Automated drill-stop by SVM classified audible signals

B. M. Pohl; J. O. Jungmann; O. Christ; U. G. Hofmann

doi:10.1109/EMBC.2012.6346091

Automated drill-stop by SVM classified audible signals

B. M. Pohl, J. O. Jungmann, O. Christ, U. G. Hofmann

Institute of Signal Processing

Abstract

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.

Original language	English
Title of host publication	2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Number of pages	4
Publisher	IEEE
Publication date	01.08.2012
Pages	956-959
Article number	6346091
ISBN (Print)	978-1-4244-4119-8
ISBN (Electronic)	978-1-4577-1787-1
DOIs	https://doi.org/10.1109/EMBC.2012.6346091
Publication status	Published - 01.08.2012
Event	34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - San Diego, United States Duration: 28.08.2012 → 01.09.2012 Conference number: 94236

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Pohl, B. M., Jungmann, J. O., Christ, O., & Hofmann, U. G. (2012). Automated drill-stop by SVM classified audible signals. In 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 956-959). Article 6346091 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society). IEEE. https://doi.org/10.1109/EMBC.2012.6346091

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abstract = "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.",

author = "Pohl, {B. M.} and Jungmann, {J. O.} and O. Christ and Hofmann, {U. G.}",

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Pohl, BM, Jungmann, JO, Christ, O & Hofmann, UG 2012, Automated drill-stop by SVM classified audible signals. in 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society., 6346091, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, pp. 956-959, 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, San Diego, California, United States, 28.08.12. https://doi.org/10.1109/EMBC.2012.6346091

Automated drill-stop by SVM classified audible signals. / Pohl, B. M.; Jungmann, J. O.; Christ, O. et al.
2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2012. p. 956-959 6346091 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society).

Research output: Chapters in Books/Reports/Conference Proceedings › Conference contribution › peer-review

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AB - 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.

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Pohl BM, Jungmann JO, Christ O, Hofmann UG. Automated drill-stop by SVM classified audible signals. In 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE. 2012. p. 956-959. 6346091. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society). doi: 10.1109/EMBC.2012.6346091

Automated drill-stop by SVM classified audible signals

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