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Towards reliable multimodal sensing in aware environments

Published: 15 November 2001 Publication History

Abstract

A prototype system for implementing a reliable sensor network for large scale smart environments is presented. Most applications within any form of smart environments (rooms, offices, homes, etc.) are dependent on reliable who, where, when, and what information of its inhabitants (users). This information can be inferred from different sensors spread throughout the space. However, isolated sensing technologies provide limited information under the varying, dynamic, and long-term scenarios (24/7), that are inherent in applications for intelligent environments. In this paper, we present a prototype system that provides an infrastructure for leveraging the strengths of different sensors and processes used for the interpretation of their collective data. We describe the needs of such systems, propose an architecture to dealwith such multi-modal fusion, and discuss the initial set of sensors and processes used to address such needs.

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cover image ACM Other conferences
PUI '01: Proceedings of the 2001 workshop on Perceptive user interfaces
November 2001
241 pages
ISBN:9781450374736
DOI:10.1145/971478
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 November 2001

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PUI01: Workshop on Perceptive User Interfaces
November 15 - 16, 2001
Florida, Orlando, USA

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  • (2016)ABROA: Audio-based room-occupancy analysis using Gaussian mixtures and Hidden Markov models2016 Future Technologies Conference (FTC)10.1109/FTC.2016.7821763(1270-1273)Online publication date: Dec-2016
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  • (2009)Performance analysis for automated gait extraction and recognition in multi-camera surveillanceMultimedia Tools and Applications10.1007/s11042-009-0378-550:1(75-94)Online publication date: 2-Oct-2009
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