Opportunity
Activity and Context Recognition with Opportunistic Sensor Configurations
| Coordinatore | EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZURICH
Organization address
address: Gloriastrasse 35 contact info |
| Nazionalità Coordinatore | Switzerland [CH] |
| Totale costo | 1˙987˙456 € |
| EC contributo | 1˙508˙768 € |
| Programma | FP7-ICT
Specific Programme "Cooperation": Information and communication technologies |
| Code Call | FP7-ICT-2007-C |
| Funding Scheme | CP |
| Anno di inizio | 2009 |
| Periodo (anno-mese-giorno) | 2009-02-01 - 2012-07-31 |
Partecipanti
| # | ||||
|---|---|---|---|---|
| 1 | EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZURICH | CH | coordinator | 0.00 |
| 2 |
Nome Ente NON disponibile
Organization address
address: Innstrasse contact info |
DE (PASSAU) | participant | 0.00 |
| 3 |
ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE
Organization address
address: BATIMENT CE 3316 STATION 1 contact info |
CH (LAUSANNE) | participant | 0.00 |
| 4 |
UNIVERSITAET LINZ
Organization address
address: ALTENBERGERSTRASSE 69 contact info |
AT (LINZ) | participant | 0.00 |
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Obiettivo del progetto (Objective)
OPPORTUNITY picks up on the very essential methodological underpinnings of any Ambient Intelligence (AmI) scenario: recognizing (and understanding) context and activity.nMethodologies 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.nWe 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 makes 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.nFour 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.nThe methods are demonstrated in complex opportunistic activity recognition scenarios, and on robust opportunistic EEG-based BCI systems.