The role of hand motion connectivity in the performance of laparoscopic procedures on a virtual reality simulator.

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  • Author(s): Loukas C;Loukas C; Rouseas C; Georgiou E
  • Source:
    Medical & biological engineering & computing [Med Biol Eng Comput] 2013 Aug; Vol. 51 (8), pp. 911-22. Date of Electronic Publication: 2013 Mar 30.
  • Publication Type:
    Journal Article
  • Language:
    English
  • Additional Information
    • Source:
      Publisher: Springer Country of Publication: United States NLM ID: 7704869 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1741-0444 (Electronic) Linking ISSN: 01400118 NLM ISO Abbreviation: Med Biol Eng Comput Subsets: MEDLINE
    • Publication Information:
      Publication: New York, NY : Springer
      Original Publication: Stevenage, Eng., Peregrinus.
    • Subject Terms:
      User-Computer Interface*; Computer-Assisted Instruction/*methods ; Hand/*physiology ; Laparoscopy/*education ; Movement/*physiology; Humans ; Laparoscopy/methods ; Pattern Recognition, Automated ; Surgery, Computer-Assisted
    • Abstract:
      Assessment of surgical skills based on virtual reality (VR) technology has received major attention in recent years, with special focus placed on experience discrimination via hand motion analysis. Although successful, this approach is restricted from extracting additional important information about the trainee's hand kinematics. In this study, we investigate the role of hand motion connectivity in the performance of a laparoscopic cholecystectomy on a VR simulator. Two groups were considered: experienced residents and beginners. The connectivity pattern of each subject was evaluated by analyzing their hand motion signals with multivariate autoregressive (MAR) models. Our analysis included the entire as well as key phases of the operation. The results revealed that experienced residents outperformed beginners in terms of the number, magnitude and covariation of the MAR weights. The magnitude of the coherence spectra between different combinations of hand signals was in favor of the experienced group. Yet, the more challenging (in terms of hand movement activity) an operational phase was, the more connections were generated, with experienced subjects performing more coordinated gestures per phase. The proposed approach provides a suitable basis for hand motion analysis of surgical trainees and could be utilized in future VR simulators for skill assessment.
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    • Publication Date:
      Date Created: 20130402 Date Completed: 20140114 Latest Revision: 20211021
    • Publication Date:
      20231215
    • Accession Number:
      10.1007/s11517-013-1063-4
    • Accession Number:
      23543278