Predictive Position Estimation for Remote Surgery under Packet Loss Using the Informer Framework

TL;DR

This paper introduces a Transformer-based Informer model combined with a Hidden Markov Model to accurately predict robotic arm positions in remote surgery, effectively handling network issues like packet loss and delays.

Contribution

It presents a novel predictive framework using the Informer model with HMM to improve real-time position estimation under network uncertainties in remote surgery.

Findings

Achieves over 90% accuracy in position prediction.

Outperforms traditional models like TCN, RNN, and LSTM.

Handles network-induced delays, jitter, and packet loss effectively.

Abstract

Accurate and real-time position estimation of the robotic arm on the patient's side is crucial for the success of remote robotic surgery in Tactile Internet environments. This paper proposes a predictive approach using the computationally efficient Transformer-based Informer model for position estimation, combined with a Four-State Hidden Markov Model (4-State HMM) to simulate realistic packet loss scenarios. The method effectively addresses network-induced delays, jitter, and packet loss, ensuring reliable performance in remote robotic surgery. The study evaluates the Informer model on the JIGSAWS dataset, demonstrating its capability to handle sequential data challenges caused by network uncertainties. Key features, including ProbSparse attention and a generative-style decoder, enhance prediction accuracy, computational speed, and memory efficiency. Results indicate that the proposed method achieves over 90 percent accuracy across varying network conditions. Furthermore, the Informer framework outperforms traditional models such as TCN, RNN, and LSTM, highlighting its suitability for real-time remote surgery applications.