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Multi-Level Sensory Interpretation and Adaptation in a Mobile Cube

Research output: Contribution to conference - Without ISBN/ISSN Conference paperpeer-review

Published

Standard

Multi-Level Sensory Interpretation and Adaptation in a Mobile Cube. / Van Laerhoven, Kristof; Villar, Nicolas; Gellersen, Hans.
2003. 111-117 Paper presented at Proc. of the third workshop on Artificial Intelligence in Mobile Systems (AIMS) at Ubicomp 2003, Seattle, WA, US.

Research output: Contribution to conference - Without ISBN/ISSN Conference paperpeer-review

Harvard

Van Laerhoven, K, Villar, N & Gellersen, H 2003, 'Multi-Level Sensory Interpretation and Adaptation in a Mobile Cube', Paper presented at Proc. of the third workshop on Artificial Intelligence in Mobile Systems (AIMS) at Ubicomp 2003, Seattle, WA, US, 1/01/00 pp. 111-117.

APA

Van Laerhoven, K., Villar, N., & Gellersen, H. (2003). Multi-Level Sensory Interpretation and Adaptation in a Mobile Cube. 111-117. Paper presented at Proc. of the third workshop on Artificial Intelligence in Mobile Systems (AIMS) at Ubicomp 2003, Seattle, WA, US.

Vancouver

Van Laerhoven K, Villar N, Gellersen H. Multi-Level Sensory Interpretation and Adaptation in a Mobile Cube. 2003. Paper presented at Proc. of the third workshop on Artificial Intelligence in Mobile Systems (AIMS) at Ubicomp 2003, Seattle, WA, US.

Author

Van Laerhoven, Kristof ; Villar, Nicolas ; Gellersen, Hans. / Multi-Level Sensory Interpretation and Adaptation in a Mobile Cube. Paper presented at Proc. of the third workshop on Artificial Intelligence in Mobile Systems (AIMS) at Ubicomp 2003, Seattle, WA, US.7 p.

Bibtex

@conference{2f940afeeffd48379f707d908cb57b23,
title = "Multi-Level Sensory Interpretation and Adaptation in a Mobile Cube",
abstract = "Signals from sensors are often analyzed in a sequence of steps, starting with the raw sensor data and eventually ending up with a classification or abstraction of these data. This paper will give a practical example of how the same information can be trained and used to initiate multiple interpretations of the same data on different, application-oriented levels. Crucially, the focus is on expanding embedded analysis software, rather than adding more powerful, but possibly resource-hungry, sensors. Our illustration of this approach involves a tangible input device the shape of a cube that relies exclusively on lowcost accelerometers. The cube supports calibration with user supervision, it can tell which of its sides is on top, give an estimate of its orientation relative to the user, and recognize basic gestures.",
keywords = "cs_eprint_id, 1532 cs_uid, 382",
author = "{Van Laerhoven}, Kristof and Nicolas Villar and Hans Gellersen",
year = "2003",
language = "English",
pages = "111--117",
note = "Proc. of the third workshop on Artificial Intelligence in Mobile Systems (AIMS) at Ubicomp 2003 ; Conference date: 01-01-1900",

}

RIS

TY - CONF

T1 - Multi-Level Sensory Interpretation and Adaptation in a Mobile Cube

AU - Van Laerhoven, Kristof

AU - Villar, Nicolas

AU - Gellersen, Hans

PY - 2003

Y1 - 2003

N2 - Signals from sensors are often analyzed in a sequence of steps, starting with the raw sensor data and eventually ending up with a classification or abstraction of these data. This paper will give a practical example of how the same information can be trained and used to initiate multiple interpretations of the same data on different, application-oriented levels. Crucially, the focus is on expanding embedded analysis software, rather than adding more powerful, but possibly resource-hungry, sensors. Our illustration of this approach involves a tangible input device the shape of a cube that relies exclusively on lowcost accelerometers. The cube supports calibration with user supervision, it can tell which of its sides is on top, give an estimate of its orientation relative to the user, and recognize basic gestures.

AB - Signals from sensors are often analyzed in a sequence of steps, starting with the raw sensor data and eventually ending up with a classification or abstraction of these data. This paper will give a practical example of how the same information can be trained and used to initiate multiple interpretations of the same data on different, application-oriented levels. Crucially, the focus is on expanding embedded analysis software, rather than adding more powerful, but possibly resource-hungry, sensors. Our illustration of this approach involves a tangible input device the shape of a cube that relies exclusively on lowcost accelerometers. The cube supports calibration with user supervision, it can tell which of its sides is on top, give an estimate of its orientation relative to the user, and recognize basic gestures.

KW - cs_eprint_id

KW - 1532 cs_uid

KW - 382

M3 - Conference paper

SP - 111

EP - 117

T2 - Proc. of the third workshop on Artificial Intelligence in Mobile Systems (AIMS) at Ubicomp 2003

Y2 - 1 January 1900

ER -