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Adaptive least squares support vector machines filter for hand tremor canceling in microsurgery

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Abstract

One of the main problems for effective control of a minimally invasive surgery (MIS) is the imprecision that caused by hand tremor. In this paper, a novel adaptive filter, the least squares support vector machines adaptive filter (LS-SVMAF), is proposed to overcome this problem. Compared with traditional methods like multi layer perceptron (MLP), LS-SVM shows a superior performance of nonlinear modeling with small scale of data set or high dimensional input space. With the LS-SVMAF, we can model and predict the hand tremor more effectively and improve the precision and reliability in the master–slave robotic system for microsurgery. Simulation results demonstrate the effectiveness of the proposed filter and its superior performance over its competing rivals.

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References

  1. Taylor RH, Stoianovici D (2003) Medical robotics in computer-integrated surgery. IEEE Trans Robotics Automat 19:765–781

    Google Scholar 

  2. Mitsuishi M (2007) Medical robot and master slave system for minimally invasive surgery. In: International conference on complex medical engineering. Beijing, pp 8–13

  3. Wang S, Yue L, Li Q et al (2008) Conceptual design and dimensional synthesis of ‘Micro Hand’. Mech Mach Theory 143:1186–1197

    Google Scholar 

  4. Veluvolu KC, Tan UX et al (2009) Adaptive filtering of physiological tremor for real-time compensation. In: IEEE international conference on robotics and biomimetics. Bangkok, Thailand, pp 524–529

  5. Latt WT, Tan UX et al (2008) Physiological tremor sensing using only accelerometers for real-time compensation. In: IEEE International Conference on Robotics and Biomimetics. Bangkok, pp 474–479

  6. Riviere CN, Reich SG, Thakor NV (1997) Adaptive fourier modeling for quantifican of tremor. J Neurosci Methods 74:77–87

    Google Scholar 

  7. Riviere CN, Rader RS, Thakor NV (1998) Adaptive canceling of physiological tremor for improved precision in microsurgery. IEEE Trans Biomed Eng 45:839–846

    Google Scholar 

  8. Ang WT, Pradeep PK, Riviere CN (2004) Active tremor compensation in microsurgery. In: Proceeding of the 26th annual international conference of the IEEE EMBS. San Francisco, pp 2738–2741

  9. Riley PO, Eosen MJ (1987) “Evaluating manual control devices for those with tremor disability”. J Rehab Res Develop 24:99–110

    Google Scholar 

  10. Zhang J, Chu F (2005) Real-time modeling and prediction of physiological hand tremor. In: International conference on acoustics, speech and signal processing. Little Rock, pp V645–V648

  11. Riviere CN, Thakor NV (1996) Modeling and canceling tremor in human-machine interfaces. IEEE Eng Med Biol 15:29–36

    Google Scholar 

  12. Riviere CN, Ang WT, Khosla PK (2003) Toward active tremor cancelling in handheld microsurgical instruments. IEEE Trans Robotics Automat 19:793–800

    Google Scholar 

  13. Suykens JAK (2001) Nonlinear modeling and support vector machines. In: IEEE instrumentation and measurement technology conference, Budapest, pp 21–23

  14. Iplikci S (2010) Support vector machines based neuro-fuzzy control of nonlinear systems. Neurocomputing 73:2097–2107

    Article  Google Scholar 

  15. Chuang CC, Su SF, Jeng JT et al (2002) Robust support vector regression networks for function approximation with outliers. IEEE Trans Neural Netw 13:1322–1329

    Article  Google Scholar 

  16. Suykens JAK, Vandewalle J, Moor BD, (2001) Optimal control by least squares support vector machines. Neural Netw 14:23–35

    Google Scholar 

  17. Suykens JAK, Vandewalle J (2000) Recurrent least squares support vector machines. IEEE Trans Circuits Syst I Fundam Theory Appl 47:1109–1114

    Article  Google Scholar 

  18. Zhang MG, Wang XG, Li WH (2005) Nonlinear system identification using least squares support vector machines. Int Conf Neural Netw Brain 1:414–418

    Article  Google Scholar 

  19. Lin CJ, Hong SJ, Lee CY (2005) Using least squares support vector machines for adaptive communication channel equalization. Appl Sci Eng 3:51–59

    Google Scholar 

  20. Jakubowski J, Kwiatos K, Chwaleba A et al (2002) Higher order statistics and neural network for tremor recognition. IEEE Trans Biomed Eng 49:152–159

    Google Scholar 

  21. Lin CT, Juang CF (1997) An adaptive neural fuzzy filter and its applications. IEEE Trans Syst Man Cybern Part B Cybern 47:635–656

    Google Scholar 

  22. Du QJ, Zhang XY (2007) Kinematics analysis and motion control of radio frequency ablation surgical robot. In: International conference on control and automation. Guangzhou, pp 1890–1895

  23. Nello C, John Shawe T (2004) An introduction to support vector machines and other kernel-learning methods. Publishing House of Electronics Idustry, Beijing

  24. Fung G, Mangasarian OL (2001) Proximal support vector machine Classifiers. In: Provost F, Srikant R (eds.) Proceedings KDD-2001: knowledge discovery and data mining, pp 77–86

  25. Fung G, Mangasarian OL (2005) Multicategory proximal support vector machine Classifiers. Mach Learn 59:77–99

    Google Scholar 

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Acknowledgments

The authors would like to thank the editor and reviewers for their very insightful and constructive comments. This work was supported by the National Natural Science Foundation of China under Project U0735003 and 60974047, the Natural Science Foundation of Guangdong Province under Project 8351009001000002 and 9151009001000011, Science and Technology Plan Projects of Guangdong Province under Project 2009B010900051, FOK Ying Tung Education Foundation of China under Project 121061, and High-Level Professionals Project of Guangdong Province.

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Authors and Affiliations

  1. Department of Automation, Guangdong University of Technology, 510006, Guangzhou, People’s Republic of China

    Zhi Liu, Qihang Wu & Yun Zhang

  2. Faculty of Science and Technology, University of Macau, Av. Padre Tomás Pereira, S.J., Taipa, Macau, SAR, People’s Republic of China

    C. L. Philip Chen

  3. Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, TX, 78249-0669, USA

    C. L. Philip Chen

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  1. Zhi Liu
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  2. Qihang Wu
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  4. C. L. Philip Chen
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Correspondence to Zhi Liu.

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Liu, Z., Wu, Q., Zhang, Y. et al. Adaptive least squares support vector machines filter for hand tremor canceling in microsurgery. Int. J. Mach. Learn. & Cyber. 2, 37–47 (2011). https://doi.org/10.1007/s13042-011-0012-5

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  • DOI: https://doi.org/10.1007/s13042-011-0012-5

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