Automatic identification of otological drilling faults: an intelligent recognition algorithm

Background This article presents an intelligent recognition algorithm that can recognize milling states of the otological drill by fusing multi‐sensor information. Methods An otological drill was modified by the addition of sensors. The algorithm was designed according to features of the milling pro...

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Veröffentlicht in:The international journal of medical robotics + computer assisted surgery Jg. 6; H. 2; S. 231 - 238
Hauptverfasser: Cao, Tianyang, Li, Xisheng, Gao, Zhiqiang, Feng, Guodong, Shen, Peng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Chichester, UK John Wiley & Sons, Ltd 01.06.2010
Schlagworte:
adaptive filter
Adaptive filters
Algorithms
Animals
characteristic curve
Drilling
Faults
Filtering
Intelligence
Interference
multi-sensor information fusion
otological drill
Recognition
Scapula - surgery
Software
Surgery
Swine
ISSN:1478-5951, 1478-596X, 1478-596X
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Zusammenfassung:Background This article presents an intelligent recognition algorithm that can recognize milling states of the otological drill by fusing multi‐sensor information. Methods An otological drill was modified by the addition of sensors. The algorithm was designed according to features of the milling process and is composed of a characteristic curve, an adaptive filter and a rule base. The characteristic curve can weaken the impact of the unstable normal milling process and reserve the features of drilling faults. The adaptive filter is capable of suppressing interference in the characteristic curve by fusing multi‐sensor information. The rule base can identify drilling faults through the filtering result data. Results The experiments were repeated on fresh porcine scapulas, including normal milling and two drilling faults. The algorithm has high rates of identification. Conclusions This study shows that the intelligent recognition algorithm can identify drilling faults under interference conditions. Copyright © 2010 John Wiley & Sons, Ltd.
Bibliographie:istex:DE568DB4E1D0D004A475483762827F5DBC68615F
ark:/67375/WNG-VM6NFLFX-B
Beijing Municipal Natural Science Foundation - No. 4092027
ArticleID:RCS312
ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ObjectType-Article-1
ObjectType-Feature-2
ISSN:1478-5951
1478-596X
1478-596X
DOI:10.1002/rcs.312