Abstract
Off-the-shelf smart devices and applications are expected to be pivotal in the coming need for massive home care. Deployment and integration of these systems in the same household may result in unplanned interactions involving users and entertainment, communication, and health-related devices. These unplanned interactions are a major concern when, for example, communication or entertainment applications interfere with the behavior of health-related devices. This paper presents a novel graph-based approach for representing the expected behavior of off-the-shelf smart devices and applications, their interactions, and for detecting interference in home care settings. A set of home care scenarios is used to assess the applicability of our approach. Our graph-based interference detection approach is integrated in the Safe Home Care reflective platform, which allows reifying the state of off-the-shelf systems and simulating home care scenarios. © 2012 IEEE.

Abstract
A Biomedical Wireless Sensor Network (BWSN) is a special Wireless Sensor Network (WSN) with a small number of nodes designed for medical applications. These networks must ensure that medical data is delivered reliably and efficiently, M order to fulfil a sel of pre-established Quality of Service (QoS) requirements, In this way, the research community have been proposing new solutions to improve QoS in WSN, namely in routing protocols and power consumption efficiency. However, there slid a need for appropriate QoS guaranties ha BWSN. In this paper, possible QoS requirements of BWSN are discussed, together with a framework to automatically evaluate the performance of such QoS techniques. That framework was used together with simulators and operating systems appropriate for WSN, COOJA and Contiki OS, and proved to be a valuable tool for a proper evaluation of QoS parameters and metrics.

Abstract

Abstract
This paper proposes a framework to support interaction with virtual objects integrated in a real life scenario. In the proposed interaction concept, the user can reshape or re-design a real space using virtual objects using several pictures of the desired space. The images are analyzed for known features such as surfaces, floor or room orientation. Using these elements, it is possible to devise an augmented reality system where the user can add virtual objects to the scenario that react to its content. These are magnetic objects because they attach itself to elements on the scene such as floor or walls. This approach liberates the user from moving objects in three-dimensional space. Instead they just push around objects in a more natural interface. The paper studies the impact of adding these magnetism effects in the interaction with an augmented reality-editing interface.

Abstract
In this paper we propose the integration of an online audio beat tracking system into the general framework of robot audition, to enable its application in musically-interactive robotic scenarios. To this purpose, we introduced a state-recovery mechanism into our beat tracking algorithm, for handling continuous musical stimuli, and applied different multi-channel preprocessing algorithms (e. g., beamforming, ego noise suppression) to enhance noisy auditory signals lively captured in a real environment. We assessed and compared the robustness of our audio beat tracker through a set of experimental setups, under different live acoustic conditions of incremental complexity. These included the presence of continuous musical stimuli, built of a set of concatenated musical pieces; the presence of noises of different natures (e. g., robot motion, speech); and the simultaneous processing of different audio sources on-the-fly, for music and speech. We successfully tackled all these challenging acoustic conditions and improved the beat tracking accuracy and reaction time to music transitions while simultaneously achieving robust automatic speech recognition.

Abstract
We present an approach for customized heartbeat classification of electrocardiogram (ECG) signals, based on the construction of one general multi-class classifier and one specific two-class classifier. The general classifier is trained on a global training dataset, containing examples of all possible classes and patterns. On the other hand, the individual-specific classifier is built using a small amount of individual data, which is a binary one-against-the-rest classifier, providing discrimination between normal and abnormal patterns from that individual. Such an individual-specific classifier can be a two-class classifier or a one-class classifier, depending on the availability of abnormal patterns in the individual training dataset. The classifications from the two classifiers are fused to obtain a final decision. The proposed approach is applied to the study of ECG heartbeat classification problem, significantly outperforming state-of-the-art methods. The proposed method can also be useful in anomaly detection of other biomedical signals.