| dc.description.abstract |
Pattern recognition strategies have been explored for prosthetic hand control for
over 20
years, but have not been commercially implemented. [1] Articulated research hands
now rival
human hand articulation, presenting new control challenges. Because no methods of
pro-
cessing signals of any source (cortical, peripheral nerve or myoelectric) are currently
capable
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
from
a single variable of grasp information. Two strategies are proposed for augmenting
intent to
control these hands. First, an array of grasp endpoints composed of the joint angles
desired
is created for each grasp, and the linear path to the desired endpoint constantly
updated.
Finger collisions are supervised by the user. Second, a larger set of grasp control
parameters
are explored. Using additional parameters allows more precise control of the arrangement
of
the ngers than whole hand grasps, a greater degree of intuitive control over the
arrangement
of the ngers, and the possibility of dextrous manipulation.
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