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.

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