Send to

Choose Destination
See comment in PubMed Commons below
Conf Proc IEEE Eng Med Biol Soc. 2012;2012:956-9. doi: 10.1109/EMBC.2012.6346091.

Automated drill-stop by SVM classified audible signals.

Author information

Institute for Signal Processing, University of Luebeck, 23562 Luebeck, Germany.


Neuroscience research often requires direct access to brain tissue in animal models which clearly requires opening of the protective cranium. Minimizing animal numbers requests only well-experienced surgeons, since clumsy performance may lead to premature death of the animal. To minimise those traumatic outcomes, an algorithmic approach for closed-loop control of our Spherical Assistant for Stereotaxic Surgery (SASSU) was designed. Controlling the surgical robot's micro-drill unit by audio pattern recognition proved to be a simple and reliable way to automatically stop the automated drill feed. Sound analysis based on the anatomical morphology of a rat skull was used to train a Support Vector Machine (SVM) classification of the time-frequency representations of the drill sound. Fully automated high throughput animal surgeries are the goal of this approach.

[Indexed for MEDLINE]
PubMed Commons home

PubMed Commons

How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for IEEE Engineering in Medicine and Biology Society
    Loading ...
    Support Center