Last years have seen a growing interest on the Serious Games topic-and in particular on Games for Health-from both scientific and industrial communities. However not only the effectiveness of this kind of games is not yet demonstrated but the distribution and adoption of these games from the normal public is still very low. In this paper we present a design strategy we adopted in on the occasion of the development of a game for hemiplegic rehabilitation named "Hammer and Planks". This game strategy allowed us to create a "game for all", as will be demonstrated by the example of the usage of the game on the occasion of a game event in the south of France.

The advancement of motion sensing input devices has enabled the collection of multivariate time-series body movement data. Analyzing such type of data is challenging due to the large amount of data and the task of mining for interesting temporal movement patterns. To address this problem, we propose an interface to visualize and analyze body movement data. This visualization enables users to navigate and explore the evolution of movement over time for different movement areas. We also propose a clustering method based on hierarchical clustering to group similar movement patterns. The proposed visualization is illustrated with a case study which demonstrates the ability of the interface to analyze body movements.

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The use of computer vision techniques to build hands-free input devices has long been a topic of interest to researchers in the field of natural interaction. In recent years Microsoft's Kinect has brought these technologies to the layman, but the most commonly used libraries for Kinect human pose recognition are closed-source. There is not yet an accepted, effective open-source alternative upon which highly specific applications can be based. We propose a novel technique for extracting the appendage configurations of users from the Kinect camera's depth feed, based on stochastic local search techniques rather than per-pixel classification.