Home // ACHI 2023, The Sixteenth International Conference on Advances in Computer-Human Interactions // View article

RHM-HAR-SK: A Multiview Dataset with Skeleton Data for Ambient Assisted Living Research

Authors:
Mohamad Reza Shahabian Alashti
Mohammad Hossein Bamorovat Abadi
Patrick Holthaus
Catherine Menon
Farshid Amirabdollahian

Keywords: Assistive Robot, Non-generative, Multi-view dataset, Skeleton-based, Activity Recognition

Abstract:
Human and activity detection has always been a vital task in Human-Robot Interaction (HRI) scenarios, such as those involving assistive robots. In particular, skeleton-based Human Activity Recognition (HAR) offers a robust and effective detection method based on human biomechanics. Recent advancements in human pose estimation have made it possible to extract skeleton positioning data accurately and quickly using affordable cameras. In interaction with a human, robots can therefore capture detailed information from a close distance and flexible perspective. However, recognition accuracy is susceptible to robot movements, where the robot often fails to capture the entire scene. To address this we propose the adoption of external cameras to improve the accuracy of activity recognition on a mobile robot. In support of this proposal, we present the dataset RHM-HAR-SK that combines multiple camera perspectives augmented with human skeleton extraction obtained by the HRNet pose estimation. We apply qualitative and quantitative analysis to the extracted skeleton and its joints to evaluate the coverage of extracted poses per camera perspective and activity. Results indicate that the recognition accuracy for the skeleton varies between camera perspectives and also joints, depending on the type of activity. However, recognition accuracy is susceptible to robot movements, where the robot often fails to capture the entire scene. To address this we propose the adoption of external cameras to improve the accuracy of activity recognition on a mobile robot. In support of this proposal, we present the dataset RHM-HAR-SK that combines multiple camera perspectives augmented with human skeleton extraction obtained by the HRNet pose estimation. We apply qualitative and quantitative analysis techniques to the extracted skeleton and its joints to demonstrate the additional value of external cameras to the robot's recognition pipeline. Results indicate that the recognition accuracy for the skeleton varies between camera perspectives and also joints, depending on the type of activity.

Pages: 181 to 187

Copyright: Copyright (c) IARIA, 2023

Publication date: April 24, 2023

Published in: conference

ISSN: 2308-4138

ISBN: 978-1-68558-078-0

Location: Venice, Italy

Dates: from April 24, 2023 to April 28, 2023