About Opportunity

Methodologies are missing to design context-aware systems that

• work over long periods of time despite changes in sensing infrastructure (sensor failures, degradation);
• provide the freedom to users to change wearable device placement;
• 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. We 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.