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Hand tracking was achieved using a novel variation
of a seed point-based CAMSHIFT tracking method
which utilises our segmentation results. The hand
motion was estimated using a simple finger tip esti-
mation based on the Douglas-Peucker algorithm and
a convexity criterion. User testing showed that the
algorithm provides stable tracking and that hand mo-
tion can usually be successfully reconstructed using
the finger tip (knuckle) estimate.
We have constructed a hand model based on an
existing modeling platform for human anatomy and
animated it using a skeletal animation framework
using predefined high-level atomic motions based
on anatomical and physiological constraints. The
hand model proved suitable both to demonstrate hand
tracking and to represent the hand motion analysis
results. The reconstructed models had slight varia-
tions in finger positions, but the basic exercises were
successfully represented. Initial user testing showed
that the application is easy to use and that it would be
useful for measuring the correctness and repetition of
hand exercises.
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A FRAMEWORK FOR WEBCAM-BASED HAND REHABILITATION EXERCISES
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