  HMMs are widely used in action and gesture recognition due to their
implementation simplicity, low computational requirement, scalability and high
parallelism. They have worth performance even with a limited training set. All
these characteristics are hard to find together in other even more accurate
methods. In this paper, we propose a novel double-stage classification
approach, based on Multiple Stream Discrete Hidden Markov Models (MSD-HMM) and
3D skeleton joint data, able to reach high performances maintaining all
advantages listed above. The approach allows both to quickly classify
pre-segmented gestures (offline classification), and to perform temporal
segmentation on streams of gestures (online classification) faster than real
time. We test our system on three public datasets, MSRAction3D, UTKinect-Action
and MSRDailyAction, and on a new dataset, Kinteract Dataset, explicitly created
for Human Computer Interaction (HCI). We obtain state of the art performances
on all of them.
