Paper Info
Title
State recognition of bone drilling based on acoustic emission in pedicle screw operation
Abstract
Pedicle drilling is an important step in pedicle screw fixation and the most significant challenge in this operation is how to determine a key point in the transition region between cancellous and inner cortical bone. The purpose of this paper is to find a method to achieve the recognition for the key point. After acquiring acoustic emission (AE) signals during the drilling process, this paper proposed a novel frequency distribution-based algorithm (FDB) to analyze the AE signals in the frequency domain after certain processes. Then we select a specific frequency domain of the signal for standard operations and choose a fitting function to fit the obtained sequence. Characters of the fitting function are extracted as outputs for identification of different bone layers. The results, which are obtained by detecting force signal and direct measurement, are given in the paper. Compared with the results above, the results obtained by AE signals are distinguishable for different bone layers and are more accurate and precise. The results of the algorithm are trained and identified by a neural network and the recognition rate reaches 84.2%. The proposed method is proved to be efficient and can be used for bone layer identification in pedicle screw fixation.
Year
DOI
Venue
2018
10.3390/s18051484
SENSORS
Keywords
Field
DocType
pedicle drilling,acoustic emission signal,frequency distribution,neural network
Frequency domain,Cortical bone,State recognition,Electronic engineering,Acoustics,Engineering,Fixation (histology),Artificial neural network,Drilling,Acoustic emission
Journal
Volume
Issue
Citations 
18
5.0
0
PageRank 
References 
Authors
0.34
2
6
Name
Order
Citations
PageRank
Guan Fengqing100.34
Sun Yu200.34
Qi Xiaozhi335.48
Hu Ying400.68
Gang Yu58810.71
Jianwei Zhang63517.45