Enhanced grip force estimation in robotic surgery: A sparrow search algorithm-optimized backpropagation neural network approach
The absence of an effective gripping force feedback mechanism in minimally invasive surgical robot systems impedes physicians' ability to accurately perceive the force between surgical instruments and human tissues during surgery, thereby increasing surgical risks. To address the challenge of i...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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AIMS Press
2024-02-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2024155?viewType=HTML |
| _version_ | 1797272635923496960 |
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| author | Yongli Yan Tiansheng Sun Teng Ren Li Ding |
| author_facet | Yongli Yan Tiansheng Sun Teng Ren Li Ding |
| author_sort | Yongli Yan |
| collection | DOAJ |
| description | The absence of an effective gripping force feedback mechanism in minimally invasive surgical robot systems impedes physicians' ability to accurately perceive the force between surgical instruments and human tissues during surgery, thereby increasing surgical risks. To address the challenge of integrating force sensors on minimally invasive surgical tools in existing systems, a clamping force prediction method based on mechanical clamp blade motion parameters is proposed. The interrelation between clamping force, displacement, compression speed, and the contact area of the clamp blade indenter was analyzed through compression experiments conducted on isolated pig kidney tissue. Subsequently, a prediction model was developed using a backpropagation (BP) neural network optimized by the Sparrow Search Algorithm (SSA). This model enables real-time prediction of clamping force, facilitating more accurate estimation of forces between instruments and tissues during surgery. The results indicate that the SSA-optimized model outperforms traditional BP networks and genetic algorithm-optimized (GA) BP models in terms of both accuracy and convergence speed. This study not only provides technical support for enhancing surgical safety and efficiency, but also offers a novel research direction for the design of force feedback systems in minimally invasive surgical robots in the future. |
| first_indexed | 2024-03-07T14:32:11Z |
| format | Article |
| id | doaj.art-9ee93763e31046bf9ce52c5e472b86e7 |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-03-07T14:32:11Z |
| publishDate | 2024-02-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-9ee93763e31046bf9ce52c5e472b86e72024-03-06T01:15:09ZengAIMS PressMathematical Biosciences and Engineering1551-00182024-02-012133519353910.3934/mbe.2024155Enhanced grip force estimation in robotic surgery: A sparrow search algorithm-optimized backpropagation neural network approachYongli Yan0Tiansheng Sun1Teng Ren 2Li Ding 31. Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing 100083, China2. The Fourth Medical Center of China General Hospital of People's Liberation Army, Beijing 100700, China3. School of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, China1. Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing 100083, China4. School of Biological Science and Medical Engineering, Beihang University, Beijing 100083, ChinaThe absence of an effective gripping force feedback mechanism in minimally invasive surgical robot systems impedes physicians' ability to accurately perceive the force between surgical instruments and human tissues during surgery, thereby increasing surgical risks. To address the challenge of integrating force sensors on minimally invasive surgical tools in existing systems, a clamping force prediction method based on mechanical clamp blade motion parameters is proposed. The interrelation between clamping force, displacement, compression speed, and the contact area of the clamp blade indenter was analyzed through compression experiments conducted on isolated pig kidney tissue. Subsequently, a prediction model was developed using a backpropagation (BP) neural network optimized by the Sparrow Search Algorithm (SSA). This model enables real-time prediction of clamping force, facilitating more accurate estimation of forces between instruments and tissues during surgery. The results indicate that the SSA-optimized model outperforms traditional BP networks and genetic algorithm-optimized (GA) BP models in terms of both accuracy and convergence speed. This study not only provides technical support for enhancing surgical safety and efficiency, but also offers a novel research direction for the design of force feedback systems in minimally invasive surgical robots in the future.https://www.aimspress.com/article/doi/10.3934/mbe.2024155?viewType=HTMLminimally invasive surgical robotclamp force estimationbp neural networksparrow search algorithm |
| spellingShingle | Yongli Yan Tiansheng Sun Teng Ren Li Ding Enhanced grip force estimation in robotic surgery: A sparrow search algorithm-optimized backpropagation neural network approach Mathematical Biosciences and Engineering minimally invasive surgical robot clamp force estimation bp neural network sparrow search algorithm |
| title | Enhanced grip force estimation in robotic surgery: A sparrow search algorithm-optimized backpropagation neural network approach |
| title_full | Enhanced grip force estimation in robotic surgery: A sparrow search algorithm-optimized backpropagation neural network approach |
| title_fullStr | Enhanced grip force estimation in robotic surgery: A sparrow search algorithm-optimized backpropagation neural network approach |
| title_full_unstemmed | Enhanced grip force estimation in robotic surgery: A sparrow search algorithm-optimized backpropagation neural network approach |
| title_short | Enhanced grip force estimation in robotic surgery: A sparrow search algorithm-optimized backpropagation neural network approach |
| title_sort | enhanced grip force estimation in robotic surgery a sparrow search algorithm optimized backpropagation neural network approach |
| topic | minimally invasive surgical robot clamp force estimation bp neural network sparrow search algorithm |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2024155?viewType=HTML |
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