Smart cities are demanding solutions for improved traffic efficiency, in order to guarantee optimal access to mobility resources available in urban areas. Intelligent video analytics deployed directly on board embedded sensors offers great opportunities to gather highly informative data about traffic and transport, allowing reconstruction of a real-time neat picture of urban mobility patterns. In this paper, we present a visual sensor network in which each node embeds computer vision logics for analyzing in real time urban traffic. The nodes in the network share their perceptions and build a global and comprehensive interpretation of the analyzed scenes in a cooperative and adaptive fashion. This is possible thanks to an especially designed Internet of Things (IoT) compliant middleware which encompasses in-network event composition as well as full support of Machine-2-Machine (M2M) communication mechanism. The potential of the proposed cooperative visual sensor network is shown with two sample applications in urban mobility connected to the estimation of vehicular flows and parking management. Besides providing detailed results of each key component of the proposed solution, the validity of the approach is demonstrated by extensive field tests that proved the suitability of the system in providing a scalable, adaptable and extensible data collection layer for managing and understanding mobility in smart cities.

Left Ventricle (LV) Ejection Fraction (EF) is a fundamental parameter for heart function assessment. Being based on border tracing, however, manual computation of EF is time-consuming and extremely prone to inter-and intraobserver variability. In this paper we present an automatic method for EF computation which provides results in agreement with those provided by expert observers. The segmentation strategy consists of two stages: first, the region of interest is identified by means of mimetic criteria; then, the identified region is used for initialization of an active contour based on a variational formulation of level set methods, which provides accurate segmentation of the LV cavity. Volume calculation is then performed according to the conventional Simpson’s rule and, finally, the EF is computed.

Background: Over the past ten years, the authors have been designing, developing, and testing pervasive technology to support children with autism (ASD). Methods: In the present study, an integrated system based on multimedia and augmented interaction technologies have been tested on young subjects with ASD and dyspraxia in the age range of 6–10 years, in charge for rehabilitation treatments; a team of clinical psychologists has analyzed the results of the experimentation. The ten children involved in the project underwent an initial assessment of praxis skills and motor coordination. Subsequently, the subjects were subdivided into two subgroups: five children participated in the experimentation and five were evaluated as the control group (treatment as usual). Results: The evaluation showed an increased score in the several aspects considered, and particularly those related to motor coordination. An improvement in balancing tests and in hands-movement testing was found. Conclusion: The children involved in the sessions showed greater ability to self-control the movement as well as to select specific motor areas. The methods used seem to be promising to improve emotional and social skills too in a motivating and enjoyable climate. A high level of acceptance by professionals was observed and parents’ feedback was also positive.

CHRONIOUS is an highly innovative Information and Communication Technologies (ICT) research Initiative that aspires to implement its vision for ubiquitous health and lifestyle monitoring. The 17 European project partners are strictly working together since February 2008 to realize and open platform to manage and monitor elderly patients with chronic diseases and many difficulties to reach hospital centers for routine controls. The testing activities will be done in Italy and Spain involving COPD (Chronic Obstructive Pulmonary Disease) and CKD (Chronic Kidney Disease) patients, these being widespread and highly expensive in terms of social and economic costs. Patients, equipped by wearable technologies and sensors and interacting with lifestyle interfaces, will be assisted by healthcare personnel able to check the health record and critical conditions through the Chronious platform data analysis and decision support system. Additionally, the new ontology based literature search engine will help the clinicians in the standardization of care delivery process. This paper is to present the main project objectives and its principal components from the intelligent system point of view.