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Temporal data fusion in multisensor systems using dynamic time warping

Hsiao, Ko Ming, West, Geoff, Venkatesh, Svetha and Kumar, Mohan 2005, Temporal data fusion in multisensor systems using dynamic time warping, in SENSORFUSION 2005 : Workshop on Information Fusion and Dissemination in Wireless Sensor Networks, IEEE, [Budapest, Hungary], pp. 1-9.

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Title Temporal data fusion in multisensor systems using dynamic time warping
Author(s) Hsiao, Ko Ming
West, Geoff
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Kumar, Mohan
Conference name Information Fusion and Dissemination in Wireless Sensor Networks. Workshop (2005 : Budapest, Hungary)
Conference location Budapest, Hungary
Conference dates 14 Jul. 2005
Title of proceedings SENSORFUSION 2005 : Workshop on Information Fusion and Dissemination in Wireless Sensor Networks
Editor(s) [Unknown]
Publication date 2005
Conference series Information Fusion and Dissemination in Wireless Sensor Networks. Workshop
Start page 1
End page 9
Total pages 9
Publisher IEEE
Place of publication [Budapest, Hungary]
Keyword(s) data
sensors
fusion process
temporal fusion
dynamic time warping (DTW)
Summary Data acquired from multiple sensors can be fused at a variety of levels: the raw data level, the feature level, or the decision level. An additional dimension to the fusion process is temporal fusion, which is fusion of data or information acquired from multiple sensors of different types over a period of time. We propose a technique that can perform such temporal fusion. The core of the system is the fusion processor that uses Dynamic Time Warping (DTW) to perform temporal fusion. We evaluate the performance of the fusion system on two real world datasets: 1) accelerometer data acquired from performing two hand gestures and 2) NOKIA’s benchmark dataset for context recognition. The results of the first experiment show that the system can perform temporal fusion on both raw data and features derived from the raw data. The system can also recognize the same class of multisensor temporal sequences even though they have different lengths e.g. the same human gestures can be performed at different speeds. In addition, the fusion processor can infer decisions from the temporal sequences fast and accurately. The results of the second experiment show that the system can perform fusion on temporal sequences that have large dimensions and are a mix of discrete and continuous variables. The proposed fusion system achieved good classification rates efficiently in both experiments
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Language eng
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©2005, IEEE
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044821

Document type: Conference Paper
Collections: School of Information Technology
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.