Learning Kinematic Mappings in Laparoscopic Surgery

被引:2
|
作者
Huang, Felix C. [1 ,5 ]
Pugh, Carla M. [3 ]
Patton, James L. [2 ,5 ]
Mussa-Ivaldi, Ferdinando A. [4 ,5 ]
机构
[1] Northwestern Univ, Dept Biomed Engn, 345 E Super St,Room 1308, Chicago, IL 60611 USA
[2] Univ Illinois, Chicago, IL 60680 USA
[3] Northwestern Univ, Evanston, IL 60208 USA
[4] Northwestern Univ, Phys Med & Rehabil Mech & Biomed Engn, Evanston, IL 60208 USA
[5] Rehabil Inst Chicago, Sensory Motor Performance Program, Chicago, IL 60611 USA
关键词
MINIMALLY INVASIVE SURGERY; FEEDBACK; REALITY; TOOL; SKILLS; MODEL;
D O I
10.1109/IEMBS.2010.5626188
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
We devised an interactive environment in which subjects could perform simulated laparoscopic maneuvers, using either unconstrained movements or standard mechanical contact typical of a box-trainer. During training the virtual tool responded to the absolute position in space (Position-Based) or the orientation (Orientation-Based) of a hand-held sensor. Volunteers were further assigned to different sequences of target distances (Near-Far-Near or Far-Near-Far). Orientation-Based control produced much lower error and task times during training, which suggests that the motor system more easily accommodates tool use with degrees of freedom that match joint angles. When evaluated in constrained (physical box-trainer) conditions, each group exhibited improved performance from training. However, Position-Based training enabled greater reductions in movement error relative to Orientation-Based (mean -13.7%, CI:-27.1, -0.4). Furthermore, the Near-Far-Near schedule allowed a greater decrease in task time relative to the Far-Near-Far sequence (mean -13.5%, CI:-19.5, -7.5). Training at shallow insertion in virtual laparoscopy might promote more efficient movement strategies by emphasizing the curvature of tool motion. In addition, our findings suggest that an understanding of absolute tool position is critical to coping with mechanical interactions between the tool and trochar.
引用
收藏
页码:2097 / 2102
页数:6
相关论文
共 50 条
  • [1] Learning Kinematic Constraints in Laparoscopic Surgery
    Huang, Felix C.
    Mussa-Ivaldi, Ferdinando A.
    Pugh, Carla M.
    Patton, James L.
    IEEE TRANSACTIONS ON HAPTICS, 2012, 5 (04) : 356 - 364
  • [2] Learning global properties of nonredundant kinematic mappings
    DeMers, D
    Kreutz-Delgado, K
    INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH, 1998, 17 (05): : 547 - 560
  • [3] Kinematic mappings of plane affinities
    Discrete Math, 1-3 (121):
  • [4] Kinematic mappings of plane affinities
    Hotje, H
    DISCRETE MATHEMATICS, 1996, 155 (1-3) : 121 - 125
  • [5] Learning Curve in Laparoscopic Rectum Surgery
    Boettger, T. C.
    Mohsenl, D.
    Beardi, J.
    Rodehorst, A.
    ZENTRALBLATT FUR CHIRURGIE, 2011, 136 (03): : 273 - 281
  • [6] Development and Kinematic Analysis of a Redundant, Modular and Backdrivable Laparoscopic Surgery Robot
    Alassi, Alaa
    Yilmaz, Nural
    Bazman, Merve
    Gur, Berke
    Tumerdem, Ugur
    2018 IEEE/ASME INTERNATIONAL CONFERENCE ON ADVANCED INTELLIGENT MECHATRONICS (AIM), 2018, : 213 - 219
  • [7] GYNECOLOGICAL LAPAROSCOPIC SURGERY: LEARNING CURVE
    Khanum, Zohra
    Khanum, Amna
    Aman-ur-Rehman
    ANNALS OF KING EDWARD MEDICAL UNIVERSITY LAHORE PAKISTAN, 2015, 21 (04): : 253 - 256
  • [8] Learning curve in human laparoscopic surgery
    Kumar U.
    Gill I.S.
    Current Urology Reports, 2006, 7 (2) : 120 - 124
  • [9] Training and learning curve in laparoscopic surgery
    Boyne, HA
    Gonzalez, BA
    Costa, C
    Rege, E
    Acerbi, ME
    Carosi, C
    6TH WORLD CONGRESS OF ENDOSCOPIC SURGERY, PTS 1 AND 2, 1998, : A1235 - A1238
  • [10] Construct of Learning Model for Laparoscopic Surgery
    Yamashiro, Kazuaki
    Goto, Akihiko
    Shiomi, Hisanori
    Murakami, Koichiro
    DIGITAL HUMAN MODELING: APPLICATIONS IN HEALTH, SAFETY, ERGONOMICS, AND RISK MANAGEMENT, 2018, 10917 : 558 - 566