  We apply recurrent neural networks to the task of recognizing surgical
activities from robot kinematics. Prior work in this area focuses on
recognizing short, low-level activities, or gestures, and has been based on
variants of hidden Markov models and conditional random fields. In contrast, we
work on recognizing both gestures and longer, higher-level activites, or
maneuvers, and we model the mapping from kinematics to gestures/maneuvers with
recurrent neural networks. To our knowledge, we are the first to apply
recurrent neural networks to this task. Using a single model and a single set
of hyperparameters, we match state-of-the-art performance for gesture
recognition and advance state-of-the-art performance for maneuver recognition,
in terms of both accuracy and edit distance. Code is available at
https://github.com/rdipietro/miccai-2016-surgical-activity-rec .
