2026-09-24T15:02:16Zhttps://ubir.buffalo.edu/oai/request

oai:ubir.buffalo.edu:10477/786372025-09-18T04:54:55Zcom_10477_77914col_10477_78397

Classification of Motor Control Features for Surgical Skills Assessment during Robotic Assisted Surgery Gorantla, Kuber Reddy; 0000-0002-8380-0075 Esfahani, Ehsan Mechanical and Aerospace Engineering mechanical engineering M.S. With the advent of new technologies in Surgery, Robotic Assisted Surgery (RAS) has been the point of research. Surgical Skill Assessment in RAS has increased interest through which the training and feedback to surgeons can be given based on the task performance. In this research, we use the experimental data of Urethro-vesicle anastomosis (UVA) on an inanimate model done by two groups of participants, namely experts(E), Novice(N)using the da Vinci® Surgical System (Sunnyvale, CA). Motor Control features, which are a part of psychomotor learning, are developed based on the camera plane coordinates by performing the feature engineering and the classification is being done using the pattern recognition principles. Our classification method when compared to the manual encoding of subject expertise based on the Dreyfus model resulted in an accuracy >98% for the intra-subject classification and >76% average accuracy for Inter-Subject Classification. Additionally, this study could form a basis for closed loop surgical training, specifically for the novitiate surgeons. 2018-10-26T02:57:14Z 2018-10-26T02:57:14Z 2018 2018-08-10 15:54:11 Text Thesis http://hdl.handle.net/10477/78637 eng Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions. Copyright retained by author. application/pdf State University of New York at Buffalo