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The use of Radio Frequency IDentification technology (RFID) in the medical context enables drug identification but also a rapid and, of course, precise identification of patients, physicians, nurses or any other health caregiver. Combining RFID tag identification with structured and secure Internet of Things (IoT) solutions, one can establish a ubiquitous and quick access to any type of medical related records, as long as one can control and adequately secure all the Internet mediated interactions. This paper presents an e-Health service architecture, along with the corresponding Internet of Things prototype implementation that makes use of RFID tags and Electronic Product Codes (EPC) standards, in order to easily establish in a ubiquitous manner a medication control system. The system, presented and tested, has a web interface and allowed for a first evaluation of the e-health proposed service. As the service is mainly focused on elderly Ambient Assisted Living (AAL) solutions, all these technologies - RFID, EPC, Object Naming Service (ONS) and IoT – have been integrated into a suitable system, able to promote better patient/physician, patient/nurse and, generally, any patient/health caregiver, interactions. The whole prototype service, entitled “RFID-based IoT for Medication Control”, and its web interface are presented and evaluated.

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Abstract. In this paper we propose an architecture for processing endoscopic procedures results. The goal is to create a complete system capable of processing any type of endoscopic multimedia results, in order to overcome the most common issues in the endoscopic domain (e.g. video's long-duration, gastroenterologist's possible difficulty to maintain the focus and efficiency during the viewing process, imperfections in images/videos). It was this scenario that led to the conception of the MIVprocessing solution, which will address these and other problems, providing an added value to the elaboration of diagnoses. The MIVprocessing is composed of five tasks: Video Summarization (elimination of the "non-informative" frames); Pre-Processing (correction/improvement of the frames); Pre-Detection; Segmentation; and Feature Extraction and Classification. The idea is to create a framework that brings together the capabilities of different but at the same time complementary concepts (e.g. image and signal processing, machine learning, computer vision). This conjugation applied to the endoscopic domain provides a set of features capable of improving the gastroenterologist's activities during and after the procedure.