RFID Sensor-Tags Feeding a Context-Aware Rule-Based Healthcare Monitoring System

Abstract

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.

A Review of Passive RFID Tag Antenna-Based Sensors and Systems for Structural Health Monitoring Applications

“…Moreover, it is powered and read by standard Gen2 readers [163]. Though extremely flexible and versatile, the WISP solution is, of course, more expensive than traditional passive RFID tags and has limitations in terms of read range, that is almost 3 m [164]. …”

An Approach for Learning Expressive Ontologies in Medical Domain

“…They proposed a method which considers the following steps: 1) extraction of terms that represent domain concepts, building a lexicon; 2) construction of an initial taxonomy of concepts using is-a relations; 3) learning non-taxonomic relations using verb phrases; 4) ontology population by the detection of instances for the discovered concepts; and 5) evaluation of the obtained results comparing them against the medical standard vocabulary MeSH. 3 Lee et al [19] presented an automatic method to build a medical ontology. Their work focuses on the use of natural language processing techniques to improve the identification of semantic relations.…”

“…The SemanticHeatlh Project [1] has taken account the development of terminologies and ontologies in the medical context for interoperability of medical systems. Also, taking account the infrastructure in health care centers, where sensors, or embedding devices are working together, different standards, data heterogeneity and lack of uniform mark-up languages for sensors and devices restricts the flawless exchange, integration and reuse of information across different systems [2,3]. Thus, in the last years, the use of ontologies in the medical context has been an important step for providing consensus and an standard mechanism to represent information [4].…”

Privacy-Related Context Information for Ubiquitous Health

“…A lot of research has been done to support personalized actions and services in home care, chronic disease management, and ambient assisted living [33-37] with different personalized health status, body sensor networks, activity or behavior monitoring, decision support, and reminder applications [32,33,35-40]. In the hospital environment, many professionals are very agile, and context-aware technologies may help by personalizing services for them by location, time, and social context [41].…”