In the context of this model, we address two problems. The first problem is the estimation of the unknown weighting parameters of a cost function from a segmented and labeled data set for an action. We show that the estimation of these parameters can be cast as a least squares optimization problem and present results for arm motions such as reaching and punching using motion capture data collected from different subjects.
The second problem is that of action recognition in which a stream of data is segmented into different actions, where the set of actions to be identified is pre-determined. We show that the problem of action recognition is similar to that of mode estimation in a hybrid system and can be solved using a particle filter if a receding horizon formulation of the optimal controller is adopted. We use the proposed approach to recognize different reaching actions from the 3D hand trajectory of subjects.