Gesture Classification in Robotic Surgery using Recurrent Neural Networks with Kinematic Information

In this work we introduce the application of Recurrent Neural Networks (RNNs) on surgical kinematic data, for the classification of gestures in three fundamental surgical tasks (suturing, needle passing knot tying). The developed RNN-based classifier achieves close to 60% average classification accuracy for all three tasks when trained and tested with dVSS kinematic data from the same operator. Our preliminary work indicates that this type of artificial neural networks can be the building blocks in gesture classification systems which can form the basis for further developing automated skill assessment methods in robotic surgery