Hand movement classification using an adaptive fuzzy expert system

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 Classification (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 represented by high level imprecise descriptions. Applying fuzzy logic also provides 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 knowledge can be imposed onto the system in the form of rules.