Search

A key problem for biological motor control is to establish a link between an idea of a movement and the generation of a set of muscle-stimulating signals that lead to the movement execution. The number of signals to generate is thereby larger than the body's mechanical degrees of freedom in which the idea of the movement may be easily expressed, as the movement is actually executed in this space. A mathematical formulation that provides a solving link is presented in this paper in the form of a layered, hierarchical control architecture. It is meant to synthesise a wide range of complex three-dimensional muscle-driven movements. The control architecture consists of a 'conceptional layer', where the movement is planned, a 'structural layer', where the muscles are stimulated, and between both an additional 'transformational layer', where the muscle-joint redundancy is resolved. We demonstrate the operativeness by simulating human stance and squatting in a three-dimensional digital human model (DHM). The DHM considers 20 angular DoFs and 36 Hill-type muscle–tendon units (MTUs) and is exposed to gravity, while its feet contact the ground via reversible stick–slip interactions. The control architecture continuously stimulates all MTUs ('structural layer') based on a high-level, torque-based task formulation within its 'conceptional layer'. Desired states of joint angles (postural plan) are fed to two mid-level joint controllers in the 'transformational layer'. The 'transformational layer' communicates with the biophysical structures in the 'structural layer' by providing direct MTU stimulation contributions and further input signals for low-level MTU controllers. Thereby, the redundancy of the MTU stimulations with respect to the joint angles is resolved, i.e. a link between plan and execution is established, by exploiting some properties of the biophysical structures modelled. The resulting joint torques generated by the MTUs via their moment arms are fed back to the conceptional layer, closing the high-level control loop. Within our mathematical formulations of the Jacobian matrix-based layer transformations, we identify the crucial information for the redundancy solution to be the muscle moment arms, the stiffness relations of muscle and tendon tissue within the muscle model, and the length–stimulation relation of the muscle activation dynamics. The present control architecture allows the straightforward feeding of conceptional movement task formulations to MTUs. With this approach, the problem of movement planning is eased, as solely the mechanical system has to be considered in the conceptional plan. [ABSTRACT FROM AUTHOR]

State-space control of myoelectric devices and real-time visualization of muscle forces in virtual rehabilitation require measuring or estimating muscle dynamic states: neuromuscular activation, tendon force and muscle length. This paper investigates whether regular (KF) and extended Kalman filters (eKF), derived directly from Hill-type muscle mechanics equations, can be used as real-time muscle state estimators for isometric contractions using raw electromyography signals (EMG) as the only available measurement. The estimators' amplitude error, computational cost, filtering lags and smoothness are compared with usual EMG-driven analysis, performed offline, by integrating the nonlinear Hill-type muscle model differential equations (offline simulations-OS). EMG activity of the three triceps surae components (soleus, gastrocnemius medialis and gastrocnemius lateralis), in three torque levels, was collected for ten subjects. The actualization interval (AI) between two updates of the KF and eKF was also varied. The results show that computational costs are significantly reduced (70x for KF and 17 $$\times $$ for eKF). The filtering lags presented sharp linear relationships with the AI (0-300 ms), depending on the state and activation level. Under maximum excitation, amplitude errors varied in the range 10-24% for activation, 5-8% for tendon force and 1.4-1.8% for muscle length, reducing linearly with the excitation level. Smoothness, measured by the ratio between the average standard variations of KF/eKF and OS estimations, was greatly reduced for activation but converged exponentially to 1 for the other states by increasing AI. Compared to regular KF, extended KF does not seem to improve estimation accuracy significantly. Depending on the particular application requirements, the most appropriate KF actualization interval can be selected. [ABSTRACT FROM AUTHOR]

A model is presented showing how peripheral factors may cause a process of movement adaptation that leads to task-specific focal hand dystonia in musicians (FHDM). To acquire a playing technique, the hand must find effective and physiologically sustainable movements within a complex set of functional demands and anatomic, ergonomic, and physiological constraints. In doing so, individually discriminating constraints may become effective, such as limited anatomic independence of finger muscles/tendons, limited joint ranges of motion, or (subclinical) neuromusculoskeletal defects. These factors may, depending on the instrument-specific playing requirements, compromise or exclude functional playing movements. The controller (i.e., the brain) then needs to develop alternative motions to execute the task, which is called compensation. We hypothesize that, if this compensation process does not converge to physiologically sustainable muscle activation patterns that satisfy all constraints, compensation could increase indefinitely under the pressure of practice. Dystonic symptoms would become manifest when overcompensation occurs, resulting in motor patterns that fail in proper task execution. The model presented in this paper only concerns the compensatory processes preceding such overcompensations and does not aim to explain the nature of the dystonic motions themselves. While the model considers normal learning processes in the development of compensations, neurological predispositions could facilitate developing overcompensations or further abnormal motor programs. The model predicts that if peripheral factors are involved, FHDM symptoms would be preceded by long-term gradual changes in playing movements, which could be validated by prospective studies. Furthermore, the model implies that treatment success might be enhanced by addressing the conflict between peripheral factors and playing tasks before decompensating/retraining the affected movements. [ABSTRACT FROM AUTHOR]