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Joel R. Martin, Mark L. Latash and Vladimir M. Zatsiorsky

This study investigated the effects of modifying contact finger forces in one direction—normal or tangential—on the entire set of the contact forces, while statically holding an object. Subjects grasped a handle instrumented with finger force-moment sensors, maintained it at rest in the air, and then slowly: (1) increased the grasping force, (2) tried to spread fingers apart, and (3) tried to squeeze fingers together. Analysis was mostly performed at the virtual finger (VF) level (the VF is an imaginable finger that generates the same force and moment as the four fingers combined). For all three tasks there were statistically significant changes in the VF normal and tangential forces. For finger spreading/squeezing the tangential force neutral point was located between the index and middle fingers. We conclude that the internal forces are regulated as a whole, including adjustments in both normal and tangential force, instead of only a subset of forces (normal or tangential). The effects of such factors as EFFORT and TORQUE were additive; their interaction was not statistically significant, thus supporting the principle of superposition in human prehension.

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Joel R. Martin, Alexander V. Terekhov, Mark L. Latash and Vladimir M. Zatsiorsky

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.