Intraoperative control for robotic spinal surgical system with audio and torque sensing

In pedicle screw insertion surgeries, the most dangerous part is the screw path drilling process. In current surgeries, surgeons guarantee not to drill through the vertebra by their haptic and auditory sense and experience. In this paper, an intraoperative real-time control method for a Robotic Spinal Surgical System (RSSS) with state sensing is proposed. A drilling state recognition with Audio-Torque fusion is developed. The short-time average drilling torque and its amplitude are used to construct a reference torque, and classify the drilling states. Aim to audio signals, Support Vector Machine (SVM) is used to classify the patterns, and Mel-frequency cepstral coefficients (MFCC) is extracted to train the mode and predict testing samples. By setting a different priority level for each sensor, the fusion information is for precise intraoperative control in the screw path drilling.

[1]  Wan Kyun Chung,et al.  Human-guided surgical robot system for spinal fusion surgery: CoRASS , 2008, 2008 IEEE International Conference on Robotics and Automation.

[2]  Jianxun Zhang,et al.  Positioning Accuracy of a Medical Robotic System for Spine Surgery , 2009, 2009 2nd International Conference on Biomedical Engineering and Informatics.

[3]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[4]  Han Meng,et al.  Parameter selection in SVM with RBF kernel function , 2012, World Automation Congress 2012.

[5]  Wan Kyun Chung,et al.  Automated Segmentation of the Lumbar Pedicle in CT Images for Spinal Fusion Surgery , 2011, IEEE Transactions on Biomedical Engineering.

[6]  J. Novakovic,et al.  C-Support Vector Classification: Selection of kernel and parameters in medical diagnosis , 2011, 2011 IEEE 9th International Symposium on Intelligent Systems and Informatics.

[7]  Shih-Tseng Lee,et al.  Force control and breakthrough detection of a bone-drilling system , 2004 .

[8]  M. Shoham,et al.  Bone-Mounted Miniature Robotic System for Spine Surgery , 2006, The First IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics, 2006. BioRob 2006..

[9]  Wan Kyun Chung,et al.  Automated surgical planning and evaluation algorithm for spinal fusion surgery with three-dimensional pedicle model , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[10]  L. Gibson.  The mechanical behaviour of cancellous bone. , 1985, Journal of biomechanics.

[11]  Ming-Syan Chen,et al.  On the Design and Analysis of the Privacy-Preserving SVM Classifier , 2011, IEEE Transactions on Knowledge and Data Engineering.

[12]  Prasad D Polur,et al.  Effect of high-frequency spectral components in computer recognition of dysarthric speech based on a Mel-cepstral stochastic model. , 2005, Journal of rehabilitation research and development.

[13]  Bijan Shirinzadeh,et al.  Physics-Based Haptic Simulation of Bone Machining , 2011, IEEE Transactions on Haptics.

[14]  Jianwei Zhang,et al.  Safety Design and Control Algorithm for Robotic Spinal Surgical System , 2011, 2011 First International Conference on Robot, Vision and Signal Processing.

[15]  Leo Joskowicz,et al.  Bone-mounted miniature robot for surgical procedures: Concept and clinical applications , 2003, IEEE Trans. Robotics Autom..

[16]  Byung-Ju Yi,et al.  A robot-assisted surgery system for spinal fusion , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[17]  Goutam Saha,et al.  On the Use of Distributed DCT in Speaker Identification , 2009, 2009 Annual IEEE India Conference.

[18]  Goutam Saha,et al.  Design, analysis and experimental evaluation of block based transformation in MFCC computation for speaker recognition , 2012, Speech Commun..

[19]  Samuel Kim,et al.  A pitch synchronous feature extraction method for speaker recognition , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[20]  Chu-Hsing Lin,et al.  Anomaly Detection Using LibSVM Training Tools , 2008, 2008 International Conference on Information Security and Assurance (isa 2008).

[21]  Mark A Gregory,et al.  A novel approach for MFCC feature extraction , 2010, 2010 4th International Conference on Signal Processing and Communication Systems.