  Due to the increasing number of mobile robots including domestic robots for
cleaning and maintenance in developed countries, human activity recognition is
inevitable for congruent human-robot interaction. Needless to say that this is
indeed a challenging task for robots, it is expedient to learn human activities
for autonomous mobile robots (AMR) for navigating in an uncontrolled
environment without any guidance. Building a correct classifier for complex
human action is non-trivial since simple actions can be combined to recognize a
complex human activity. In this paper, we trained a model for human activity
recognition using convolutional neural network. We trained and validated the
model using the Vicon physical action dataset and also tested the model on our
generated dataset (VMCUHK). Our experiment shows that our method performs with
high accuracy, human activity recognition task both on the Vicon physical
action dataset and VMCUHK dataset.
