Pattern recognition strategies have been explored for prosthetic hand control for
years, but have not been commercially implemented.  Articulated research hands
human hand articulation, presenting new control challenges. Because no methods of
cessing signals of any source (cortical, peripheral nerve or myoelectric) are currently
of delivering joint angle intent, the information content of user intent must be expanded.
Control information for the 15 to 20 joints of a highly articulated hand must be derived
a single variable of grasp information. Two strategies are proposed for augmenting
control these hands. First, an array of grasp endpoints composed of the joint angles
is created for each grasp, and the linear path to the desired endpoint constantly
Finger collisions are supervised by the user. Second, a larger set of grasp control
are explored. Using additional parameters allows more precise control of the arrangement
the ngers than whole hand grasps, a greater degree of intuitive control over the
of the ngers, and the possibility of dextrous manipulation.
Proceedings of the MEC’08 conference, UNB; 2008.
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