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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.

A central problem in motor control relates to the coordination of the arm's many degrees of freedom. This problem concerns the many arm postures (kinematics) that correspond to the same hand position in space and the movement trajectories between begin and end position (dynamics) that result in the same arm postures. The aim of this study was to compare the predictions for arm kinematics by various models on human motor control with experimental data and to study the relation between kinematics and dynamics. Goal-directed arm movements were measured in 3-D space toward far and near targets. The results demonstrate that arm postures for a particular target depend on previous arm postures, contradicting Donders's law. The minimum-work and minimum-torque-change models, on the other hand, predict a much larger effect of initial posture than observed. These data suggest that both kinematics and dynamics affect postures and that their relative contribution might depend on instruction and task complexity.

Influence of mechanical interactions between the shoulder and elbow on production of different coordination patterns during horizontal arm movements is investigated in the present study. Subjects performed cyclical movements along a circle and along lines of 4 different orientations. Cycling frequency was manipulated to highlight control features responsible for interactive torque regulation. When the shoulder was involved in motion, torque analysis revealed that this joint was controlled similarly during all movement types. At the elbow, however, each movement type required a specific pattern of regulation of interactive torque with muscle torque. When interactive torque acted in the direction of the required elbow rotation, the demands for active control were lower than when the interactive torque resisted elbow motion and had to be actively suppressed. Kinematic analysis demonstrated that increases in cycling frequency systematically deformed the fingertip path. The amount of these deformations differed across movement types, being more pronounced for movements where the interactive torque resisted joint motion. It appears that interactive torque can assist or resist movement at the joints, making control of some movement types more difficult than others.

In this paper, a literature review is presented regarding the hydrodynamic effects of different hand and arm movements during swimming with the aim to identify lacunae in current methods and knowledge, and to distil practical guidelines for coaches and swimmers seeking to increase swimming speed. Experimental and numerical studies are discussed, examining the effects of hand orientation, thumb position, finger spread, sculling movements, and hand accelerations during swimming, as well as unsteady properties of vortices due to changes in hand orientation. Collectively, the findings indicate that swimming speed may be increased by avoiding excessive sculling movements and by spreading the fingers slightly. In addition, it appears that accelerating the hands rather than moving them at constant speed may be beneficial, and that (in front crawl swimming) the thumb should be abducted during entry, catch, and upsweep, and adducted during the pull phase. Further experimental and numerical research is required to confirm these suggestions and to elucidate their hydrodynamic underpinnings and identify optimal propulsion techniques. To this end, it is necessary that the dynamical motion and resulting unsteady effects are accounted for, and that flow visualization techniques, force measurements, and simulations are combined in studying those effects.

Using a model for the neuromuscular control of human arm movements, the possible roles of different proprioceptive signals are analyzed. The control model is represented by a neural network and includes both feedback and feedforward control modes. After a learning process, the controller regulates a wide range of arm movements. Evaluation of the roles of different afferent signals shows that sensed muscle forces are important to achieve accurate control of fast movements. For a moderately high loop delay (50 ms), velocity feedback is not essential, but for small loop delays (0 and 25 ms) an increased performance is attained by feedback of velocity. Position sense is essential to prevent steady-state errors. The arm impedance is affected considerably by the delay in the control loop and by the configuration of the motor control system. The achieved relation between muscle length and force is similar to the invariant characteristics laying at the basis of the equilibrium-point (EP) hypothesis. However, control of fast movements on the basis of EP alone is not feasible, but requires feedforward control. During training in a velocity-dependent force field, the impedance of the arm increases at first, due to enhanced cocontraction. Subsequently, both impedance and movement errors decrease, indicating a successful representation of the changed inverse dynamics.

A three-dimensional (3-D) arm movement model is presented to simulate kinematic properties and muscle forces in reaching arm movements. Healthy subjects performed reaching movements repetitively either with or without a load in the hand. Joint coordinates were measured. Muscle moment arms, 3-D angular acceleration, and moment of inertias of arm segments were calculated to determine 3-D joint torques. Variances of hand position, arm configuration, and muscle activities were calculated. Ratios of movement variances observed in the two conditions (load versus without load) showed no differences for hand position and arm configuration variances. Virtual muscle force variances for all muscles except deltoid posterior and EMG variances for four muscles increased significantly by moving with the load. The greatly increased variances in muscle activity did not imply equally high increments in kinematic variances. We conclude that enhanced muscle cooperation through synergies helps to stabilize movement at the kinematic level when a load is added.

We have investigated whether differences in EMG activity in mono- and bi-articuiar muscles for concentric and eccentric contractions (van Bolhuis, Gielen, & van Ingen Schenau, 1998) have to be attributed to a specific muscle coordination strategy or whether they are merely a demonstration of adaptations necessary to adjust for muscle contractile properties. Slow, multi-joint arm movements were studied in a horizontal plane with an external force applied at the wrist. Kinematics and electromyography data from 10 subjects were combined with data from a 3-D model of the arm and a Hill-type muscle model Data for both mono- and bi-articular muscles revealed a higher activation in concentric than in eccentric contractions. The model calculations indicated that the measured difference in activation (20%) was much larger than expected based on the force-velocity relationship (predicting changes of ~5%). Although these findings eliminate the force-velocity relationship as the main explanation for changes in EMG, it cannot be ruled out that other muscle contractile properties, such as history dependence of muscle force, determine muscle activation levels in the task that was studied.

Jerk (time derivative of acceleration) of the endpoint of a multi-joint kinematic chain can be represented as the sum of terms related to jerks, accelerations, and velocities in individual joints. We investigated the relative contribution of these terms during simulations of planar movement of a 3-segment kinematic chain and also during unconstrained movements at different velocities, over different amplitudes, and with different intentionally changed curvature. Our results demonstrate that the term related to individual joint jerks dominates in the total endpoint jerk. This domination was particularly strong during voluntary movements and was not as striking during the simulations based on 5th-order polynomial functions for individual joint trajectories. Thus, the minimum-jerk criterion for multi-joint movements can be well approximated by minimization of the jerk-related terms for individual joints. The decomposition of endpoint jerk into its terms shows potential limitations of the commonly used 5th-order polynomial modeling for describing voluntary multi-joint movements.