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The OPPORTUNITY Framework and Data Processing Ecosystem for Opportunistic Activity and Context Recognition

  • Marc Kurz
  • , Gerold Hölzl
  • , Alois Ferscha
  • , Alberto Calatroni
  • , Daniel Roggen
  • , Gerhard Tröster
  • , Hesam Sagha
  • , Ricardo Chavarriaga
  • , Jose Millan
  • , David Bannach
  • , Kai Kunze
  • , Paul Lukowicz

Research output: Contribution to journalArticlepeer-review

Abstract

Opportunistic sensing can be used to obtain data from the sensors that just happen to be present in the user's surrounding. By harnessing these opportunistic sensor configurations to infer activity or context, ambient intelligence environments become more robust, have improved user comfort thanks to reduced requirements on body-worn sensor deployment, and they are not limited to a conscribed location defined by sensors specifically deployed for an application We present the OPPORTUNITY Framework and Data Processing Ecosystem to recognize human activities or contexts in such opportunistic sensor configurations. It addresses the challenge of inferring human activities with limited guarantees about placement, nature and run-time availability of sensors. We realize this by a combination of: (i) a sensing/context framework capable of coordinating sensor recruitment according to a high level recognition goal, (ii) the corresponding dynamic instanciation of data processing elements to infer activities, (iii) a tight interaction between both in an "ecosystem" allowing to autonomously discover novel knowledge about sensor characteristics that is re-usable in subsequent recognition queries. This allows the system to operate in open-ended environments. We demonstrate OPPORTUNITY on a large scale dataset collected to exhibit the sensor-rich characteristics of opportunistic sensing systems. The dataset comprises 25 hours of activities of daily living, collected from 12 subjects. It contains the data of 72 sensors of 10 modalities and part 15 networked sensor systems deployed in objects, on body and in the environment. We show the mapping from a recognition goal to an instanciation of the recognition system. We show the knowledge acquisition and reuse of the autonomously discovered semantic meaning of an unknown new sensor, the autonomous update of the trust indicator of a sensor due to unforeseen deteriorations, and the autonomous discovery of the on-body sensor placement.
Original languageEnglish
Number of pages29
JournalInternational Journal of Sensors, Wireless Communications and Control
Publication statusPublished - Jun 2011

Fields of science

  • 102 Computer Sciences
  • 102001 Artificial intelligence
  • 102018 Artificial neural networks

JKU Focus areas

  • Engineering and Natural Sciences (in general)

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