  Surgical gesture recognition is important for surgical data science and
computer-aided intervention. Even with robotic kinematic information,
automatically segmenting surgical steps presents numerous challenges because
surgical demonstrations are characterized by high variability in style,
duration and order of actions. In order to extract discriminative features from
the kinematic signals and boost recognition accuracy, we propose a multi-task
recurrent neural network for simultaneous recognition of surgical gestures and
estimation of a novel formulation of surgical task progress. To show the
effectiveness of the presented approach, we evaluate its application on the
JIGSAWS dataset, that is currently the only publicly available dataset for
surgical gesture recognition featuring robot kinematic data. We demonstrate
that recognition performance improves in multi-task frameworks with progress
estimation without any additional manual labelling and training.
