Deep disengagement detection during telesurgery

Abstract

Various user input device (UID) disengagement-detection techniques based on real-time time-series data processing and deep learning. More specifically, various UID disengagement-detection techniques include training a long short-term memory (LSTM) network-based classifier based on the acquired time-series data of UID motions including both surgical motions and docking motions. The trained deep-learning classifier can then be used during teleoperation sessions to monitor the movements of UIDs, and continuously classify the real-time UID motions as either teleoperation motions or docking motions. The disengagement-detection techniques can immediately disengage the UIDs from the surgical tools as soon as the monitored UID motions are classified as docking motions by the trained classifier, thereby preventing unintended surgical tool motions. The disengagement-detection techniques allow the UIDs and the surgical tools to become disengaged naturally by simply having the user putting the UIDs back to their docking positions without having to take any additional actions.