Toward verifying the user of motion-controlled robotic arm systems via the robot behavior

Huang, L., Meng, Z., Deng, Z., Wang, C., Li, L. and Zhao, G. (2022) Toward verifying the user of motion-controlled robotic arm systems via the robot behavior. IEEE Internet of Things Journal, 9(22), pp. 22422-22433. (doi: 10.1109/JIOT.2021.3121623)

Abstract

Motion-controlled robotic arms allow a user to interact with a remote real world without physically reaching it. By connecting cyberspace to the physical world, such interactive teleoperations are promising to improve remote education, virtual social interactions and online participatory activities. In this work, we build up a motion-controlled robotic arm framework comprising a robotic arm end and a user end, which are connected via a network and responsible for manipulator control and motion capture respectively. To protect the system access, we propose to verify who is controlling the robotic arm by examining the robotic arm’s behavior, which adds a second security layer in addition to the system login credentials. We show that a robotic arm’s motion inherits its human controller’s behavioral biometric in interactive control scenarios. By extracting the angle readings of the robotic arm’s all joints, the proposed user authentication approach reconstructs the robotic arm’s end-effector movement trajectory that follows the user’s hand. Furthermore, we derive the unique robotic motion features to capture the user’s behavioral biometric embedded in the robot motions and develop learning-based algorithms to verify the robotic arm user to be one of the enrolled users or a nonuser. Extensive experiments show that our system achieves 94% accuracy to distinguish users while preventing user identity spoofing attacks with 95% accuracy.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Zhao, Dr Guodong and Li, Dr Emma and Meng, Mr Zhen
• Authors: Huang, L., Meng, Z., Deng, Z., Wang, C., Li, L., and Zhao, G.
• College/School: College of Science and Engineering > School of Engineering > Systems Power and Energy
• Journal Name: IEEE Internet of Things Journal
• Publisher: IEEE
• ISSN: 2327-4662
• ISSN (Online): 2327-4662
• Published Online: 20 October 2021
• Copyright Holders: Copyright © 2021 IEEE
• First Published: First published in IEEE Internet of Things Journal 9(22): 22422-22433
• Publisher Policy: Reproduced in accordance with the publisher copyright policy

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
304896		EPSRC-IAA: Early Stage Commercialisation of a PET Imaging Agent for the Detection of Cardiovascular Disease and Cancer	Andrew Sutherland	Engineering and Physical Sciences Research Council (EPSRC)	EP/R511705/1	Chemistry