Dynamic Quantification of Activity Recognition Capabilities in Opportunistic Systems

Marc Kurz, Gerold Hölzl, Alois Ferscha, Hesam Sagha, Ricardo Chavarriaga, Jose Millan

Publikation: Beitrag in Buch/Bericht/TagungsbandKonferenzbeitragBegutachtung

3 Zitate (Scopus)


Opportunistic activity and context recognition systems draw from the characteristic to use sensing devices that just happen to be available instead of pre-defining them at the design time of the system in order to achieve a recognition goal at runtime. Whenever a user and/or application states a recognition goal at runtime to the system, the available sensing devices configure an ensemble of the best available set of sensors for the specified recognition goal. This paper presents an approach to show how machine learning technologies (classification, fusion and anomaly detection) are integrated in a prototypical opportunistic activity and context recognition system (referred to as the OPPORTUNITY Framework). We define a metric that quantifies the ensemble's capabilities according to a recognition goal and evaluate the approach with respect to the requirements of an opportunistic system (e.g. to compute an ensemble's configuration and reconfiguration at runtime).

Titel2011 IEEE 73rd Vehicular Technology Conference, VTC2011-Spring - Proceedings
Herausgeber (Verlag)IEEE
ISBN (Print)9781424483310
PublikationsstatusVeröffentlicht - 2011
VeranstaltungVehicular Technology Conference (VTC Spring), 2011 IEEE 73rd - Budapest, Ungarn
Dauer: 15 Mai 201118 Mai 2011


NameIEEE Vehicular Technology Conference
ISSN (Print)1550-2252


KonferenzVehicular Technology Conference (VTC Spring), 2011 IEEE 73rd


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