Dynamic Quantification of Activity Recognition Capabilities in Opportunistic Systems

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

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

2 Citations (Scopus)

Abstract

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).

Original languageEnglish
Title of host publication2011 IEEE 73rd Vehicular Technology Conference, VTC2011-Spring - Proceedings
PublisherIEEE
ISBN (Print)9781424483310
DOIs
Publication statusPublished - 2011
EventVehicular Technology Conference (VTC Spring), 2011 IEEE 73rd - Budapest, Hungary
Duration: 15 May 201118 May 2011

Publication series

NameIEEE Vehicular Technology Conference
ISSN (Print)1550-2252

Conference

ConferenceVehicular Technology Conference (VTC Spring), 2011 IEEE 73rd
CountryHungary
CityBudapest
Period15.05.201118.05.2011

Keywords

  • Activity and context recognition
  • Opportunistic sensing
  • Sensor networks

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