Opportunity

Activity and Context Recognition with Opportunistic Sensor Configurations

Coordinatore	EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZURICH    more  Organization address address: Gloriastrasse 35 postcode: 8092 contact info Titolo: Dr. Nome: Roggen Cognome: Daniel Email: send email Telefono: 41446322993 Fax: 41446321210
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

#	participant	country	role	EC contrib. [€]
1	EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZURICH	CH	coordinator	0.00
2	Nome Ente NON disponibile  Organization address address: Innstrasse city: PASSAU postcode: 94032 contact info Titolo: Prof. Nome: Paul Cognome: Lukowicz Email: send email Telefono: +49 851 509 3080 Fax: +49 851 509 3082	DE (PASSAU)	participant	0.00
3	ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE  Organization address address: BATIMENT CE 3316 STATION 1 city: LAUSANNE postcode: 1015 contact info Titolo: Prof. Nome: José del R. Cognome: Millán Email: send email Telefono: +41 21 6937795 Fax: +41 21 6936930	CH (LAUSANNE)	participant	0.00
4	UNIVERSITAET LINZ  Organization address address: ALTENBERGERSTRASSE 69 city: LINZ postcode: 4040 contact info Titolo: Prof. Nome: Alois Cognome: Ferscha Email: send email Telefono: 4369910000000 Fax: 4373220000000	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.