Ej. Holden et al., HAND MOVEMENT CLASSIFICATION USING AN ADAPTIVE FUZZY EXPERT-SYSTEM, International journal of expert systems, 9(4), 1996, pp. 465-480
Citations number
14
Categorie Soggetti
Computer Sciences, Special Topics","Computer Science Artificial Intelligence
Hand sign recognition, in general, may be divided into two stages: the
motion sensing, which extracts useful movement data from the signer's
motion; and the classification process, which classifies the movement
data as a sign. We have developed a prototype of the Hand Sign Classi
fication (HSC) system that classifies a series of the full degrees-of-
freedom kinematic data of a hand into sign language signs. It is built
as a fuzzy expert system in which the sign knowledge can be represent
ed by high level imprecise descriptions. Applying fuzzy logic also pro
vides the system with the ability to produce a confidence level for an
output. The HSC system has an adaptive engine that trains the system
to handle variations in the movement data, or to adapt to differences
amongst signers. Adaptive fuzzy systems are often compared with neural
networks in their adaptability, but unlike neural networks, expert kn
owledge can be imposed onto the system in the form of rules.