, effort beyond 40% of the maximum possible effort did not confer further increases in stability. We also found multiple muscle activation patterns with equivalent effort and stability, which could underlie variability observed across individuals with similar motor ability. Trade-off between muscle effort and limb stability could underlie diversity in muscle activation patterns observed across individuals, disease, learning, and rehabilitation.Author summaryCurrent computational musculoskeletal models select muscle activation patterns that minimize the amount of muscle activity used to generate a movement, creating unstable limb dynamics. However, experimentally, muscle activation patterns with various level of co-activation are observed for performing the same task both within and across individuals that likely help to stabilize the limb. Here we show that a trade-off between muscular effort and limb stability across the wide range of possible muscle activation patterns for a motor task could explain the diversity of muscle activation patterns seen across individuals, disease, learning and rehabilitation. Increased muscle activity is necessary to stabilize the limb, but could also limit the ability to learn new muscle activation pattern, potentially providing a mechanism to explain individual-specific muscle coordination patterns in health and disease. Finally, we provide a straightforward method for improving the physiological relevance of muscle activation pattern and musculoskeletal stability in simulations.