Layered architecture for real time sign recognition: Hand gesture and movement
Sign and gesture recognition offers a natural way for humancomputer interaction. This paper presents a real time sign recognition architecture including both gesture and movement recognition. Among the different technologies available for sign recognition data gloves and accelerometers were chosen for the purposes of this research. Due to the real time nature of the problem, the proposed approach works in two different tiers, the segmentation tier and the classification tier. In the first stage the glove and accelerometer signals are processed for segmentation purposes, separating the different signs performed by the system user. In the second stage the values received from the segmentation tier are classified. In an effort to emphasize the real use of the architecture, this approach deals specially with problems like sensor noise and simplification of the training phase.
sign recognition movement recognition machine learning signal processing human machine interface