The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.02 - April-June (2008 vol.7)
pp: 22-31
David Bannach , University of Passau
Oliver Amft , ETH Zurich
Paul Lukowicz , University of Passau
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.
INDEX TERMS
rapid prototyping, activity recognition, context recognition, CRN Toolbox, wearable devices, mobile devices, distributed processing, pattern recognition, gesture recognition
CITATION
David Bannach, Oliver Amft, Paul Lukowicz, "Rapid Prototyping of Activity Recognition Applications", IEEE Pervasive Computing, vol.7, no. 2, pp. 22-31, April-June 2008, doi:10.1109/MPRV.2008.36
REFERENCES
1. P. Costa et al., "The RUNESMiddleware for Networked Embedded Systems and Its Application in a Disaster Management Scenario," Proc. 5th IEEE Int'l Conf. Pervasive Computing and Communications (PerCom 07), IEEE CS Press, 2007, pp. 69–78.
2. J. Hill et al., "System Architecture Directions for Networked Sensors," ACM SIGPLANNotices, vol. 35, no. 11, 2000, pp. 93–104.
3. T. Weis et al., "Rapid Prototyping for Pervasive Applications," IEEE Pervasive Computing, vol. 6, no. 2, 2007, pp. 76–84.
4. S. Li et al., "Event Detection Using Data Service Middleware in Distributed Sensor Networks," Telecommunication Systems, vol. 26, nos. 2–4 2004, pp. 351–368.
5. K. Edwards et al., "The Challenges of User-Centered Design and Evaluation for Middleware," CHI Letters, vol. 5, no. 1, pp. 297–304.
6. C. Becker et al., "PCOM: A Component System for Pervasive Computing," Proc. 2nd IEEE Conf. Pervasive Computing and Communications (PerCom 04), IEEE CS Press, 2004, pp. 67–76.
7. D. Crockford, The Application/json Media Type for JavaScript Object Notation (JSON), IETF RFC 4627, July 2006; www.ietf.org/rfcrfc4627.txt.
8. K. Adamer et al., "Developing a Wearable Assistant for Hospital Ward Rounds: An Experience Report," to be published in Proc. Int'l Conf. Internet of Things (IOT 08), Springer, 2008; www.the-internet-of-things.org.
9. D. Bannach et al., "Distributed Modular Toolbox for Multimodal Context Recognition," Proc. 19th Int'l Conf. Architecture of Computing Systems, LNCS 3894, Springer, 2006, pp. 99–113.
10. T. Stiefmeier et al., "Event-Based Activity Tracking in Work Environments," Proc. 3rd Int'l Forum Applied Wearable Computing (IFAWC06), TZI Universität Bremen, 2006; http://spring.bologna.enea.it/ifawc/2006/ proceedingsIFAWC2006_10.pdf.
11. D. Bannach et al., "Waving Real Hand Gestures Recorded by Wearable Motion Sensors to a Virtual Car and Driver in a Mixed-Reality Parking Game," Proc. IEEE Symp. Computational Intelligence and Games (CIG 07), IEEE Press, 2007, pp. 32–39.
12. O. Amft, M. Kusserow, and G. Tröster, "Probabilistic Parsing of Dietary Activity Events," Proc. 4th Int'l Workshop Wearable and Implantable Body Sensor Networks (BSN 07), IFMBE13, Springer, 2007, pp. 242–247.
17 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool