A Framework for Predicting Haptic Feedback in Needle Insertion in 5G Remote Robotic Surgery

Document Type

Conference Proceeding

Source of Publication

2020 IEEE 17th Annual Consumer Communications and Networking Conference, CCNC 2020

Publication Date



© 2020 IEEE. Robots are being used more and more in surgery due to the many benefits they bring (e.g. reduction of patient discomfort, precision, reliability). Remote robotic surgery is now expected to become a reality due to the emergence of 5G. Needle insertion is a crucial element of many robotic surgical procedures such as biopsies, injections, neurosurgery, and brachytherapy cancer treatment. During needle insertion in remote robotic surgery, there is still no guarantee that the surgeon will obtain the haptic feedback from the patient side within the stringent deadlines, even in 5G settings. This paper proposes a framework for learning by imitation as a way to predict the messages that will eventually fail to reach their destination within the required deadlines. By leveraging expert demonstrations, the Hidden Markov Model is used to encapsulate a set of expert force/torque profiles and corresponding parameters during the off-line training process. A Gaussian mixture regression is then used to reproduce a generalized version of the force/torque profile and corresponding parameters during the prediction. Simulations are conducted to evaluate the performance of the proposed method. They show that our proposed framework is able to execute predictions in much less than the 1ms end-to-end latency requirement of remote robotic surgery.




Institute of Electrical and Electronics Engineers Inc.

Last Page



Computer Sciences


fifth generation (5G), haptic feedback, machine learning, remote robotic surgery, Tactile internet

Scopus ID


Indexed in Scopus


Open Access