3D hand trajectory segmentation by curvatures and hand orientation for classification through a probabilistic approach

In this work we present the segmentation and classification of 3D hand trajectory. Curvatures features are acquired by (r, ¿, h) and the hand orientation is acquired by approximating the hand plane in 3D space. The 3D positions of the hand movement are acquired by markers of a magnetic tracking system. Observing humans movements we perform a learning phase using histogram techniques. Based on the learning phase is possible classify reach-to-grasp movements applying Bayes rule to recognize the way that a human grasps an object by continuous classification based on multiplicative updates of beliefs. We are classifying the hand trajectory by its curvatures and by hand orientation along the trajectory individually. Both results are compared after some trials to verify the best classification between these two kinds of segmentation. Using entropy as confidence level, we can give weights for each kind of classification to combine both, acquiring a new classification for results comparison. Using these techniques we developed an application to estimate and classify two possible types of grasping by the reach-to-grasp movements performed by humans. These reported steps are important to understand some human behaviors before the object manipulation and can be used to endow a robot with autonomous capabilities (e.g. reaching objects for handling).