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We have investigated, in fast movements, the hypothesis that bi-articular muscles are preferentially selected to control me direction of force exerted on the environment, while mono-articular muscles are selected to control both this exerted force direction as well as the movement direction. Fourteen subjects performed ballistic arm movements involving shoulder and elbow rotations in the horizontal plane, either with or without an external force applied at the wrist. Joint torques required to counteract the external force were in the same order of magnitude as those required to overcome the inertial load during movements. EMG was recorded from mono- and bi-articular flexors and extensors of me elbow and shoulder. Signals were rectified and integrated (IREMG) over 100 ms following the first detected activity. MANOVA revealed mat, contrary to the hypothesis, IREMG of bi-articular muscles varied with movement direction just as that of the mono-articular muscles. It was concluded that the present data do not support me hypothesis mentioned above. A second finding was that movement effects on IREMG were much stronger than external force effects. This could not be explained using Hill's force-velocity relationship. It may be an indication that in the initiation of fast movements, IREMG is not only tuned to movement dynamics and muscle contractile properties, but also to me dynamics of the build up of an active state of the muscle.

It has been shown in previous research that the initial phase of EMG for a punching movement remained almost unchanged regardless of whether an external force was applied to the arm. The purpose of the present study was to explain this finding with the help of simulations. A two-dimensional model of me arm actuated by 6 Hill-type muscles was used to simulate a punching movement in the horizontal plane from a prescribed starting position with 90° elbow flexion. Input to the model was the stimulation of me muscles, and output were, among others, muscle forces and segmental accelerations. A genetic algorithm was used to determine the muscle onset times mat minimized movement duration and targeting error. In a subsequent forward simulation, the optimized muscle onset times for an unloaded punching movement were superimposed on the isometric stimulation necessary to hold me arm in the starting position while an external force was applied to the arm. The resulting movement was only slightly different from the unloaded movement. It appeared that because of the low level of isometric muscle force prior to the movement, and the high level of stimulation during the movement, muscle force was increased at a rate mat was almost independent of the prior force level. These results confirmed the suggestion that the initial phase of EMG in ballistic movements is more related to the rate of change of force than to the absolute force level. It is hypothesized mat this may simplify the task of the nervous system in the choice of initial muscle activity in ballistic arm movements because no adjustments to varying external forces are required.

This paper provides an overview of forward dynamic neuromusculoskeletal modeling. The aim of such models is to estimate or predict muscle forces, joint moments, and/or joint kinematics from neural signals. This is a four-step process. In the first step, muscle activation dynamics govern the transformation from the neural signal to a measure of muscle activation—a time varying parameter between 0 and 1. In the second step, muscle contraction dynamics characterize how muscle activations are transformed into muscle forces. The third step requires a model of the musculoskeletal geometry to transform muscle forces to joint moments. Finally, the equations of motion allow joint moments to be transformed into joint movements. Each step involves complex nonlinear relationships. The focus of this paper is on the details involved in the first two steps, since these are the most challenging to the biomechanician. The global process is then explained through applications to the study of predicting isometric elbow moments and dynamic knee kinetics.

Functional near-infrared spectroscopy was used to evaluate prefrontal cortex activation differences between older adults with multiple sclerosis (MS) and healthy older adults (HOA) during the performance of a balance- and attention-demanding motor task. Ten older adults with MS and 12 HOA underwent functional near-infrared spectroscopy recording while talking, virtual beam walking, or virtual beam walking while talking on a self-paced treadmill. The MS group demonstrated smaller increases in prefrontal cortex oxygenation levels than HOA during virtual beam walking while talking than talking tasks. These findings indicate a decreased ability to allocate additional attentional resources in challenging walking conditions among MS compared with HOA. This study is the first to investigate brain activation dynamics during the performance of balance- and attention-demanding motor tasks in persons with MS.