In a globalized world members of groups may be anywhere, and the need for ubiquitous Idea Generation emerged. This led to two main needs: the creation of Smart Decision Rooms prepared for this new reality and following the Ambient Intelligence paradigm; and the creation of context aware middleware. This paper describes OLAVAmI a context aware middle-ware system which was tested in LAID environment, a Smart Meeting Room. OLAVAmI allows video production focusing on the speaker, an audio-to-text conversion service, and the multimedia database of meetings produced in an autonomous way. To experiment OLAVAmI usage and functionalities one of the tools present in LAID test bed was used and the results are presented in this article.

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Heart disorders are one of the most problematic issues of human health. There are currently many efforts to reduce the time for first assistance based on electronic systems that continuously records the electric heart activity for further inspection and anomalies detection. The most popular are portable monitoring systems based on the Electrocardiogram (ECG) signal. However, an efficient detection of heart problems still being a big challenge mainly due to the difficulty of accessing some specific cardiac problems and signal variations between different patients. A different technique for heart diagnosing is based on spatial recording of electrical heart activity, commonly known as Vectorcardiogram (VCG). VCG is pointed by several authors as a more efficient tool than ECG for heart inspection and problem detection. This research explores the possibility of using VCG signals in a portable device for constant heart monitoring and injuries detection. It is presented a portable solution with VCG recording and digital signal processing for automatic diagnosis. This paper covers the aspects of the VCG signal and its ability to be converted into a 12-lead ECG signal. It is also presented a system for automatic diagnosis based on VCG which is based on a multichannel portable hardware platform to support noise cancellation, parameter extraction and decision making algorithms.

Heart disorders are one of the most problematic issues of human health. There are currently many efforts to reduce the time for first assistance based on electronic systems that continuously records the electric heart activity (ECG), for further inspection and anomalies detection. However, an efficient automatic detection of heart problems still being a big challenge mainly due to difficulty of accessing some specific cardiac problems, signal variations between different patients and the level of uncertainty caused by the absence of significant data or medical elements from patient. This research explores the use of several health condition signals and indicators for heart monitoring and injuries detection. This paper presents the first approach of a portable solution running decision support algorithms to produce medical diagnosis based on electrical and sound heart signals, ambient conditions, patient mobility, patient personal data and clinical history.

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