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    A Fuzzy Reasoning System for Computer-Guided Laparoscopy Training

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    A_Fuzzy_Reasoning_System_for_C ...
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    Description:
    Final Accepted Manuscript
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    Author
    Hong, Minsik
    Meisner, Kai
    Lee, SeungHyun
    Schreiber, Andre M.
    Rozenblit, Jerzy W.
    Affiliation
    University of Arizona, Department of Electrical and Computer Engineering
    Issue Date
    2020-10-11
    Keywords
    fuzzy reasoning
    guidance system
    surgical training
    
    Metadata
    Show full item record
    Publisher
    IEEE
    Citation
    M. Hong, K. Meisner, S. Lee, A. M. Schreiber and J. W. Rozenblit, "A Fuzzy Reasoning System for Computer-Guided Laparoscopy Training," 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Toronto, ON, 2020, pp. 1712-1717, doi: 10.1109/SMC42975.2020.9283423.
    Journal
    IEEE Transactions on Systems, Man, and Cybernetics: Systems
    Rights
    © 2020 IEEE.
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    Abstract
    A system called the Computer-Assisted Surgical Trainer (CAST) that provides force, visual, and audio guidance with objective assessments for laparoscopic surgery skills training in a non-patient training setup was developed. To support the guidance and assessment of trainees, given an object transfer task, a fuzzy reasoning system is proposed to estimate states of objects as well as the completion of basic actions. A peg transfer task is implemented as a target application. Based on a modeling approach of this peg transfer task, fuzzy production rules are defined and a reasoning process is designed. The experimental test results show the effectiveness of the proposed fuzzy reasoning system. © 2020 IEEE.
    ISSN
    2168-2216
    DOI
    10.1109/smc42975.2020.9283423
    Version
    Final accepted manuscript
    Sponsors
    National Science Foundation
    ae974a485f413a2113503eed53cd6c53
    10.1109/smc42975.2020.9283423
    Scopus Count
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    UA Faculty Publications

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