By Leandro Pecchia, Liming Chen, Chris Nugent, Jose Bravo
This publication constitutes the refereed complaints of the sixth foreign Workshop on Ambient Assisted dwelling, IWAAL 2014, held in Belfast, united kingdom, in December 2014. The forty two complete papers offered with 12 papers of the workshop bet 2014 and 10 papers of a unique consultation HTA have been conscientiously reviewed and chosen from quite a few submissions. the point of interest of the papers is on following issues: ADL detection, reputation, type; behavioural alterations, training and schooling; AAL layout and technical review; expression, temper and speech attractiveness; health and wellbeing tracking, chance prediction and evaluation; localization; and person personal tastes, usability, AAL recognition and adoption.
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Additional info for Ambient Assisted Living and Daily Activities: 6th International Work-Conference, IWAAL 2014, Belfast, UK, December 2-5, 2014. Proceedings
This included defining data structures for storing device information such as group addresses, data types and device names, as well as managing access to the bus system by providing an interface that can be accessed by other systems or components. As a first step we defined the database structure, followed by a REST interface that makes the database accessible for other applications and software components, using the HTTP2 protocol. A major advantage of a REST interface over other technologies is that it is well supported by a lot of programming languages.
Silhouette-based human action recognition using sequences of key poses. Pattern Recognition Letters 34, 1799–1807 (2013) 2. : A vision-based system for intelligent monitoring: human behaviour analysis and privacy by context. Sensors 14, 8895–8925 (2014) 3. : A probabilistic, discriminative and distributed system for the recognition of human actions from multiple views. Neurocomputing 75, 78–87 (2012) 4. : Human action recognition with sparse classification and multiple-view learning. 12040 5.
A learned action model is then used to classify the cuboid into one of the predefined action classes. Although the IDT descriptor  has shown the best performance up to date on a range of action recognition datasets, the STIP descriptor has the advantage of computational efficiency due to the sparse nature of its features and not requiring the precomputed person detections. As a result, both descriptors are considered in this paper. The classification stage uses discriminative models to learn the appearance and motion patterns of each action class, while employing various fusion techniques to combine either the visual features or the classification scores of multiple camera views for an aggregated decision making.