David Bannach, Oliver Amft, Paul Lukowicz


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.   [Download]


@article {Bannach:Rapid:2008:6519,
	number = {2}, 
	month = {}, 
	year = {2008}, 
	title = {Rapid Prototyping of Activity Recognition Applications}, 
	journal = {IEEE Pervasive Computing}, 
	volume = {7}, 
	pages = {22-31}, 
	publisher = {IEEE Educational Activities Department, Piscataway, NJ, USA}, 
	author = {David Bannach, Oliver Amft, Paul Lukowicz}, 
	keywords = {CRN Toolbox, activity recognition, context recognition, distributed processing, gesture recognition, mobile devices, pattern recognition, rapid prototyping, rapid prototyping, activity recognition, context recognition, CRN Toolbox, wearable devices, mobil}