Handwriting learning systems: Towards an adaptation model in virtual environments

Handwriting learning is a complex and multi-steps process where trainees are supposed to improve several psycho-motor and cognitive skills. Intelligent tutoring systems are used for non-gestural teaching and are strategy-based, whereas sensorial-feedback systems (visual, audio, haptic, ...) are widely used for gestural teaching. These approaches are reactive, improve learning performances, but do not integrate motor skill evolution through time. We discuss two challenging issues i.e. user activity analysis and adaptive guidance for handwriting learning with mixed reality systems.