In this paper, we present a method for recognizing human activities using information sensed by an RGB-D camera, namely the Microsoft Kinect. Our approach is based on the estimation of some relevant joints of the human body by means of the Kinect; three different machine learning techniques, i.e., K-means clustering, support vector machines, and hidden Markov models, are combined to detect the postures involved while performing an activity, to classify them, and to model each activity as a spatiotemporal evolution of known postures. Experiments were performed on Kinect Activity Recognition Dataset, a new dataset, and on CAD-60, a public dataset. Experimental results show that our solution outperforms four relevant works based on RGB-D image fusion, hierarchical Maximum Entropy Markov Model, Markov Random Fields, and Eigenjoints, respectively. The performance we achieved, i.e., precision/recall of 77.3% and 76.7%, and the ability to recognize the activities in real time show promise for applied use

No abstract

Long QT syndrome incidence is increasing in general population. A careful pre-, peri- and post-operative management is needed for patients with this syndrome because of the risk of Torsades de Pointes and malignant arrhythmias. The available data regarding prevention of lethal Torsades de Pointes during anesthesia in patients with long QT syndrome is scant and conflicting: only case reports and small case series with different outcomes have been published. Actually, there are no definitive guidelines on pre-, peri- and post-operative anesthetic management of congenital long QT syndrome. Our review focuses on anesthetic recommendations for patients diagnosed with congenital long QT syndrome furnishing some key points for preoperative optimization, intraoperative anesthetic agents and postoperative care plan, which could be the best for patients with c-long QT syndrome who undergo surgery.

In recent years, reduction of energy consumption in buildings has increasingly gained interest among researchers mainly due to practical reasons, such as economic advantages and long-term environmental sustainability. Many solutions have been proposed in the literature to address this important issue from complementary perspectives, which are often hard to capture in a comprehensive manner. This survey article aims at providing a structured and unifying treatment of the existing literature on intelligent energy management systems in buildings, with a distinct focus on available architectures and methodology supporting a vision transcending the well-established smart home vision, in favor of the novel Ambient Intelligence paradigm. Our exposition will cover the main architectural components of such systems, beginning with the basic sensory infrastructure, moving on to the data processing engine where energy-saving strategies may be enacted, to the user interaction interface subsystem, and finally to the actuation infrastructure necessary to transfer the planned modifications to the environment. For each component, we will analyze different solutions, and we will provide qualitative comparisons, also highlighting the impact that a single design choice can have on the rest of the system.

No abstract