The Context Recognition Network (CRN) Toolbox permits fast implementation of activity and context recognition systems. Using parameterizable and reusable software components, it provides a broad set of online algorithms for multimodal sensor input, signal processing, and pattern recognition. The CRN Toolbox also features mechanisms for distributed processing and support for mobile and wearable devices. Three case studies demonstrate its versatility. In these case studies, the CRN Toolbox supports information flow in hospitals, monitors walking habits to help prevent cardiovascular diseases, and recognizes hand gestures in a car-parking game. This article is part of a special issue on activity-based computing.
%0 Journal Article
%1 BannachAmftLukowicz08pervasive
%A Bannach, David
%A Amft, Oliver
%A Lukowicz, Paul
%D 2008
%J Pervasive Computing
%K v1205 ieee paper embedded ai action pattern recognition sensor data processing
%N 2
%P 22-31
%R 10.1109/MPRV.2008.36
%T Rapid Prototyping of Activity Recognition Applications
%V 7
%X The Context Recognition Network (CRN) Toolbox permits fast implementation of activity and context recognition systems. Using parameterizable and reusable software components, it provides a broad set of online algorithms for multimodal sensor input, signal processing, and pattern recognition. The CRN Toolbox also features mechanisms for distributed processing and support for mobile and wearable devices. Three case studies demonstrate its versatility. In these case studies, the CRN Toolbox supports information flow in hospitals, monitors walking habits to help prevent cardiovascular diseases, and recognizes hand gestures in a car-parking game. This article is part of a special issue on activity-based computing.
@article{BannachAmftLukowicz08pervasive,
abstract = {The Context Recognition Network (CRN) Toolbox permits fast implementation of activity and context recognition systems. Using parameterizable and reusable software components, it provides a broad set of online algorithms for multimodal sensor input, signal processing, and pattern recognition. The CRN Toolbox also features mechanisms for distributed processing and support for mobile and wearable devices. Three case studies demonstrate its versatility. In these case studies, the CRN Toolbox supports information flow in hospitals, monitors walking habits to help prevent cardiovascular diseases, and recognizes hand gestures in a car-parking game. This article is part of a special issue on activity-based computing.},
added-at = {2012-05-30T10:42:44.000+0200},
author = {Bannach, David and Amft, Oliver and Lukowicz, Paul},
biburl = {https://www.bibsonomy.org/bibtex/27ffbb4e78029c834f83e59ec90f38ee3/flint63},
doi = {10.1109/MPRV.2008.36},
file = {IEEE Digital Library:2008/BannachAmftLukowicz08pervasive.pdf:PDF},
groups = {public},
interhash = {05dc6fa7eea1085be7aad774d0e75ae5},
intrahash = {285ea2937395f8e18dcccf7a8208e6cd},
issn = {1536-1268},
journal = {Pervasive Computing},
keywords = {v1205 ieee paper embedded ai action pattern recognition sensor data processing},
number = 2,
pages = {22-31},
timestamp = {2016-05-17T20:22:18.000+0200},
title = {Rapid Prototyping of Activity Recognition Applications},
username = {flint63},
volume = 7,
year = 2008
}