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 (TI) environments. This paper presents a prediction model based on the Transformer-based Informer framework for accurate and efficient posit...
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MDPI AG
2025-05-01
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| author | Muhammad Hanif Lashari Shakil Ahmed Wafa Batayneh Ashfaq Khokhar |
| author_facet | Muhammad Hanif Lashari Shakil Ahmed Wafa Batayneh Ashfaq Khokhar |
| author_sort | Muhammad Hanif Lashari |
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| description | 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 (TI) 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. The Informer framework uses features such as ProbSparse attention, attention distilling, and a generative-style decoder to focus on position-critical features while maintaining a low computational complexity of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi mathvariant="script">O</mi><mo>(</mo><mi>L</mi><mo form="prefix">log</mo><mi>L</mi><mo>)</mo></mrow></semantics></math></inline-formula>. The method is evaluated using the JIGSAWS dataset, achieving a prediction accuracy of over 90% under various network scenarios. A comparison with models such as TCN, RNN, and LSTM demonstrates the Informer framework’s superior performance in handling position prediction and meeting real-time requirements, making it suitable for Tactile Internet-enabled robotic surgery. |
| format | Article |
| id | doaj-art-91fab40c52264b47b4addbfe1e266d35 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-05-01 |
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| spelling | doaj-art-91fab40c52264b47b4addbfe1e266d352025-08-20T03:47:58ZengMDPI AGSensors1424-82202025-05-012510306710.3390/s25103067A Predictive Approach for Enhancing Accuracy in Remote Robotic Surgery Using Informer ModelMuhammad Hanif Lashari0Shakil Ahmed1Wafa Batayneh2Ashfaq Khokhar3Department of Electrical & Computer Engineering, Iowa State University, Ames, IA 50011, USADepartment of Electrical & Computer Engineering, Iowa State University, Ames, IA 50011, USADepartment of Electrical & Computer Engineering, Iowa State University, Ames, IA 50011, USADepartment of Electrical & Computer Engineering, Iowa State University, Ames, IA 50011, USAPrecise 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 (TI) 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. The Informer framework uses features such as ProbSparse attention, attention distilling, and a generative-style decoder to focus on position-critical features while maintaining a low computational complexity of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi mathvariant="script">O</mi><mo>(</mo><mi>L</mi><mo form="prefix">log</mo><mi>L</mi><mo>)</mo></mrow></semantics></math></inline-formula>. The method is evaluated using the JIGSAWS dataset, achieving a prediction accuracy of over 90% under various network scenarios. A comparison with models such as TCN, RNN, and LSTM demonstrates the Informer framework’s superior performance in handling position prediction and meeting real-time requirements, making it suitable for Tactile Internet-enabled robotic surgery.https://www.mdpi.com/1424-8220/25/10/3067tactile internetremote robotic surgerytransformerinformer modelfour-state hidden Markov modelpacket loss |
| spellingShingle | Muhammad Hanif Lashari Shakil Ahmed Wafa Batayneh Ashfaq Khokhar A Predictive Approach for Enhancing Accuracy in Remote Robotic Surgery Using Informer Model Sensors tactile internet remote robotic surgery transformer informer model four-state hidden Markov model packet loss |
| title | A Predictive Approach for Enhancing Accuracy in Remote Robotic Surgery Using Informer Model |
| title_full | A Predictive Approach for Enhancing Accuracy in Remote Robotic Surgery Using Informer Model |
| title_fullStr | A Predictive Approach for Enhancing Accuracy in Remote Robotic Surgery Using Informer Model |
| title_full_unstemmed | A Predictive Approach for Enhancing Accuracy in Remote Robotic Surgery Using Informer Model |
| title_short | A Predictive Approach for Enhancing Accuracy in Remote Robotic Surgery Using Informer Model |
| title_sort | predictive approach for enhancing accuracy in remote robotic surgery using informer model |
| topic | tactile internet remote robotic surgery transformer informer model four-state hidden Markov model packet loss |
| url | https://www.mdpi.com/1424-8220/25/10/3067 |
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