Recognition and segmentation of surgical gestures

Kurzfassung

Temporal segmentation and recognition of actions performed throughout a video have numerous applications in robotics, medical science, surveillance, etc. It plays a crucial role in the field of Minimally Invasive Robotic Surgery (MIRS), wherein the results can help obscure skill deficiencies, predict the most probable future gesture and improve the quality of feedback provided during surgical training. The current state-of-the-art techniques for MIRS are developed based on kinematic data. However, recent works have found video data to be equally discriminative. Since MIRS is a safety-critical application, we estimate the model's confidence for each prediction. The model is trained and evaluated using both the video and kinematic data available in the JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS) dataset.