  In this paper, we present a descriptor for human whole-body actions based on
motion coordination. We exploit the principle, well known in neuromechanics,
that humans move their joints in a coordinated fashion. Our coordination-based
descriptor (CODE) is computed by two main steps. The first step is to identify
the most informative joints which characterize the motion. The second step
enriches the descriptor considering minimum and maximum joint velocities and
the correlations between the most informative joints. In order to compute the
distances between action descriptors, we propose a novel correlation-based
similarity measure. The performance of CODE is tested on two public datasets,
namely HDM05 and Berkeley MHAD, and compared with state-of-the-art approaches,
showing recognition results.
