RFID Sensor-Tags Feeding a Context-Aware Rule-Based Healthcare Monitoring System
- 699 Downloads
Along with the growing of the aging population and the necessity of efficient wellness systems, there is a mounting demand for new technological solutions able to support remote and proactive healthcare. An answer to this need could be provided by the joint use of the emerging Radio Frequency Identification (RFID) technologies and advanced software choices. This paper presents a proposal for a context-aware infrastructure for ubiquitous and pervasive monitoring of heterogeneous healthcare-related scenarios, fed by RFID-based wireless sensors nodes. The software framework is based on a general purpose architecture exploiting three key implementation choices: ontology representation, multi-agent paradigm and rule-based logic. From the hardware point of view, the sensing and gathering of context-data is demanded to a new Enhanced RFID Sensor-Tag. This new device, de facto, makes possible the easy integration between RFID and generic sensors, guaranteeing flexibility and preserving the benefits in terms of simplicity of use and low cost of UHF RFID technology. The system is very efficient and versatile and its customization to new scenarios requires a very reduced effort, substantially limited to the update/extension of the ontology codification. Its effectiveness is demonstrated by reporting both customization effort and performance results obtained from validation in two different healthcare monitoring contexts.
KeywordsUHF RFID Sensor integration Healthcare monitoring system Context-awareness Ontology Pervasive computing
- 1.Goebel, R., et al, Lecture notes in artificial intelligence. Proc. of 12th Conf. on Artificial Intelligence in Medicine, AIME 2009, Verona (Italy), Springer, July 18–22, 2009.Google Scholar
- 2.Cortés, U., and Ed, Poch M., Advanced agent-based environmental management systems. Whitestein series in software agents technologies and autonomic computing. Birkhäuser Verlag, Berlin, 2009.Google Scholar
- 4.Virtanen, J., Ukkonen, L., Bjorninen, T., and Sydanheimo, L., Printed humidity sensor for UHF RFID systems. IEEE Conf. Sensor Appl. Symp. (SAS): 269, Feb 23–25, 2010.Google Scholar
- 5.De Donno, D., Ricciato, F., Catarinucci, L., Coluccia, A., and Tarricone, L., Challenge: Towards distributed RFID sensing with software-defined radio, MobiCom 2010, Chicago, Illinois, USA, pp. 97–104, Sept. 2010.Google Scholar
- 6.Yeager, D., Powledge, P., Prasad, R., Wetherall, D., and Smith, J., Wirelessly-charged UHF tags for sensor data collection. In: Proc. IEEE Int. Conf. RFID. pp. 320–327, Apr. 16–17, 2008.Google Scholar
- 7.EPCglobal, EPC radio-frequency identify protocols class-1 generation-2 UHF RFID protocol for communications at 860 MHz-960 MHz, version 1.0.9, 2005.Google Scholar
- 8.Paganelli, F., et al, ERMHAN, a context-aware service platform to support continuous care networks for home-based assistance. Int. J. Telemed. Appl. 2008(5), 2008.Google Scholar
- 9.Esposito, A., Tarricone, L., Zappatore, M., Catarinucci, L., and Colella, R., A framework for context-aware home-health monitoring. Int. J. Autonom. Adapt. Comm. Syst. 3(1), 2010.Google Scholar
- 12.Chen, H., et al., An ontology for context-aware pervasive computing environments. Knowl. Eng. Rev. Camb. Univ. Press 18:197–207, 2003.Google Scholar
- 13.Catarinucci, L., Colella, R., and Tarricone, L., A cost-effective UHF RFID tag for transmission of generic sensor data in wireless sensor networks. IEEE. Trans. Microw. Theor. Tech. (MTT)–Special Issue on RFID Technology ISSN: 0018–9480, 2009.Google Scholar
- 15.Otero, A., et al, Intelligent alarms for patient supervision. Proc. of the IEEE Int. Symp. on Intelligent Signal Processing, WISP 2007, pp. 1–6, 2007.Google Scholar
- 16.Dockhorn-Costa, P., Ferreira Pires, L., and Van Sinderen, M., Architectural patterns for context-aware services platforms, 2nd Int. Workshop on Ubiquitous Computing (IWUC), in conjunction with ICEIS, Miami, USA, 2005.Google Scholar
- 17.JADE Java Agent Development Environment homepage. http://jade.cselt.it. (last access: July 2011).
- 18.Protégé homepage. http://protege.stanford.edu/. (last access: July 2011).
- 19.OWL. http://w3.org/TR/2004/RDC-owl-features-20040210/. (last access: July 2011).
- 20.Friedman-Hill, E., Jess in action. Manning Publications Co, Greenwich, 2003.Google Scholar
- 21.BeanGenerator, http://protege.cim3.net/cgi-bin/wiki.pl?OntologyBeanGenerator (last access: July 2011).
- 22.Catarinucci, L., Cappelli, M., Colella, R., and Tarricone, L., A novel and low-cost multisensor-tag for Rfid applications. In: Healthcare Int. J.: Microwave and Optical Technology Letters. J. Wiley & Sons, 2008.Google Scholar
- 23.Williamson, J., JESS normalized rules http://www.jessrules.com/jesswiki/view?KeepYourRulesNormalized. (last access: July 2011).
- 24.AIDA homepage: On-line free web-based diabetes software simulator. http://www.2aida.org/aida/options.htm. (last access: July 2011).
- 25.Blanchard, S., et al, AIDA on-line: A glucose and insulin simulator on the WWW. Proc. of the 20th Annual Int. Conf. on the IEEE Engineering in Medicine and Biology Society, 3, p. 1159–1162, 1998.Google Scholar
- 26.Borowczyk, A., Gawinecki, M., and Paprzycki, M., BDI agents in a patient monitoring scenario, 2nd Int. Conf. on Pervasive Computing Technologies for Healthcare, PervasiveHealth08, pp. 82–85, 2008.Google Scholar
- 27.Talaei-Khoei, A., Ray, P., and Parameswaran, N., An awareness framework for agent-based mobile health monitoring, 3rd Int. Conf. on Next Generation Mobile Applications, Services and Technologies (NGMAST’09), pp. 108–113, 2009.Google Scholar
- 28.Ruan, J., MacCaull, W., and Jewers, H., Enhancing patient-centered palliative care with collaborative agents web intelligence and intelligent agent technology (WI-IAT), 2010 IEEE/WIC/ACM Int. Conf., vol.: 3, pp. 356–360, 2010.Google Scholar
- 29.Mansour, A., et al., Finding similar patients in a multi-agent environment, 2011 Annual Meeting of the North American of the Fuzzy Information Processing Society (NAFIPS), pp. 1–6, 2011.Google Scholar
- 30.Paranjape, R., and Gill, S., Agent-based simulation of healthcare for type II diabetes, 2nd Int. Conf. on Advances in System Simulation (SIMUL10), pp. 22–27, 2010.Google Scholar