  Providing an accurate and efficient assessment of operative difficulty is
important for designing robot-assisted teleoperation interfaces that are easy
and natural for human operators to use. In this paper, we aim to develop a
data-driven approach to numerically characterize the operative difficulty
demand of complex teleoperation. In effort to provide an entirely
task-independent assessment, we consider using only data collected from the
human user including: (1) physiological response, and (2) movement kinematics.
By leveraging an unsupervised domain adaptation technique, our approach learns
the user information that defines task difficulty in a well-known source,
namely, a Fitt's target reaching task, and generalizes that knowledge to a more
complex human motor control scenario, namely, the teleoperation of a robotic
system. Our approach consists of two main parts: (1) The first part accounts
for the inherent variances of user physiological and kinematic response between
these cross-domain motor control scenarios that are vastly different. (2) A
stacked two-layer learner is designed to improve the overall modeling
performance, yielding a 96.6% accuracy in predicting the known difficulty of a
Fitts' reaching task when using movement kinematic features. We then validate
the effectiveness of our model by investigating teleoperated robotic needle
steering as a case study. Compared with a standard NASA TLX user survey, our
results indicate significant differences in the difficulty demand for various
choices of needle steering control algorithms, p<0.05, as well as the
difficulty of steering the needle to different targets, p<0.05. The results
highlight the potential of our approach to be used as a design tool to create
more intuitive and natural teleoperation interfaces in robot-assisted systems.
