Extracting human behavioral biometrics from robot motions

Long Huang, Zhen Meng, Zeyu Deng, Chen Wang, Liying Li, Guodong Zhao

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Motion-controlled robots 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. This work builds up a motion-controlled robotic arm framework and proposes to verify who is controlling the robotic arm by examining the robotic arm's behavior. We show that a robotic arm's motion inherits its human controller's behavioral biometric in interactive control scenarios. 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. Extensive experiments show that our system achieves high accuracy to distinguish users while using the robot's behaviors.
Original languageEnglish
Title of host publicationMobiCom '21
Subtitle of host publicationProceedings of the 27th Annual International Conference on Mobile Computing and Networking
Place of PublicationNew York
Number of pages3
ISBN (Print)9781450383424
Publication statusPublished - 25 Oct 2021
EventACM MobiCom '21: The 27th Annual International Conference on Mobile Computing and Networking - New Orleans, United States
Duration: 31 Jan 20224 Feb 2022

Publication series

NameProceedings of the Annual International Conference on Mobile Computing and Networking


ConferenceACM MobiCom '21
Country/TerritoryUnited States
CityNew Orleans


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