Dynamic order selection analysis in adaptive polynomial Kalman filtering: implementation and integration of sensor data and hybrid image processing for bio-inspired needle systems
This study investigates dynamic-order selection in Adaptive Polynomial Kalman Filtering (APKF) for tracking the bioinspired dual-sheath needle systems used in biopsy procedures. Emphasizing integration of sensor data and hybrid image processing, the goal is to achieve precise motion estimation, whic...
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| Format: | Article |
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Taylor & Francis Group
2025-12-01
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| Series: | Systems Science & Control Engineering |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/21642583.2025.2546839 |
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| author | Dileep Sivaraman Branesh M. Pillai Cholatip Wiratkapun Jackrit Suthakorn Songpol Ongwattanakul |
| author_facet | Dileep Sivaraman Branesh M. Pillai Cholatip Wiratkapun Jackrit Suthakorn Songpol Ongwattanakul |
| author_sort | Dileep Sivaraman |
| collection | DOAJ |
| description | This study investigates dynamic-order selection in Adaptive Polynomial Kalman Filtering (APKF) for tracking the bioinspired dual-sheath needle systems used in biopsy procedures. Emphasizing integration of sensor data and hybrid image processing, the goal is to achieve precise motion estimation, which is critical to medical robotics. A hybrid image tracking system combined with APKF was implemented for real-time needle tip tracking and validated using a linear rail setup. Initial simulations showed that the standard APKF significantly outperformed traditional Kalman Filtering (KF), achieving an average reduction of 46.9% in Root Mean Square Error (RMSE), 57.8% in Mean Absolute Error (MAE), and 64.5% in Median Absolute Deviation (MAD). To further improve the performance, model-order selection criteria–Mean Squared Error (MSE), Akaike Information Criterion (AIC), Corrected AIC (AICc), and Bayesian Information Criterion (BIC)–were applied within the APKF framework. This led to even greater reductions in RMSE (55.4%), MAE (61.2%), and MAD (65.9%) compared with KF. The results highlight the effectiveness of combining model-order selection with adaptive filtering to enhance real-time estimation. The proposed tracking system demonstrates improved accuracy and control, reinforcing the potential of bioinspired needle systems in robot-assisted biopsy procedures. |
| format | Article |
| id | doaj-art-44937fdf31ec4ab5aab5d8a88ed8bc4b |
| institution | Kabale University |
| issn | 2164-2583 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Systems Science & Control Engineering |
| spelling | doaj-art-44937fdf31ec4ab5aab5d8a88ed8bc4b2025-08-21T11:16:07ZengTaylor & Francis GroupSystems Science & Control Engineering2164-25832025-12-0113110.1080/21642583.2025.2546839Dynamic order selection analysis in adaptive polynomial Kalman filtering: implementation and integration of sensor data and hybrid image processing for bio-inspired needle systemsDileep Sivaraman0Branesh M. Pillai1Cholatip Wiratkapun2Jackrit Suthakorn3Songpol Ongwattanakul4Department of Biomedical Engineering, Center for Biomedical and Robotics Technology (BART LAB), Faculty of Engineering, Mahidol University, Nakhon Pathom, ThailandDepartment of Industrial Systems Engineering (ISE), School of Engineering and Technology (SET), Asian Institute of Technology (AIT), Pathum Thani, ThailandDepartment of Radiology, Breast Diagnostic Center, Division of Diagnostic Radiology, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, ThailandDepartment of Biomedical Engineering, Center for Biomedical and Robotics Technology (BART LAB), Faculty of Engineering, Mahidol University, Nakhon Pathom, ThailandDepartment of Biomedical Engineering, Center for Biomedical and Robotics Technology (BART LAB), Faculty of Engineering, Mahidol University, Nakhon Pathom, ThailandThis study investigates dynamic-order selection in Adaptive Polynomial Kalman Filtering (APKF) for tracking the bioinspired dual-sheath needle systems used in biopsy procedures. Emphasizing integration of sensor data and hybrid image processing, the goal is to achieve precise motion estimation, which is critical to medical robotics. A hybrid image tracking system combined with APKF was implemented for real-time needle tip tracking and validated using a linear rail setup. Initial simulations showed that the standard APKF significantly outperformed traditional Kalman Filtering (KF), achieving an average reduction of 46.9% in Root Mean Square Error (RMSE), 57.8% in Mean Absolute Error (MAE), and 64.5% in Median Absolute Deviation (MAD). To further improve the performance, model-order selection criteria–Mean Squared Error (MSE), Akaike Information Criterion (AIC), Corrected AIC (AICc), and Bayesian Information Criterion (BIC)–were applied within the APKF framework. This led to even greater reductions in RMSE (55.4%), MAE (61.2%), and MAD (65.9%) compared with KF. The results highlight the effectiveness of combining model-order selection with adaptive filtering to enhance real-time estimation. The proposed tracking system demonstrates improved accuracy and control, reinforcing the potential of bioinspired needle systems in robot-assisted biopsy procedures.https://www.tandfonline.com/doi/10.1080/21642583.2025.2546839Adaptive polynomial Kalman filterbioinspired needledual-sheath mechanismrobotic biopsy |
| spellingShingle | Dileep Sivaraman Branesh M. Pillai Cholatip Wiratkapun Jackrit Suthakorn Songpol Ongwattanakul Dynamic order selection analysis in adaptive polynomial Kalman filtering: implementation and integration of sensor data and hybrid image processing for bio-inspired needle systems Systems Science & Control Engineering Adaptive polynomial Kalman filter bioinspired needle dual-sheath mechanism robotic biopsy |
| title | Dynamic order selection analysis in adaptive polynomial Kalman filtering: implementation and integration of sensor data and hybrid image processing for bio-inspired needle systems |
| title_full | Dynamic order selection analysis in adaptive polynomial Kalman filtering: implementation and integration of sensor data and hybrid image processing for bio-inspired needle systems |
| title_fullStr | Dynamic order selection analysis in adaptive polynomial Kalman filtering: implementation and integration of sensor data and hybrid image processing for bio-inspired needle systems |
| title_full_unstemmed | Dynamic order selection analysis in adaptive polynomial Kalman filtering: implementation and integration of sensor data and hybrid image processing for bio-inspired needle systems |
| title_short | Dynamic order selection analysis in adaptive polynomial Kalman filtering: implementation and integration of sensor data and hybrid image processing for bio-inspired needle systems |
| title_sort | dynamic order selection analysis in adaptive polynomial kalman filtering implementation and integration of sensor data and hybrid image processing for bio inspired needle systems |
| topic | Adaptive polynomial Kalman filter bioinspired needle dual-sheath mechanism robotic biopsy |
| url | https://www.tandfonline.com/doi/10.1080/21642583.2025.2546839 |
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