000164702 001__ 164702
000164702 005__ 20190316235054.0
000164702 037__ $$aARTICLE
000164702 245__ $$aThe OPPORTUNITY Framework and Data Processing Ecosystem for Opportunistic Activity and Context Recognition
000164702 269__ $$a2012
000164702 260__ $$c2012
000164702 336__ $$aJournal Articles
000164702 520__ $$aOpportunistic sensing can be used to obtain data from sensors that just happen to be present in the user’s surroundings. 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 predefined and restricted 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 instantiation of data processing elements to infer activities, (iii) a tight interaction between the last two elements in an “ecosystem” allowing to autonomously discover novel knowledge about sensor characteristics that is reusable 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 richness and related characteristics, typical of opportunistic sensing systems. The dataset comprises 25 hours of activities of daily living, collected from 12 subjects. It contains data of 72 sensors covering 10 modalities and 15 networked sensor systems deployed in objects, on the body and in the environment. We show the mapping from a recognition goal to an instantiation of the recognition system. We also show the knowledge acquisition and reuse of the autonomously discovered semantic meaning of a new unknown 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.
000164702 6531_ $$a[Opportunity]
000164702 700__ $$aKurz, Mark
000164702 700__ $$aGerold, Hölzl
000164702 700__ $$aFerscha, Alois
000164702 700__ $$aCalatroni, Alberto
000164702 700__ $$aRoggen, Daniel
000164702 700__ $$aTröster, Gerhard
000164702 700__ $$0242182$$g191533$$aSagha, Hesam
000164702 700__ $$0241256$$g137762$$aChavarriaga, Ricardo
000164702 700__ $$0240030$$g149175$$aMillán, José del R.
000164702 700__ $$aBannach, David
000164702 700__ $$aKunze, Kai
000164702 700__ $$aLukowicz, Paul
000164702 773__ $$j1$$tInternational Journal of Sensors, Wireless Communications and Control$$k2$$q102-125
000164702 8564_ $$uhttp://www.benthamscience.com/swcc/index.htm$$zURL
000164702 8564_ $$uhttps://infoscience.epfl.ch/record/164702/files/KurzHFeCaRoTrSaCh11.pdf$$zn/a$$s3930551$$yn/a
000164702 909C0 $$xU12103$$0252018$$pCNBI
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000164702 909CO $$qGLOBAL_SET$$pSTI$$particle$$ooai:infoscience.tind.io:164702
000164702 917Z8 $$x191533
000164702 917Z8 $$x137762
000164702 917Z8 $$x137762
000164702 917Z8 $$x137762
000164702 917Z8 $$x137762
000164702 937__ $$aEPFL-ARTICLE-164702
000164702 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000164702 980__ $$aARTICLE