A Predictive Approach for Enhancing Accuracy in Remote Robotic Surgery Using Informer Model

Precise and real-time estimation of the robotic arm’s position on the patient’s side is essential for the success of remote robotic surgery in Tactile Internet environments. This paper presents a prediction model based on the Transformer-based Informer framework for accurate and efficient position estimation, combined with a Four-State Hidden Markov Model (4-State HMM) to simulate realistic packet loss scenarios. The proposed approach addresses challenges such as network delays, jitter, and packet loss to ensure reliable and precise operation in remote surgical applications. The method integrates the optimization problem into the Informer model by embedding constraints such as energy efficiency, smoothness, and robustness into its training process using a differentiable optimization layer. Evaluation using the JIGSAWS dataset achieves a prediction accuracy exceeding 90% under diverse network scenarios, outperforming models such as TCN, RNN, and LSTM, demonstrating its suitability for real-time Tactile Internet-enabled robotic surgery.

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Sensors