Projects per year
Abstract
Our gestural habits convey a multitude of information of different levels of
granularity that can be exploited for human–computer interaction. Gestures
can provide additional or redundant information accompanying a verbal utterance,
they can have a meaning in themselves, or they can provide the addressee
with subtle clues about personality or cultural background. Gestures are an
extremly rich source of communication-specific and contextual information
for interactions in ambient intelligence environments. This chapter reviews
the semantic layers of gestural interaction, focusing on the layer beyond communicative intent, and presents interface techniques to capture and analyze
gestural input, taking into account nonstandard approaches such as acceleration
analysis and the use of physiological sensors.
granularity that can be exploited for human–computer interaction. Gestures
can provide additional or redundant information accompanying a verbal utterance,
they can have a meaning in themselves, or they can provide the addressee
with subtle clues about personality or cultural background. Gestures are an
extremly rich source of communication-specific and contextual information
for interactions in ambient intelligence environments. This chapter reviews
the semantic layers of gestural interaction, focusing on the layer beyond communicative intent, and presents interface techniques to capture and analyze
gestural input, taking into account nonstandard approaches such as acceleration
analysis and the use of physiological sensors.
| Original language | English |
|---|---|
| Title of host publication | Human-Centric Interfaces for Ambient Intelligence |
| Editors | Hamid Aghajan, Ramón López-Cózar Delgado, Juan Carlos Augusto |
| Number of pages | 0 |
| Publisher | Academic Press |
| Publication date | 2010 |
| Pages | 327-345 |
| Chapter | 13 |
| ISBN (Print) | 978-0-12-374708-2 |
| Publication status | Published - 2010 |
Keywords
- Gesture Recognition
- Emotion
- Personality
- Culture
Fingerprint
Dive into the research topics of 'Nonsymbolic Gestural Interaction for Ambient Intelligence'. Together they form a unique fingerprint.Projects
- 3 Finished
-
ASLERD: Association for Smart Learning Ecosystems and Regional Development
Rehm, M. (PI)
01/09/2015 → 31/12/2021
Project: Research
-
CUBE-G: CUlture-adaptive BEhavior Generation
Rehm, M. (Project Participant)
02/11/2006 → 26/02/2010
Project: Research
-
CALLAS: Conveying Affectiveness in Leading-edge Living Adaptive Systems
Rehm, M. (Project Participant)
01/11/2006 → 30/04/2010
Project: Research