Activity Recognition in Opportunistic Sensor Environments

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

doi:10.1016/j.procs.2011.09.003

Activity Recognition in Opportunistic Sensor Environments

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

Research Center Hagenberg

Publikation: Beitrag in Buch/Bericht/Tagungsband › Konferenzbeitrag › Begutachtung

10 Zitate (Scopus)

Abstract

OPPORTUNITY is project under the EU FET-Open funding¹ in which we develop mobile systems to recognize human activity in dynamically varying sensor setups [1,2]. The system autonomously discovers available sensors around the user and self-configures to recognize desired activities. It reconfigures itself as the environment changes, and encompasses principles supporting autonomous operation in open-ended environments.OPPORTUNITYmainstreams ambient intelligence and improves user acceptance by relaxing constraints on body-worn sensor characteristics, and eases the deployment in real-world environments.We summarize key achievements of the project so far. The project outcomes are robust activity recognition systems. This may enable smarter activity-aware energy-management in buildings, and advanced activity-aware health assistants.

Originalsprache	Englisch
Titel	The European Future Technologies Conference and Exhibition (FET11)
Seiten	173-174
Seitenumfang	2
Band	7
DOIs	https://doi.org/10.1016/j.procs.2011.09.003
Publikationsstatus	Veröffentlicht - 2011
Veranstaltung	The European Future Technologies Conference and Exhibition 2011 - Budapest, Ungarn Dauer: 4 Mai 2011 → 6 Mai 2011

Publikationsreihe

Name	Procedia Computer Science
Herausgeber (Verlag)	Elsevier BV

Konferenz

Konferenz	The European Future Technologies Conference and Exhibition 2011
Land/Gebiet	Ungarn
Ort	Budapest
Zeitraum	04.05.2011 → 06.05.2011

UN SDGs

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SDG 3 – Gute Gesundheit und Wohlergehen

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10.1016/j.procs.2011.09.003

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Roggen, D., Calatroni, A., Förster, K., Tröster, G., Lukowicz, P., Bannach, D., Ferscha, A., Kurz, M., Hölzl, G., Sagha, H., Millan, J., & Chavarriaga, R. (2011). Activity Recognition in Opportunistic Sensor Environments. in The European Future Technologies Conference and Exhibition (FET11) (Band 7, S. 173-174). (Procedia Computer Science). https://doi.org/10.1016/j.procs.2011.09.003

@inproceedings{af6a7521cd3b423d9ed32f22afc50422,

title = "Activity Recognition in Opportunistic Sensor Environments",

abstract = "OPPORTUNITY is project under the EU FET-Open funding1 in which we develop mobile systems to recognize human activity in dynamically varying sensor setups [1,2]. The system autonomously discovers available sensors around the user and self-configures to recognize desired activities. It reconfigures itself as the environment changes, and encompasses principles supporting autonomous operation in open-ended environments.OPPORTUNITYmainstreams ambient intelligence and improves user acceptance by relaxing constraints on body-worn sensor characteristics, and eases the deployment in real-world environments.We summarize key achievements of the project so far. The project outcomes are robust activity recognition systems. This may enable smarter activity-aware energy-management in buildings, and advanced activity-aware health assistants.",

keywords = "Activity recognition, Adaptive systems, Context framework, Machine learning, Pervasive computing",

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

year = "2011",

doi = "10.1016/j.procs.2011.09.003",

language = "English",

volume = "7",

series = "Procedia Computer Science",

publisher = "Elsevier BV",

pages = "173--174",

booktitle = "The European Future Technologies Conference and Exhibition (FET11)",

note = "The European Future Technologies Conference and Exhibition 2011 ; Conference date: 04-05-2011 Through 06-05-2011",

}

Roggen, D, Calatroni, A, Förster, K, Tröster, G, Lukowicz, P, Bannach, D, Ferscha, A, Kurz, M, Hölzl, G, Sagha, H, Millan, J & Chavarriaga, R 2011, Activity Recognition in Opportunistic Sensor Environments. in The European Future Technologies Conference and Exhibition (FET11). Bd. 7, Procedia Computer Science, S. 173-174, The European Future Technologies Conference and Exhibition 2011, Budapest, Ungarn, 04.05.2011. https://doi.org/10.1016/j.procs.2011.09.003

TY - GEN

T1 - Activity Recognition in Opportunistic Sensor Environments

AU - Roggen, Daniel

AU - Calatroni, Alberto

AU - Förster, Kilian

AU - Tröster, Gerhard

AU - Lukowicz, Paul

AU - Bannach, David

AU - Ferscha, Alois

AU - Kurz, Marc

AU - Hölzl, Gerold

AU - Sagha, Hesam

AU - Millan, Jose

AU - Chavarriaga, Ricardo

PY - 2011

Y1 - 2011

N2 - OPPORTUNITY is project under the EU FET-Open funding1 in which we develop mobile systems to recognize human activity in dynamically varying sensor setups [1,2]. The system autonomously discovers available sensors around the user and self-configures to recognize desired activities. It reconfigures itself as the environment changes, and encompasses principles supporting autonomous operation in open-ended environments.OPPORTUNITYmainstreams ambient intelligence and improves user acceptance by relaxing constraints on body-worn sensor characteristics, and eases the deployment in real-world environments.We summarize key achievements of the project so far. The project outcomes are robust activity recognition systems. This may enable smarter activity-aware energy-management in buildings, and advanced activity-aware health assistants.

AB - OPPORTUNITY is project under the EU FET-Open funding1 in which we develop mobile systems to recognize human activity in dynamically varying sensor setups [1,2]. The system autonomously discovers available sensors around the user and self-configures to recognize desired activities. It reconfigures itself as the environment changes, and encompasses principles supporting autonomous operation in open-ended environments.OPPORTUNITYmainstreams ambient intelligence and improves user acceptance by relaxing constraints on body-worn sensor characteristics, and eases the deployment in real-world environments.We summarize key achievements of the project so far. The project outcomes are robust activity recognition systems. This may enable smarter activity-aware energy-management in buildings, and advanced activity-aware health assistants.

KW - Activity recognition

KW - Adaptive systems

KW - Context framework

KW - Machine learning

KW - Pervasive computing

UR - https://www.scopus.com/pages/publications/84856480186

U2 - 10.1016/j.procs.2011.09.003

DO - 10.1016/j.procs.2011.09.003

M3 - Conference contribution

VL - 7

T3 - Procedia Computer Science

SP - 173

EP - 174

BT - The European Future Technologies Conference and Exhibition (FET11)

T2 - The European Future Technologies Conference and Exhibition 2011

Y2 - 4 May 2011 through 6 May 2011

ER -

Activity Recognition in Opportunistic Sensor Environments

Abstract

Publikationsreihe

Konferenz

UN SDGs

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