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Institute of Pervasive Computing
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Opportunity.

Activity and context recognition with opportunistic sensor configurations.

Funding EU FP7-ICT-2007.8.0, CP; additional funding: bm:wf    
Project no. 225938    
Duration 2009-2012
Consortium Eidgenössische Technische Hochschule Zürich*, Johannes Kepler Universität Linz, Ecole Polytechnique Fédérale de Lausanne, Universität Passau
Role Proposer, Area Leader, Project Partner
Opportunity Poster

OPPORTUNITY picks up on the very essential methodological underpinnings of any Ambient Intelligence (AmI) scenario: recognizing (and understanding) context and activity.

Methodologies are missing to design context-aware systems: (1) working over long periods of time despite changes in sensing infrastructure (sensor failures, degradation); (2) providing the freedom to users to change wearable device placement; (3) that can be deployed without user-specific training. This limits the real-world deployment of AmI systems.

We develop opportunistic systems that recognize complex activities/contexts despite the absence of static assumptions about sensor availability and characteristics. They are based on goal-oriented sensor assemblies spontaneously arising and self-organizing to achieve a common activity/context recognition goal. They are embodied and situated, relying on self-supervised learning to achieve autonomous operation. They make best use of the available resources, and keep working despite-or improves thanks to-changes in the sensing environment. Changes include e.g., placement, modality, sensor parameters and can occur at runtime.

Four groups contribute to this goal. They develop: (1) intermediate features that reduce the impact of sensor parameter variability and isolate the recognition chain from sensor specificities; (2) classifier and classifier fusion methods suited for opportunistic systems, capable of incorporating new knowledge online, monitoring their own performance, and dynamically selecting most appropriate information sources; (3) unsupervised dynamic adaptation and autonomous evolution principles to cope with short term changes and long term trends in sensor infrastructure, (4) goal-oriented cooperative sensor ensembles to opportunistically collect data about the user and his environment in a scalable way.

The methods are demonstrated in complex opportunistic activity recognition scenarios, and on robust opportunistic EEG-based BCI systems.

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