Human Activity Recognition for Mobile Robot
This addresses the problem of enabling autonomous mobile robots to navigate uncontrolled environments through human activity recognition, but appears incremental as it applies an existing method (CNN) to new data.
The paper tackles human activity recognition for mobile robots by training a convolutional neural network model, achieving high accuracy on both the Vicon physical action dataset and a new VMCUHK dataset.
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.