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

Stats-Calculus Pose Descriptor Feeding A Discrete HMM Low-latency Detection and Recognition System For 3D Skeletal Actions