Abstract:
This paper is related to the domain of human
activity recognition in both depth images and
skeleton joints. In this paper, for the detection task, a
RGB-D sensor (Microsoft Kinect) is used. To obtain
discriminative features for action detection,
combination of a depth shape features from the 3D
space and joints features are investigated. The
detection and classification of such features is
accomplished by the posture analysis technique,
based on K-means and finally, activity recognition
are performed by means of HMMs built on the set of
known postures to improve performance and
accuracy. The proposed system can be evaluated on a
new dataset which contains five activities (standing,
walking, sit down, lying and bending) and another
public dataset MSRDailyActivity3D. The proposed
system can be applied to the specific domain of
healthcare system including home and hospital to
keep older adults functioning at higher levels and
living independently.
