A Framework for Opportunistic Context and Activity Recognition

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

A Framework for Opportunistic Context and Activity Recognition

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

Institute of Pervasive Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

Abstract

Opportunistic activity and context recognition systems draw from the characteristics to use sensing devices that just happen to be available rather than pre-defining a fixed sensor infrastructure at design time. Opportunistic sensing offers the possibility to obtain data from sensors that just happen to be available in the area surrounding the user. This enables users/or applications to state recognition goals, saying what has to be sensed for, at runtime to the system. The available sensing devices that can contribute to the recognition goal are configured to an ensemble, the best set of sensors for the recognition goal. This paper describes the OPPORTUNITY Framework and shows its functionality with respect to four features (goal querying and sensor configuration, sensor appears/disappears, sensor modifies data and sensor learns from other sensor) to show the dynamic nature of an opportunistic system as the available sensing infrastructure is not fixed and changes during runtime.

Original language	English
Title of host publication	9th International Conference on Pervasive Computing (Pervasive2011)
Number of pages	4
Publication status	Published - Jun 2011

Fields of science

102 Computer Sciences
102001 Artificial intelligence
102018 Artificial neural networks

JKU Focus areas

Engineering and Natural Sciences (in general)

Cite this

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title = "A Framework for Opportunistic Context and Activity Recognition",

abstract = "Opportunistic activity and context recognition systems draw from the characteristics to use sensing devices that just happen to be available rather than pre-defining a fixed sensor infrastructure at design time. Opportunistic sensing offers the possibility to obtain data from sensors that just happen to be available in the area surrounding the user. This enables users/or applications to state recognition goals, saying what has to be sensed for, at runtime to the system. The available sensing devices that can contribute to the recognition goal are configured to an ensemble, the best set of sensors for the recognition goal. This paper describes the OPPORTUNITY Framework and shows its functionality with respect to four features (goal querying and sensor configuration, sensor appears/disappears, sensor modifies data and sensor learns from other sensor) to show the dynamic nature of an opportunistic system as the available sensing infrastructure is not fixed and changes during runtime.",

author = "Gerold H{\"o}lzl and Marc Kurz and Alois Ferscha and Daniel Roggen and Alberto Calatroni and Gerhard Tr{\"o}ster and Ricardo Chavarriaga and Jose Millan and Hesam Sagha and Paul Lukowicz and David Bannach",

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T1 - A Framework for Opportunistic Context and Activity Recognition

AU - Hölzl, Gerold

AU - Kurz, Marc

AU - Ferscha, Alois

AU - Roggen, Daniel

AU - Calatroni, Alberto

AU - Tröster, Gerhard

AU - Chavarriaga, Ricardo

AU - Millan, Jose

AU - Sagha, Hesam

AU - Lukowicz, Paul

AU - Bannach, David

PY - 2011/6

Y1 - 2011/6

N2 - Opportunistic activity and context recognition systems draw from the characteristics to use sensing devices that just happen to be available rather than pre-defining a fixed sensor infrastructure at design time. Opportunistic sensing offers the possibility to obtain data from sensors that just happen to be available in the area surrounding the user. This enables users/or applications to state recognition goals, saying what has to be sensed for, at runtime to the system. The available sensing devices that can contribute to the recognition goal are configured to an ensemble, the best set of sensors for the recognition goal. This paper describes the OPPORTUNITY Framework and shows its functionality with respect to four features (goal querying and sensor configuration, sensor appears/disappears, sensor modifies data and sensor learns from other sensor) to show the dynamic nature of an opportunistic system as the available sensing infrastructure is not fixed and changes during runtime.

AB - Opportunistic activity and context recognition systems draw from the characteristics to use sensing devices that just happen to be available rather than pre-defining a fixed sensor infrastructure at design time. Opportunistic sensing offers the possibility to obtain data from sensors that just happen to be available in the area surrounding the user. This enables users/or applications to state recognition goals, saying what has to be sensed for, at runtime to the system. The available sensing devices that can contribute to the recognition goal are configured to an ensemble, the best set of sensors for the recognition goal. This paper describes the OPPORTUNITY Framework and shows its functionality with respect to four features (goal querying and sensor configuration, sensor appears/disappears, sensor modifies data and sensor learns from other sensor) to show the dynamic nature of an opportunistic system as the available sensing infrastructure is not fixed and changes during runtime.

M3 - Conference proceedings

BT - 9th International Conference on Pervasive Computing (Pervasive2011)

ER -