We envision opportunistic activity recognition systems. They are goal-oriented sensor assemblies that spontaneously arise and self-organize to achieve a common goal, here activity and context recognition.
The objective of OPPORTUNITY is to develop generic principles, algorithms and system architectures to reliably recognize complex activities and contexts despite the absence of static assumptions about sensor availability and characteristics in opportunistic systems.
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 that
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. We develop:
The methods are demonstrated in complex opportunistic activity recognition scenarios, and on robust opportunistic EEG-based BCI systems.
Do not hesitate to contact the consortium.
We develop opportunistic activity recognition systems: goal-oriented sensor assemblies spontaneously arise and self-organize to achieve a common activity and context recognition. We develop algorithms and architectures underlying context recognition in opportunistic systems.
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