Human Activity Recognition for Mobile Robot

TL;DR

This paper presents a convolutional neural network model for human activity recognition in mobile robots, trained and validated on established and new datasets, demonstrating high accuracy in complex environments.

Contribution

The paper introduces a CNN-based approach for human activity recognition in mobile robots, validated on both existing and newly generated datasets, improving recognition accuracy in uncontrolled settings.

Findings

High accuracy achieved on Vicon dataset

Effective recognition on new VMCUHK dataset

CNN model suitable for autonomous robot navigation

Abstract

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.