State recognition of bone drilling with audio signal in Robotic Orthopedics Surgery System

Bone drilling is an important and difficult process in orthopedic surgeries. To detect the drilling state of a Robotic Orthopedic Surgery System (ROSS) in real-time, a state recognition method based on audio signals, the Acoustic Emission (AE) signals generated in drilling process, is proposed in this paper. By an analysis via power spectral density of the AE signals, an appropriate frequency band is selected for state recognition. The Exponential Mean Amplitude (EMA) and the Hurst Exponent (HE) are used to illustrate the energy characteristics and stability of the AE signals in the chosen frequency band, respectively. The recognition algorithm combines the two different features is performed on a embedded device in the experiments. Finally, the experiments are carried out to demonstrate the effectiveness of the proposed drilling state recognition method.

[1]  K. Ikuta,et al.  “Surgery recorder system” for recording position and force of forceps during laparoscopic surgery , 2007, 2007 IEEE/ASME international conference on advanced intelligent mechatronics.

[2]  H. Blok.  On the nature of the stock market: Simulations and experiments , 2000, cond-mat/0010211.

[3]  Wen-Yo Lee,et al.  Force control and breakthrough detection of a bone-drilling system , 2003, IEEE/ASME Transactions on Mechatronics.

[4]  U. G. Hofmann,et al.  Towards an automated, minimal invasive, precision craniotomy on small animals , 2011, 2011 5th International IEEE/EMBS Conference on Neural Engineering.

[5]  A-lan Jiang,et al.  Experimental study of acoustic emission characteristics of underwater concrete structures , 2008, 2008 Symposium on Piezoelectricity, Acoustic Waves, and Device Applications.

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

[7]  Tianmiao Wang,et al.  3D navigation and monitoring for spinal milling operation based on registration between multiplanar fluoroscopy and CT images , 2012, Comput. Methods Programs Biomed..

[8]  John Kenneth Salisbury,et al.  Visuohaptic simulation of bone surgery for training and evaluation , 2006, IEEE Computer Graphics and Applications.

[9]  P. Croucher,et al.  Assessment of cancellous bone structure: Comparison of strut analysis, trabecular bone pattern factor, and marrow space star volume , 1996, Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research.

[10]  Th. Beth,et al.  ANALYSIS OF DRILL SOUND IN SPINE SURGERY , 2004 .

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

[12]  Liming Cheng,et al.  Pedicle screw fixation for traumatic fractures of the thoracic and lumbar spine. , 2013, The Cochrane database of systematic reviews.

[13]  Ulrich G. Hofmann,et al.  Automated drill-stop by SVM classified audible signals , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[14]  P Zioupos,et al.  Mechanical properties and the hierarchical structure of bone. , 1998, Medical engineering & physics.

[15]  Zhiguo Wang,et al.  Estimating Hurst exponent with wavelet packet , 2006, 2006 7th International Conference on Computer-Aided Industrial Design and Conceptual Design.

[16]  R. Singleton,et al.  Spectral analysis of the call of the male killer whale , 1967, IEEE Transactions on Audio and Electroacoustics.

[17]  Xin Duan,et al.  Intramedullary nailing for tibial shaft fractures in adults. , 2012, The Cochrane database of systematic reviews.