  Motivation: Recognizing human actions in a video is a challenging task which
has applications in various fields. Previous works in this area have either
used images from a 2D or 3D camera. Few have used the idea that human actions
can be easily identified by the movement of the joints in the 3D space and
instead used a Recurrent Neural Network (RNN) for modeling. Convolutional
neural networks (CNN) have the ability to recognise even the complex patterns
in data which makes it suitable for detecting human actions. Thus, we modeled a
CNN which can predict the human activity using the joint data. Furthermore,
using the joint data representation has the benefit of lower dimensionality
than image or video representations. This makes our model simpler and faster
than the RNN models. In this study, we have developed a six layer convolutional
network, which reduces each input feature vector of the form 15x1961x4 to an
one dimensional binary vector which gives us the predicted activity. Results:
Our model is able to recognise an activity correctly upto 87% accuracy. Joint
data is taken from the Cornell Activity Datasets which have day to day
activities like talking, relaxing, eating, cooking etc.
