  Recognition of human actions, under low observational latency, is a growing
interest topic, nowadays. Many approaches have been represented based on a
provided set of 3D Cartesian coordinates system originated at a certain
specific point located on a root joint. In this paper, We will present a
statistical detection and recognition system using Hidden Markov Model using 7
types of pose descriptors. * Cartesian Calculus Pose descriptor. * Angular
Calculus Pose descriptor. * Mixed-mode Stats-Calculus Pose descriptor. *
Centro-Stats-Calculus Pose descriptor. * Rela-Centro-Stats-Calculus Pose
descriptor. * Rela-Centro-Stats-Calculus DCT Pose descriptor. *
Rela-Centro-Stats-Calculus DCT-AMDF Pose descriptor. Stats-Calculus is a
feature extracting technique, that is developed on Moving Pose descriptor , but
using a combination of Statistics measures and Calculus measures.
