  In this paper, we tackle the problem of 24 hours-monitoring patient actions
in a ward such as "stretching an arm out of the bed", "falling out of the bed",
where temporal movements are subtle or significant. In the concerned scenarios,
the relations between scene layouts and body kinematics (skeletons) become
important cues to recognize actions; however they are hard to be secured at a
testing stage. To address this problem, we propose a kinematic-layout-aware
random forest which takes into account the kinematic-layout (\ie layout and
skeletons), to maximize the discriminative power of depth image appearance. We
integrate the kinematic-layout in the split criteria of random forests to guide
the learning process by 1) determining the switch to either the depth
appearance or the kinematic-layout information, and 2) implicitly closing the
gap between two distributions obtained by the kinematic-layout and the
appearance, when the kinematic-layout appears useful. The kinematic-layout
information is not required for the test data, thus called "privileged
information prior". The proposed method has also been testified in cross-view
settings, by the use of view-invariant features and enforcing the consistency
among synthetic-view data. Experimental evaluations on our new dataset PATIENT,
CAD-60 and UWA3D (multiview) demonstrate that our method outperforms various
state-of-the-arts.
