  In this paper, we present a novel approach for continuous operator
authentication in teleoperated robotic processes based on Hidden Markov Models
(HMM). While HMMs were originally developed and widely used in speech
recognition, they have shown great performance in human motion and activity
modeling. We make an analogy between human language and teleoperated robotic
processes (i.e. words are analogous to a teleoperator's gestures, sentences are
analogous to the entire teleoperated task or process) and implement HMMs to
model the teleoperated task. To test the continuous authentication performance
of the proposed method, we conducted two sets of analyses. We built a virtual
reality (VR) experimental environment using a commodity VR headset (HTC Vive)
and haptic feedback enabled controller (Sensable PHANToM Omni) to simulate a
real teleoperated task. An experimental study with 10 subjects was then
conducted. We also performed simulated continuous operator authentication by
using the JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS). The
performance of the model was evaluated based on the continuous (real-time)
operator authentication accuracy as well as resistance to a simulated
impersonation attack. The results suggest that the proposed method is able to
achieve 70% (VR experiment) and 81% (JIGSAW dataset) continuous classification
accuracy with as short as a 1-second sample window. It is also capable of
detecting the impersonation attack in real-time.
