Abstract-This paper details the development and testing of a serious-game based movement therapy aimed at encouraging stroke patients with upper limb motor disorders to practice physical exercises. The system contains a series of Virtual Reality (VR) games. A framework for VR movement therapy is described which consists of a number of serious games designed to encourage patients' physical activity in highly motivating, virtual environments where various factors such as size and gravity can be scaled to adapt to individual patient's abilities and in-game performance. Another goal of this study is to determine whether the provision of serious games based interventions improves motor outcome after stroke. A pilot study with 8 participants who have a first hemispheric stroke shows improvements on impairment measurement and functional measurement shortly after completion of the intervention and 6 weeks after the intervention. Despite its limitations the findings of this study support the effectiveness of serious games in the treatment of participants with hemiplegia. The study also raises awareness of the benefits of using serious games in movement therapy after stroke.

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During a catastrophic event, the International Charter 1 "Space and Major Disasters" is regularly activated and provides the rescue teams damage maps prepared by a photo-interpreter team basing on pre and post-disaster satellite images. A satellite image manual processing must be accomplished in most cases to build these maps, a complex and demanding process. Given the importance of time in such critical situations, automatic or semiautomatic tools are highly recommended. Despite the quick treatment presented by automatic processing, it usually presents a semantic gap issue. Our aim is to express expert knowledge using a well-defined knowledge representation method: ontologies and make semantics explicit in geographic and remote sensing applications by taking the ontology advantages in knowledge representation, expression, and knowledge discovery. This research focuses on the design and implementation of a comprehensive geographic ontology in the case of major disasters, that we named GEO-MD, and illustrates its application in the case of Haiti 2010 earthquake. Results show how the ontology integration reduces the semantic gap and improves the automatic classification accuracy.

Abstract-The International Charter1 "Space and Major Disasters" is regularly activated during a catastrophic event and offers rescue team damage maps. Most of these maps are built by means of satellite image manual processing, which is often complex and demanding in terms of time and energy. Automatic processing offers prompt treatment; nevertheless it usually presents a semantic gap handicap. The exploitation of ontologies to bridge the semantic gap has been widely recommended due to their quality of knowledge representation, expression, and discovery. In this work, we present an ontology-based semantic hierarchical classification method to undertake this problem. Ontology components are translated to image-based parameters and used to assist the classification process with two levels and 12 embedded classes. The region of interest is selected from the first level, and exhaustively analyzed and classified at the second level. The 2010 Haiti earthquake was selected as study area for this work. Experiments were performed using very high resolution multi-temporal QuickBird imagery and eCognition software.

The University of Gloucestershire accepts no liability for any infringement of intellectual property rights in any material deposited but will remove such material from public view pending investigation in the event of an allegation of any such infringement.

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Recent work in animated human-like agent has made impressive progress toward generating agents with believable appearances and realistic motions for the interactive applications of inhabited virtual worlds. It remains difficult, however, to instruct animated agents to perform specific tasks or take initiatives. This paper addresses the challenge of instructability by introducing cognitive modelling -a novel logical approach based on a highly developed logical theory of actions, i. e. Event Calculus. Cognitive models go beyond behavioural models in that they govern an agent's behaviour by reasoning about its knowledge, actions and events. To facilitate the construction of the cognitive models, we propose a high-level behaviour specification language (BSL) from the event calculus formalism. Using BSL, we can specify an agent's domain knowledge, design behaviour controllers and then control the agent's behaviour in terms of goals and/or user's instructions. This approach allows agent's behaviours to be specified and controlled more naturally and intuitively, more succinctly and at a much higher level of abstraction than would otherwise be possible. It also provides a logical characterisation of planning via abductive reasoning process. Furthermore, we integrate sensing capability into our underlying theoretical framework, thus enabling animated agents to generate appropriate behaviour even in complex, dynamic virtual worlds. An animated human-like interface agent for virtual environments is used to demonstrate the approach. The architecture for implementing the approach is also described.

The University of Gloucestershire accepts no liability for any infringement of intellectual property rights in any material deposited but will remove such material from public view pending investigation in the event of an allegation of any such infringement.