  A modern operating room (OR) provides a plethora of advanced medical devices.
In order to better facilitate the information offered by them, they need to
automatically react to the intra-operative context. To this end, the progress
of the surgical workflow must be detected and interpreted, so that the current
status can be given in machine-readable form. In this work, Random Forests (RF)
and Hidden Markov Models (HMM) are compared and combined to detect the surgical
workflow phase of a laparoscopic cholecystectomy. Various combinations of data
were tested, from using only raw sensor data to filtered and augmented
datasets. Achieved accuracies ranged from 64% to 72% for the RF approach, and
from 80% to 82% for the combination of RF and HMM.
