<mods:mods version="3.0" xsi:schemaLocation="http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-0.xsd" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:mods="http://www.loc.gov/mods/v3"><mods:titleInfo><mods:title>Recognition and anticipation of hand motions using a recurrent neural network</mods:title></mods:titleInfo><mods:name type="personal"><mods:namePart type="given">P</mods:namePart><mods:namePart type="family">Vamplew</mods:namePart><mods:role><mods:roleTerm type="text">author</mods:roleTerm></mods:role></mods:name><mods:name type="personal"><mods:namePart type="given">A</mods:namePart><mods:namePart type="family">Adams</mods:namePart><mods:role><mods:roleTerm type="text">author</mods:roleTerm></mods:role></mods:name><mods:abstract>Previous work in recognition of hand gestures has concentrated on classification of hand shapes, with relatively little work done on hand motions. This paper describes a recurrent neural network which has been trained to classify sixteen different hand trajectories, including relatively complex paths such as circles and back-and-forth motions. The network's ability to anticipate the classification of an incomplete gesture is also examined, and its implications for segmentation of gestures is discussed.</mods:abstract><mods:classification authority="lcc">280200 Artificial Intelligence and Signal and Image Processing</mods:classification><mods:originInfo><mods:dateIssued encoding="iso8061">1995</mods:dateIssued></mods:originInfo><mods:genre>Conference or Workshop Item</mods:genre></mods:mods>