Coordinated force production in multi-finger tasks: finger interaction and neural network modeling |
| |
Authors: | Vladimir M Zatsiorsky Zong-Ming Li Mark L Latash |
| |
Institution: | (1) Biomechanics Laboratory, Department of Kinesiology, The Pennsylvania State University, University Park, PA 16802, USA, US |
| |
Abstract: | During maximal voluntary contraction (MVC) with several fingers, the following three phenomena are observed: (1) the total
force produced by all the involved fingers is shared among the fingers in a specific manner (sharing); (2) the force produced by a given finger in a multi-finger task is smaller than the force generated by this finger in a
single-finger task (force deficit); (3) the fingers that are not required to produce any force by instruction are involuntary activated (enslaving). We studied involuntary force production by individual fingers (enslaving effects, EE) during tasks when (an)other finger(s) of the hand generated maximal voluntary pressing force in isometric conditions.
The subjects (n = 10) were instructed to press as hard as possible on the force sensors with one, two, three and four fingers acting in parallel
in all possible combinations. The EE were (A) large, the slave fingers always producing a force ranging from 10.9% to 54.7%
of the maximal force produced by the finger in the single-finger task; (B) nearly symmetrical; (C) larger for the neighboring
fingers; and (D) non-additive. In most cases, the EE from two or three fingers were smaller than the EE from at least one
finger (this phenomenon was coined occlusion). The occlusion cannot be explained only by anatomical musculo-tendinous connections. Therefore, neural factors contribute
substantially to the EE. A neural network model that accounts for all the three effects has been developed. The model consists
of three layers: the input layer that models a central neural drive; the hidden layer modeling transformation of the central
drive into an input signal to the muscles serving several fingers simultaneously (multi-digit muscles); and the output layer
representing finger force output. The output of the hidden layer is set inversely proportional to the number of fingers involved.
In addition, direct connections between the input and output layers represent signals to the hand muscles serving individual
fingers (uni-digit muscles). The network was validated using three different training sets. Single digit muscles contributed
from 25% to 50% of the total finger force. The master matrix and the enslaving matrix were computed; they characterize the
ability of a given finger to enslave other fingers and its ability to be enslaved. Overall, the neural network modeling suggests
that no direct correspondence exists between neural command to an individual finger and finger force. To produce a desired
finger force, a command sent to an intended finger should be scaled in accordance with the commands sent to the other fingers.
Received: 17 October 1997 / Accepted in revised form: 12 May 1998 |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|