  Purpose: Manual feedback in basic RMIS training can consume a significant
amount of time from expert surgeons' schedule and is prone to subjectivity.
While VR-based training tasks can generate automated score reports, there is no
mechanism of generating automated feedback for surgeons performing basic
surgical tasks in RMIS training. In this paper, we explore the usage of
different holistic features for automated skill assessment using only robot
kinematic data and propose a weighted feature fusion technique for improving
score prediction performance.
  Methods: We perform our experiments on the publicly available JIGSAWS dataset
and evaluate four different types of holistic features from robot kinematic
data - Sequential Motion Texture (SMT), Discrete Fourier Transform (DFT),
Discrete Cosine Transform (DCT) and Approximate Entropy (ApEn). The features
are then used for skill classification and exact skill score prediction. Along
with using these features individually, we also evaluate the performance using
our proposed weighted combination technique.
  Results: Our results demonstrate that these holistic features outperform all
previous HMM based state-of-the-art methods for skill classification on the
JIGSAWS dataset. Also, our proposed feature fusion strategy significantly
improves performance for skill score predictions achieving up to 0.61 average
spearman correlation coefficient.
  Conclusions: Holistic features capturing global information from robot
kinematic data can successfully be used for evaluating surgeon skill in basic
surgical tasks on the da Vinci robot. Using the framework presented can
potentially allow for real time score feedback in RMIS training.
