Comparison of Interfinger Connection Matrix Computation Techniques

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Joel R. Martin Pennsylvania State University

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Alexander V. Terekhov Université Pierre et Marie Curie

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Mark L. Latash Pennsylvania State University

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Vladimir M. Zatsiorsky Pennsylvania State University

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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 (Corresponding Author), Mark L. Latash, and Vladimir M. Zatsiorsky are with the Department of Kinesiology, Pennsylvania State University, University Park, PA. Alexander V. Terekhov is with the Université Pierre et Marie Curie, Paris, France.

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