A hypothesis was proposed that the central nervous system controls force production by the fingers through hypothetical neural commands. The neural commands are scaled between values of 0 to 1, indicating no intentional force production or maximal voluntary contraction (MVC) force production, respectively. A matrix of interfinger connections transforms neural commands into finger forces. Two methods have been proposed to compute the interfinger connection matrix. The first method uses only single finger MVC trials and multiplies the interfinger connection matrix by a gain factor. The second method uses a neural network model based on experimental data. The performance of the two methods was compared on the MVC data and on a data set of submaximal forces, collected over a range of total forces and moments of force. The methods were compared in terms of (1) ability to predict finger forces, (2) accuracy of neural command reconstruction, and (3) preserved planarity of force data for submaximal force production task. Both methods did a reasonable job of predicting the total force in multifinger MVC trials; however, the neural network model performed better in regards to all other criteria. Overall, the results indicate that for modeling multifinger interaction the neural network method is preferable.
Joel R. Martin, Alexander V. Terekhov, Mark L. Latash and Vladimir M. Zatsiorsky
Xun Niu, Alexander V. Terekhov, Mark L. Latash and Vladimir M. Zatsiorsky
The goal of the research is to reconstruct the unknown cost (objective) function(s) presumably used by the neural controller for sharing the total force among individual fingers in multifinger prehension. The cost function was determined from experimental data by applying the recently developed Analytical Inverse Optimization (ANIO) method (Terekhov et al. 2010). The core of the ANIO method is the Theorem of Uniqueness that specifies conditions for unique (with some restrictions) estimation of the objective functions. In the experiment, subjects (n = 8) grasped an instrumented handle and maintained it at rest in the air with various external torques, loads, and target grasping forces applied to the object. The experimental data recorded from 80 trials showed a tendency to lie on a 2-dimensional hyperplane in the 4-dimensional finger-force space. Because the constraints in each trial were different, such a propensity is a manifestation of a neural mechanism (not the task mechanics). In agreement with the Lagrange principle for the inverse optimization, the plane of experimental observations was close to the plane resulting from the direct optimization. The latter plane was determined using the ANIO method. The unknown cost function was reconstructed successfully for each performer, as well as for the group data. The cost functions were found to be quadratic with nonzero linear terms. The cost functions obtained with the ANIO method yielded more accurate results than other optimization methods. The ANIO method has an evident potential for addressing the problem of optimization in motor control.