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Classification, detection and prediction of adverse and anomalous events in medical robots

Cao, Feng
http://rave.ohiolink.edu/etdc/view?acc_num=case1339166738

Abstract Details

2012, Master of Sciences, Case Western Reserve University, EECS - Computer and Information Sciences.
In this project we propose a framework to model the behavior and evaluate the reliability and safety of robotic surgery systems. A software simulator and associated user interface tools are used to generate the simulated hardware/software data of a robotic system performing interventions on small animals. The main contributions of this work are the usage of Dynamic Bayesian Networks (DBN) to model both software and hardware dynamics of the robotic surgery system, as well as detecting adverse and anomalous (A&A) events in the system. We show empirically that the model can accurately capture aspects of the software/hardware dynamics. Furthermore, we show that the models are able to accurately classify, detect and predict certain kinds of A&A events. Finally, comparison between different models demonstrates the usefulness of modeling both hardware and software state, as opposed to using only hardware state alone.
Soumya Ray, PhD (Committee Chair)
Andy Podgurski, PhD (Committee Member)
M. Cenk Çavuşoğlu, PhD (Committee Member)
Gultekin Ozsoyoglu, PhD (Committee Member)
59 p.
Computer Science
Robotic surgery system; Adverse and anomalous event detection; Dynamic Bayesian network

Recommended Citations

Citations

  • Cao, F. (2012). Classification, detection and prediction of adverse and anomalous events in medical robots [Master's thesis, Case Western Reserve University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=case1339166738

    APA Style (7th edition)

  • Cao, Feng. Classification, detection and prediction of adverse and anomalous events in medical robots. 2012. Case Western Reserve University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=case1339166738.

    MLA Style (8th edition)

  • Cao, Feng. "Classification, detection and prediction of adverse and anomalous events in medical robots." Master's thesis, Case Western Reserve University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=case1339166738

    Chicago Manual of Style (17th edition)

case1339166738
559
© 2012, all rights reserved.
This open access ETD is published by Case Western Reserve University School of Graduate Studies and OhioLINK.