Publication: A windowing approach for activity recog...
Master data
Title: | A windowing approach for activity recognition in sensor data streams |
Subtitle: | |
Abstract: | Determining the appropriate data window size for online sensor data streams to recognize a specific activity is still a challenging task. In particular, when new sensor events are recorded. This paper proposes a windowing algorithm which presents promising results to recognize complex activities, e.g., in a smart home environment. The underlying basic idea is to analyze the sensor data in order to identify the set of “best fitting sensors”: it contains those sensors that most contribute to the recognition task, and therefore should be considered in a window. To validate our approach, we applied it on the CASAS data set which is an international data set for activity recognition. Based on the promising results, we believe that this algorithm can assist to detect human activities. Thus, our approach might be used in Active and Assisted Living Environments (AAL), where activity recognition is required to distinguish the types of help, a person needs to master his/her daily life activities. |
Keywords: |
Publication type: | Article in compilation (Authorship) |
Publication date: | 11.08.2016 (Online) |
Published by: |
Eighth International Conference on Ubiquitous and Future Networks (ICUFN)
Eighth International Conference on Ubiquitous and Future Networks (ICUFN)
(
IEEE Xplore Digital Library;
)
to publication |
Title of the series: | - |
Volume number: | - |
First publication: | Yes |
Version: | - |
Page: | - |
Versionen
Keine Version vorhanden |
Publication date: | 11.08.2016 |
ISBN (e-book): |
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eISSN: | 2288-0712 |
DOI: | http://dx.doi.org/10.1109/ICUFN.2016.7536937 |
Homepage: | http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7527553 |
Open access |
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Assignment
Organisation | Address | ||||
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Fakultät für Technische Wissenschaften
Institut für Artificial Intelligence und Cybersecurity
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AT - A-9020 Klagenfurt |
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Research Cluster | No research Research Cluster selected |
Peer reviewed |
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Projects: | No related projects |
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