  Human action recognition has been one of the most active fields of research
in computer vision for last years. Two dimensional action recognition methods
are facing serious challenges such as occlusion and missing the third dimension
of data. Development of depth sensors has made it feasible to track positions
of human body joints over time. This paper proposes a novel method of action
recognition which uses temporal 3D skeletal Kinect data. This method introduces
the definition of body states and then every action is modeled as a sequence of
these states. The learning stage uses Fisher Linear Discriminant Analysis (LDA)
to construct discriminant feature space for discriminating the body states.
Moreover, this paper suggests the use of the Mahalonobis distance as an
appropriate distance metric for the classification of the states of involuntary
actions. Hidden Markov Model (HMM) is then used to model the temporal
transition between the body states in each action. According to the results,
this method significantly outperforms other popular methods, with recognition
rate of 88.64% for eight different actions and up to 96.18% for classifying
fall actions.
