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Using Two-Third Power Law for Segmentation of Hand Movement in Robotic Assisted Surgery

[+] Author Affiliations
Somayeh B. Shafiei, Ehsan T. Esfahani

University at Buffalo, State University of New York, Buffalo, NY

Khurshid A. Guru

Roswell Park Cancer Institute, Buffalo, NY

Paper No. DETC2015-47813, pp. V05CT08A014; 6 pages
  • ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 5C: 39th Mechanisms and Robotics Conference
  • Boston, Massachusetts, USA, August 2–5, 2015
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-5714-4
  • Copyright © 2015 by ASME


In this study, we have developed a robust and accurate algorithm based on concept of two-third power law in human motor control to segment the hand trajectory of robotic surgeons into smaller segments. We hypothesis that tracking a longer trajectory is subjected to higher cognitive workload that may lead in to an imperfect CNS performance in programming muscle activation which will lead to more number of segment trajectories and pause points in hand movements.

To test our hypothesis, after segmenting the trajectory, we determine the correlation between affine velocity and workload extracted from Surgeon’s Electroencephalography (EEG) features. EEG features are extracted by using brain waves recorded by wireless brain computer interface (B-Alert X-10 system). In our experimental study, 2 groups of participants three “experts” and five “Competent and Proficient” performed Urethro-vesical Anastomosis on an inanimate model, using the da-Vinci Surgical System® (Sunnyvale, CA).

Copyright © 2015 by ASME



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