Next Article in Journal
Combining Denoising Autoencoders and Dynamic Programming for Acoustic Detection and Tracking of Underwater Moving Targets
Previous Article in Journal
An Algorithm Based on Text Position Correction and Encoder-Decoder Network for Text Recognition in the Scene Image of Visual Sensors
Previous Article in Special Issue
A High-Resolution Open Source Platform for Building Envelope Thermal Performance Assessment Using a Wireless Sensor Network
Open AccessArticle

BioMove: Biometric User Identification from Human Kinesiological Movements for Virtual Reality Systems

1
Department of Computer Science and Software Engineering, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China
2
Department of Computer Science, University of Liverpool, Liverpool L69 7ZXl, UK
3
Department of Computer and Information Science, University of Mississippi, Oxford, MS 38677, USA
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(10), 2944; https://doi.org/10.3390/s20102944
Received: 30 December 2019 / Revised: 6 May 2020 / Accepted: 8 May 2020 / Published: 22 May 2020
Virtual reality (VR) has advanced rapidly and is used for many entertainment and business purposes. The need for secure, transparent and non-intrusive identification mechanisms is important to facilitate users’ safe participation and secure experience. People are kinesiologically unique, having individual behavioral and movement characteristics, which can be leveraged and used in security sensitive VR applications to compensate for users’ inability to detect potential observational attackers in the physical world. Additionally, such method of identification using a user’s kinesiological data is valuable in common scenarios where multiple users simultaneously participate in a VR environment. In this paper, we present a user study (n = 15) where our participants performed a series of controlled tasks that require physical movements (such as grabbing, rotating and dropping) that could be decomposed into unique kinesiological patterns while we monitored and captured their hand, head and eye gaze data within the VR environment. We present an analysis of the data and show that these data can be used as a biometric discriminant of high confidence using machine learning classification methods such as kNN or SVM, thereby adding a layer of security in terms of identification or dynamically adapting the VR environment to the users’ preferences. We also performed a whitebox penetration testing with 12 attackers, some of whom were physically similar to the participants. We could obtain an average identification confidence value of 0.98 from the actual participants’ test data after the initial study and also a trained model classification accuracy of 98.6%. Penetration testing indicated all attackers resulted in confidence values of less than 50% (<50%), although physically similar attackers had higher confidence values. These findings can help the design and development of secure VR systems. View Full-Text
Keywords: biometrics; user Identification; usability; security; virtual reality; kinesiology; mobility biometrics; user Identification; usability; security; virtual reality; kinesiology; mobility
Show Figures

Figure 1

MDPI and ACS Style

Olade, I.; Fleming, C.; Liang, H.-N. BioMove: Biometric User Identification from Human Kinesiological Movements for Virtual Reality Systems. Sensors 2020, 20, 2944.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop