Abstract
The study of stress and fatigue among First Responders is a major step in mitigating this public health problem. Blood pressure, heart rate variability and fatigue related arrhythmia are three of the main "windows" to study stress and fatigue. In this paper we present a wearable medical device, capable of acquiring an electrocardiogram and estimating blood pressure in real time, through a pulse wave transit time approach. The system is based on an existent certified wearable medical device called "Vital Jacket" and is aimed to become a tool to allow cardiologists in studying stress and fatigue among first response professionals.

Abstract

Abstract
This paper describes the work developed in the scope of the European project TEAM-HOS, regarding the experience of using the ‘Unified Modelling Language’ (UML) as a tool for modelling optimal solutions in healthcare. In this paper we explain how UML is being used to cover information and process modelling as well as for modelling organisational structures and their interdependencies, in order to define the optimal integrated information exchange inside the hospital. We present an overall assessment of UML, based on our experience, and we explain why we found UML to be a practical formalism for ‘modelling the hospital into the future’. © 2002 Inderscience Enterprises Ltd.

Abstract
Introduction: Current scientific knowledge reinforces that successful reorganization of surviving nervous tissue supports cognitive and motor recovery after stroke. The development of new neurorehabilitation tools to modulate this physiologic process is needed. In this context, vibratory stimuli are a noninvasive form of proprioceptive stimulation of the nervous system and are freely available and easy to use at a low cost. Objectives: To access the feasibility and tolerability of targeted vibratory stimuli delivered through a wearable device in an early post-stroke setting. Patients and methods: Five stroke patients were recruited from a stroke unit setting having a first ever medial cerebral artery ischemic stroke with motor deficit. The stimulation device developed delivered external vibratory stimuli to major joints at preprogrammed arrays of intensity, duration and interval of actuation. The tolerability test was set for five-hour duration and during that period data on vital parameters, cognitive, motor and sensitive performance as well as anxiety scores were recorded. Results: The device remained in place throughout and none of the patients or relatives asked to interrupt the tolerability test. There were no major complications during the trial or the ensuing days. Attention to the affected side during stimulation was increased in four patients, and two were reported as clearly more awake during the test. Discussion: This is the first tolerability test focused on the use of targeted vibratory stimulus as a neurorehabilitation tool in stroke patients. There were no hazards to report and most interestingly the majority of patients showed increased awareness to the affected side of the body. These findings will be further analyzed under functional MRI control and on long-term ambulatory use trials.

Abstract
Mobile Sensor Networks based on connected vehicles and smart phones are poised to become key enablers in the development of sustainable and intelligent transportation systems in urban environments. By gathering and processing massive amounts of data in real-time, this form of information and communication infrastructure can be instrumental towards improving traffic flow, reducing carbon emissions and promoting multi-modal mobility and enhanced coordination among public transit systems. We propose a system architecture for a Massive Multi-Sensor Urban Scanner capable of acquiring large quantities of real-time information from a vast variety of sources and sending the data to a back-end data processing cloud using multiple communication interfaces. Requirements, technical challenges, design choices and first results are explained in detail based on a prototype that is currently being deployed in Porto, Portugal.

Abstract
This paper studies the suitability of brain activity, namely electroencephalogram signals, as raw material for conducting biometric authentication of individuals. Brain responses were extracted with visual stimulation, leading to biological brain responses known as Visual Evoked Potentials. We evaluated a novel method, using only 8 occipital electrodes and the energy of differential EEG signals, to extract information about the subjects for further use as their biometric features. To classify the features obtained from each individual, we used a one-class classifier per subject and we tested four types of classifiers: K-Nearest Neighbor, Support Vector Data Description and two other classifiers resulting from the combination of the two ones previously mentioned. After testing these four classifiers with features of 70 subjects, the results showed that visual evoked potentials are suitable for an accurate biometric authentication.