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

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

9 Citations (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.

Original language	English
Title of host publication	The European Future Technologies Conference and Exhibition (FET11)
Pages	173-174
Number of pages	2
Volume	7
DOIs	https://doi.org/10.1016/j.procs.2011.09.003
Publication status	Published - 2011
Event	The European Future Technologies Conference and Exhibition 2011 - Budapest, Hungary Duration: 4 May 2011 → 6 May 2011

Publication series

Name	Procedia Computer Science
Publisher	Elsevier BV

Conference

Conference	The European Future Technologies Conference and Exhibition 2011
Country/Territory	Hungary
City	Budapest
Period	04.05.2011 → 06.05.2011

Keywords

Activity recognition
Adaptive systems
Context framework
Machine learning
Pervasive computing

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.procs.2011.09.003

Cite this

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) (Vol. 7, pp. 173-174). (Procedia Computer Science). https://doi.org/10.1016/j.procs.2011.09.003

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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). vol. 7, Procedia Computer Science, pp. 173-174, The European Future Technologies Conference and Exhibition 2011, Budapest, Hungary, 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 - http://www.scopus.com/inward/record.url?scp=84856480186&partnerID=8YFLogxK

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

Publication series

Conference

Keywords

UN SDGs

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