An evidential fusion approach for activity recognition under uncertainty in ambient intelligence environments

Authors:
Faouzi Sebbak;Abdelghani Chibani;Yacine Amirat;Farid Benhammadi;Aicha Mokhtari
Affiliations:
LISSI, AI, LRIA Laboratory, UPEC, EMP, USTHB;LISSI Laboratry, UPEC, Paris, France;LISSI Laboratry, UPEC, Paris, France;AI Laboratory, EMP, Algiers, Algeria;LRIA Laboratory, USTHB, Algiers, Algeria
Venue:
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Year:
2012

Citing 20
Cited 0

On the justification of Dempster's rule of combination

Artificial Intelligence
Case-based reasoning: foundational issues, methodological variations, and system approaches

AI Communications
A Middleware Infrastructure for Active Spaces

IEEE Pervasive Computing
Reasoning about Uncertain Contexts in Pervasive Computing Environments

IEEE Pervasive Computing
Applying case-based reasoning and multi-agent intelligent system to context-aware comparative shopping

Decision Support Systems
Logic Programming for Context-Aware Pervasive Computing: Language Support, Characterizing Situations, and Integration with the Web

WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
Intelligent Agents Meet the Semantic Web in Smart Spaces

IEEE Internet Computing
Toward an OSGi-Based Infrastructure for Context-Aware Applications

IEEE Pervasive Computing
Loosely Coupling Ontological Reasoning with an Efficient Middleware for Context-awareness

MOBIQUITOUS '05 Proceedings of the The Second Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services
OntoBayes: An Ontology-Driven Uncertainty Model

CIMCA '05 Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-1 (CIMCA-IAWTIC'06) - Volume 01
Accurate activity recognition in a home setting

UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Toward Managing Uncertain Spatial Information for Situational Awareness Applications

IEEE Transactions on Knowledge and Data Engineering
A conceptual framework and a toolkit for supporting the rapid prototyping of context-aware applications

Human-Computer Interaction
An infrastructure approach to context-aware computing

Human-Computer Interaction
Evidential fusion of sensor data for activity recognition in smart homes

Pervasive and Mobile Computing
Context reasoning using extended evidence theory in pervasive computing environments

Future Generation Computer Systems
Developing context-aware pervasive computing applications: Models and approach

Pervasive and Mobile Computing
Context-awareness in user modelling: requirements analysis for a case-based reasoning application

ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
Activity recognition using temporal evidence theory

Journal of Ambient Intelligence and Smart Environments
A semantic context-aware access control framework for secure collaborations in pervasive computing environments

ISWC'06 Proceedings of the 5th international conference on The Semantic Web

Quantified Score

Hi-index	0.00

Visualization

Abstract

In ambient intelligence environments, the information provided by robot's embedded sensors and physical or logical entities may be inaccurate and uncertain. The Dempster-Shafer evidence Theory (DST) gives a mathematical convenient framework for the evidential fusion representation and inference of uncertain information. However, DST yields counterintuitive results in high conflicting ambient intelligence situations. This paper aims to provide a new strategy to manage conflict in activity recognition process in the ambient intelligence applications. It addresses the challenge of uncertainty and proposes an evidential fusion model based on the management of conflicting situation to optimize decision making in activity recognition. The proposed approach gives intuitive interpretation for combining multiple sources in conflicting situations and avoids the problems of using The Dempster-Shafer rule of combination.