33,500 research outputs found
Linking recorded data with emotive and adaptive computing in an eHealth environment
Telecare, and particularly lifestyle monitoring, currently relies on the ability to detect and respond to changes in individual behaviour using data derived from sensors around the home. This means that a significant aspect of behaviour, that of an individuals emotional state, is not accounted for in reaching a conclusion as to the form of response required. The linked concepts of emotive and adaptive computing offer an opportunity to include information about emotional state and the paper considers how current developments in this area have the potential to be integrated within telecare and other areas of eHealth. In doing so, it looks at the development of and current state of the art of both emotive and adaptive computing, including its conceptual background, and places them into an overall eHealth context for application and development
A survey on mouth modeling and analysis for Sign Language recognition
© 2015 IEEE.Around 70 million Deaf worldwide use Sign Languages (SLs) as their native languages. At the same time, they have limited reading/writing skills in the spoken language. This puts them at a severe disadvantage in many contexts, including education, work, usage of computers and the Internet. Automatic Sign Language Recognition (ASLR) can support the Deaf in many ways, e.g. by enabling the development of systems for Human-Computer Interaction in SL and translation between sign and spoken language. Research in ASLR usually revolves around automatic understanding of manual signs. Recently, ASLR research community has started to appreciate the importance of non-manuals, since they are related to the lexical meaning of a sign, the syntax and the prosody. Nonmanuals include body and head pose, movement of the eyebrows and the eyes, as well as blinks and squints. Arguably, the mouth is one of the most involved parts of the face in non-manuals. Mouth actions related to ASLR can be either mouthings, i.e. visual syllables with the mouth while signing, or non-verbal mouth gestures. Both are very important in ASLR. In this paper, we present the first survey on mouth non-manuals in ASLR. We start by showing why mouth motion is important in SL and the relevant techniques that exist within ASLR. Since limited research has been conducted regarding automatic analysis of mouth motion in the context of ALSR, we proceed by surveying relevant techniques from the areas of automatic mouth expression and visual speech recognition which can be applied to the task. Finally, we conclude by presenting the challenges and potentials of automatic analysis of mouth motion in the context of ASLR
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