2020/04/27 by Zonghe Chua, Chua, Zonghe, Anthony Jarc +7 · 1 citation
Engineering · Medicine · #Anatomy and Medical Technology #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Robotics (cs.RO) #Soft Robotics and Applications #Surgical Simulation and Training
paper · pdf · doi:10.48550/arxiv.2004.13226
openalex publication_date 2020/04/27 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28
The lack of haptic feedback in Robot-assisted Minimally Invasive Surgery\n(RMIS) is a potential barrier to safe tissue handling during surgery. Bayesian\nmodeling theory suggests that surgeons with experience in open or laparoscopic\nsurgery can develop priors of tissue stiffness that translate to better force\nestimation abilities during RMIS compared to surgeons with no experience. To\ntest if prior haptic experience leads to improved force estimation ability in\nteleoperation, 33 participants were assigned to one of three training\nconditions: manual manipulation, teleoperation with force feedback, or\nteleoperation without force feedback, and learned to tension a silicone sample\nto a set of force values. They were then asked to perform the tension task, and\na previously unencountered palpation task, to a different set of force values\nunder teleoperation without force feedback. Compared to the teleoperation\ngroups, the manual group had higher force error in the tension task outside the\nrange of forces they had trained on, but showed better speed-accuracy functions\nin the palpation task at low force levels. This suggests that the dynamics of\nthe training modality affect force estimation ability during teleoperation,\nwith the prior haptic experience accessible if formed under the same dynamics\nas the task.\n