A User-Dependent Easily-Adjusted Static Finger Language Recognition System
  for Handicapped Aphasiacs 
From: Applied Artificial Intelligence - Vol 23 Issue 10
By: Yu-Fen Fu and Cheng-Seen Ho

Unlike sign language, which usually involves large-scale movements to form a
gesture, finger language, suitable for handicapped aphasiacs, is represented
by relatively small-scale hand gestures accessible by a mere change of the
bending manner of a patient's fingers. Therefore, we need a system that can
tackle the specificity of each handicapped aphasiac. We propose a system that
fulfills this requirement by employing a programmable data glove to capture
tiny movement-related finger gestures, an optical signal value-parameterized
function to calculate the finger bending degrees, and an automatic regression
module to extract most adequate finger features for a specific patient. The
selected features are fed into a neural network, which learns to build a
finger language recognition model for the specific patient. Then the system
can be available for use by the specific user. At the time of this writing,
the achieved average success rate was 100% from unbiased field experiments.  

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Contributed by Rich Simpson