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From a highly distributed timed automata specification, the paper analyses an implementation in the form of a looping controller, launching possibly many tasks in each cycle. Qualitative and quantitative constraints are distinguished on the specification to allow such an implementation, and the analysis of the semantic differences between the specification and the implementation leads to define an overapproximating model. The implementation is then "sandwiched" between the original specification and the new model, allowing to check if the important properties of the specification are preserved by the implementation.

The purpose of this paper is to describe human motions and emotions that appear on real video images with compact and informative representations. We aimed to recognize expressive motions and analyze the relationship between human body features and emotions. We propose a new descriptor vector for expressive human motions inspired from the Laban Movement Analysis method (LMA), a descriptive language with an underlying semantics that allows to qualify human motion in its different aspects. The proposed descriptor is fed into a machine learning framework including, Random Decision Forest, Multi-Layer Perceptron and two multiclass Support Vector Machines methods. We evaluated our descriptor first for motion recognition and second for emotion recognition from the analysis of expressive body movements. Preliminary experiments with three public datasets, MSRC-12, MSR Action 3D and UTkinect showed that our model performs better than many existing motion recognition methods. We also built a dataset composed of 10 control motions (move, turn left, turn right, stop, sit down, wave, dance, introduce yourself, increase velocity, decrease velocity). We tested our descriptor vector and