This study involved an optimization, numerical analysis of a network for two-hand multi-finger force production, analogous in its structure to the double-representation mirror image (DoReMi) network suggested earlier based on neurophysiological data on cortical finger representations. The network accounts for phenomena of enslaving (unintended finger force production), force deficit (smaller force produced by a finger in multi-finger tasks as compared to its single-finger task), and bilateral deficit (smaller forces produced in two-hand tasks as compared to one-hand tasks). Matrices of connection weights were computed, and the results of optimization were compared to the experimental data on finger forces during one- and two-hand maximal force production (MVC) tasks. The network was able to reproduce the experimental data in two-hand experiments with high accuracy (average error was 1.2 N); it was also able to reproduce findings in one-hand multi-finger MVC tasks, which were not used during the optimization procedure, although with a somewhat higher error (2.8 N). Our analysis supports the feasibility of the DoReMi network. It suggests that within-a-hand force deficit and bilateral force deficit are phenomena of different origins whose effects add up. Is also supports a hypothesis that force deficit and enslaving have different neural origins.
Simon R. Goodman, Mark L. Latash, Sheng Li and Vladimir M. Zatsiorsky
Daniel A. Marinho, Tiago M. Barbosa, Victor M. Reis, Per L. Kjendlie, Francisco B. Alves, João P. Vilas-Boas, Leandro Machado, António J. Silva and Abel I. Rouboa
The main aim of this study was to investigate the effect of finger spread on the propulsive force production in swimming using computational fluid dynamics. Computer tomography scans of an Olympic swimmer hand were conducted. This procedure involved three models of the hand with differing finger spreads: fingers closed together (no spread), fingers with a small (0.32 cm) spread, and fingers with large (0.64 cm) spread. Steady-state computational fluid dynamics analyses were performed using the Fluent code. The measured forces on the hand models were decomposed into drag and lift coefficients. For hand models, angles of attack of 0°, 15°, 30°, 45°, 60°, 75°, and 90°, with a sweep back angle of 0°, were used for the calculations. The results showed that the model with a small spread between fingers presented higher values of drag coefficient than did the models with fingers closed and fingers with a large spread. One can note that the drag coefficient presented the highest values for an attack angle of 90° in the three hand models. The lift coefficient resembled a sinusoidal curve across the attack angle. The values for the lift coefficient presented few differences among the three models, for a given attack angle. These results suggested that fingers slightly spread could allow the hand to create more propulsive force during swimming.
Gregory P. Slota, Mark L. Latash and Vladimir M. Zatsiorsky
When grasping and manipulating objects, the central controller utilizes the mechanical advantage of the normal forces of the fingers for torque production. Whether the same is valid for tangential forces is unknown. The main purpose of this study was to determine the patterns of finger tangential forces and the use of mechanical advantage as a control mechanism when dealing with objects of nonuniform finger positioning. A complementary goal was to explore the interaction of mechanical advantage (moment arm) and the role a finger has as a torque agonist/antagonist with respect to external torques (±0.4 N m). Five 6-df force/torque transducers measured finger forces while subjects held a prism handle (6 cm width × 9 cm height) with and without a single finger displaced 2 cm (handle width). The effect of increasing the tangential moment arm was significant (p < .01) for increasing tangential forces (in >70% of trials) and hence creating greater moments. Thus, the data provides evidence that the grasping system as a rule utilizes mechanical advantage for generating tangential forces. The increase in tangential force was independent of whether the finger was acting as a torque agonist or antagonist, revealing their effects to be additive.
Sheng Li, Jennifer A. Stevens, Derek G. Kamper and William Z. Rymer
The purpose of this study was to investigate the effect of motor imagery on the premotor time (PMT). Twelve healthy adults performed reaction time movements in response to external visual signals at rest, when holding an object (muscle activation), or performing different background imagined movements (motor imagery). When compared to rest, muscle activation reduced the PMT; imagined finger extension of the right hand and imagined finger flexion of the left hand elongated the PMT; imagined finger flexion of the right hand had no effect on the PMT. This movement-specific effect is interpreted as the sum of the excitatory effect caused by enhanced corticospinal excitability specifically for the primary mover of the imagined movement and an overall inhibition associated with increased task complexity during motor imagery. Our results clearly demonstrate that motor imagery has movement-specific effects on the PMT.
Temporal and spatial movement characteristics are often seen as controlled separately, although they are not independent. Even in the case of simple oscillations mean frequency and mean amplitude covary when one or the other is changed intentionally. The present experiment revealed that in rapid finger oscillations there is also a cycle-to-cycle covariation so that smaller amplitudes are associated with locally increased frequency and (the associated) earlier electromyographic (EMG) bursts. Both globally and locally the observed covariations are consistent with modeling rhythmic movements as output of a driven damped oscillator. The existence of local spatio-temporal covariations suggests limitations for models of timing and reasons for the observation that spatio-temporal movement characteristics cannot be chosen arbitrarily even in uniarticular movements.
Jae Kun Shim, Jeffrey Hsu, Sohit Karol and Ben F. Hurley
The purpose of the current study was to investigate the effects of finger strength training (ST) on finger strength, independence, force control, and adaptations in multifinger coordination. Thirty-three healthy, young (23.0 ± 2.9 years) subjects were randomly assigned into 4 groups. Group 1 (G1) trained all fingers together, Group 2 (G2) trained individual fingers without restricting movements of the non-training fingers, and Group 3 (G3) trained individual fingers while restricting the movement of the nontraining fingers. The control group (G0) did not undergo any training. A vertically hanging load was attached to a spring that passed through a pulley. The other end of the string extended to the horizontal plane and had thimbles attached to it. Subjects were asked to rest their forearm on the table and lift the load by inserting their fingers into the thimbles. The training protocol lasted 6 weeks. Identical experimental tests were conducted 4 times, biweekly, across the 6-week training. Force coordination and moment coordination, defined as synergies stabilizing the resultant force and the resultant moment of all finger forces, in a multifinger pressing task were quantified using the Uncontrolled Manifold (UCM) analysis. The UCM analysis allocates motor variability into two components, one in the null space of a motor task and the other perpendicular to the null space. During multifinger pressing tasks, multifinger coordination exists when the variability in the null space is greater than the variability in the subspace perpendicular to the null space. The multifinger coordination was quantified as the difference between the variance within the null space and that perpendicular to the null space, normalized by the total variance. Thus, the coordination measure in our analysis is a unitless variable. A greater coordination measure indicates better multifinger coordination. Moment-stabilizing multifinger coordination increased only in G1 (from 1.197 ± 0.004 to 1.323 ± 0.002, p < .01), and force-stabilizing coordination increased only in G3 (from 0.207 ± 0.106 to 0.727 ± 0.071, p < .01). Finger strength, measured by the maximal voluntary finger force of pressing 4 fingers, increased significantly in all training groups (from 103.7 ± 3.1 N to 144.0 ± 3.6 N for training groups, all p < .001). Finger-force errors, quantified by the deviations between the required force profiles (20% maximal voluntary force) presented to the subjects and the actual force produced, decreased significantly with ST for all the training groups (all p < .05). Finger independence also decreased significantly for all the training groups (p < .05). We conclude that the neuromuscular system adaptations to multifinger ST are specific to the training protocol being employed, yielding improvements in different types of multifinger coordination (i.e., coordination-specific ST), finger-force control, and finger strength and a decrease in finger independence. Finger independence, depending on the nature of the task, might or might not be favorable to certain task performances. We suggest that ST protocol should be carefully designed for the improvement of specific coordination of multieffector motor systems.
Stacey L. Gorniak, Marcos Duarte and Mark L. Latash
We explored possible effects of negative covariation among finger forces in multifinger accurate force production tasks on the classical Fitts’s speed-accuracy trade-off. Healthy subjects performed cyclic force changes between pairs of targets “as quickly and accurately as possible.” Tasks with two force amplitudes and six ratios of force amplitude to target size were performed by each of the four fingers of the right hand and four finger combinations. There was a close to linear relation between movement time and the log-transformed ratio of target amplitude to target size across all finger combinations. There was a close to linear relation between standard deviation of force amplitude and movement time. There were no differences between the performance of either of the two “radial” fingers (index and middle) and the multifinger tasks. The “ulnar” fingers (little and ring) showed higher indices of variability and longer movement times as compared with both “radial” fingers and multifinger combinations. We conclude that potential effects of the negative covariation and also of the task-sharing across a set of fingers are counterbalanced by an increase in individual finger force variability in multifinger tasks as compared with single-finger tasks. The results speak in favor of a feed-forward model of multifinger synergies. They corroborate a hypothesis that multifinger synergies are created not to improve overall accuracy, but to allow the system larger flexibility, for example to deal with unexpected perturbations and concomitant tasks.
J. Paulo Vilas-Boas, Rui J. Ramos, Ricardo J. Fernandes, António J. Silva, Abel I. Rouboa, Leandro Machado, Tiago M. Barbosa and Daniel A. Marinho
The aim of this research was to numerically clarify the effect of finger spreading and thumb abduction on the hydrodynamic force generated by the hand and forearm during swimming. A computational fluid dynamics (CFD) analysis of a realistic hand and forearm model obtained using a computer tomography scanner was conducted. A mean flow speed of 2 m·s−1 was used to analyze the possible combinations of three finger positions (grouped, partially spread, totally spread), three thumb positions (adducted, partially abducted, totally abducted), three angles of attack (a = 0°, 45°, 90°), and four sweepback angles (y = 0°, 90°, 180°, 270°) to yield a total of 108 simulated situations. The values of the drag coefficient were observed to increase with the angle of attack for all sweepback angles and finger and thumb positions. For y = 0° and 180°, the model with the thumb adducted and with the little finger spread presented higher drag coefficient values for a = 45° and 90°. Lift coefficient values were observed to be very low at a = 0° and 90° for all of the sweepback angles and finger and thumb positions studied, although very similar values are obtained at a = 45°. For y = 0° and 180°, the effect of finger and thumb positions appears to be much most distinct, indicating that having the thumb slightly abducted and the fingers grouped is a preferable position at y = 180°, whereas at y = 0°, having the thumb adducted and fingers slightly spread yielded higher lift values. Results show that finger and thumb positioning in swimming is a determinant of the propulsive force produced during swimming; indeed, this force is dependent on the direction of the flow over the hand and forearm, which changes across the arm’s stroke.
Mark L. Latash, Fan Gao and Vladimir M. Zatsiorsky
The method of multidimensional scaling was applied to matrices of finger interaction (IFM) computed for individual participants for finger force production tasks. When IFMs for young controls, elderly, and persons with Down syndrome were pooled, only two dimensions described interpersonal differences; these were related to total force and to the total amount of enslaving. When IFMs for each group were analyzed separately, subpopulation-specific dimensions were found. Potentially, this analysis can be applied to discover meaningful dimensions that reflect differences in indices of finger interaction across and within subpopulations which differ in their apparent ability to use the hand. It may also be useful for tracking changes in finger interaction that occur in the process of specialized training or motor rehabilitation.
Akinori Nagano, Shinsuke Yoshioka, Dean Charles Hay and Senshi Fukashiro
The purpose of this study was to test whether a light finger touch on one’s own body (upper legs) reduces postural sway. Ten healthy males participated. In the first part of the study, the participants stood upright with their eyes closed on a force platform while ground reaction force data were collected. Two conditions differing in the placement of the arms and fingers were tested. In the no-touch condition, the participants kept their hands in loose fists. In the finger-touch condition, the participants lightly touched the lateral sides of the upper legs with all fingers. Postural sway measures were calculated from the ground reaction force data. In the second part of the study, the participants stood upright on a pneumatic balance disk while ground reaction force data were collected. Experimental and measurement protocols were identical to those used in the first part of the study. The results showed that light finger touch on the upper legs significantly reduced postural sway on the balance disk up to ~7%. The data from this study suggest that decreased postural sway due to finger contact may improve balance control during other standing tasks.